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404

There's nothing here.
+ - + diff --git a/about.html b/about.html index 9bc50f373..fe5709bff 100644 --- a/about.html +++ b/about.html @@ -8,7 +8,7 @@ - + @@ -200,6 +200,6 @@ or granules: files containing the imagery for a small part of the Earth and a single observation date) makes it unnecessary complicated to analyse them. The solution to this is to store these data in the cloud, on compute back-ends, process them there, and browse the results or download resulting figures or numbers. But how do we do that?

openEO develops an open application programming interface (API) that connects clients like R, Python and JavaScript to big Earth observation cloud back-ends in a simple and unified way.

With such an API,

  • each client can work with every back-end, and
  • it becomes possible to compare back-ends in terms of capacity, cost, and results (validation, reproducibility)

# openEO?

The acronym openEO contracts two concepts:

  • open: used here in the context of open source software; open source software is available in source code form, and can be freely modified and redistributed; the openEO project will create open source software, reusable under a liberal open source license (Apache 2.0)
  • EO: Earth observation

Jointly, the openEO targets the processing and analysis of Earth observation data. The main objectives of the project are the following concepts:

  • Simplicity: nowadays, many end-users use Python or R to analyse data and JavaScript to develop web applications; analysing large amounts of EO imagery should be equally simple, and seamlessly integrate with existing workflows
  • Unification: current EO cloud back-ends all have a different API (opens new window), making EO data analysis hard to validate and reproduce and back-ends difficult to compare in terms of capability and costs, or to combine them in a joint analysis across back-ends. A unified API can resolve many of these problems.

The following pages introduce the core concepts of the project. Make sure to introduce yourself to the major technical terms used in the openEO project by reading the glossary.

# Why an API?

An API is an application programming interface. It defines a language that two computers (a client and a server) use to communicate.

The following figure shows how many interfaces are needed to be able to compare back-ends from different clients, without an openEO API:

Structure before openEO

With an openEO API (dark blue), the situation becomes much easier:

Structure with openEO

However, existing back-ends need to be taught to work with the new API, and clients that interact with back-ends need to be developed.

The task of the openEO project is to design, develop, and evaluate an API for cloud-based Earth Observation data processing.

# More information

Edit this page (opens new window)
Last Updated: 7/11/2024, 10:57:13 PM
- + diff --git a/assets/js/100.409b85b4.js b/assets/js/100.bdbced4d.js similarity index 95% rename from assets/js/100.409b85b4.js rename to assets/js/100.bdbced4d.js index 13338553e..3adbe33a0 100644 --- a/assets/js/100.409b85b4.js +++ b/assets/js/100.bdbced4d.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[100],{508:function(e,t,n){"use strict";n.r(t);var r=n(4),a=Object(r.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("Today, the newly established "),t("RouterLink",{attrs:{to:"/psc.html"}},[e._v("openEO Project Steering Committee")]),e._v("\nreleased a new version 1.0.1 of the openEO API. This doesn't include any new\nfunctionality, but clarifies ambiguous parts of the specification and changes\nthe API's recommendation regarding CORS headers to avoid potential security\nissues with credentials in web browsers. 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many openEO processes implemented in Python")])]),e._v(" "),r("p",[e._v("Our approach to tackle language-agnostic user-defined functions can be found in the following repositories:")]),e._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"https://github.com/Open-EO/openeo-udf",target:"_blank",rel:"noopener noreferrer"}},[e._v("UDF Python reference implementation"),r("OutboundLink")],1)]),e._v(" "),r("li",[r("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-udf",target:"_blank",rel:"noopener noreferrer"}},[e._v("UDF R reference implementation"),r("OutboundLink")],1)])]),e._v(" "),r("h2",{attrs:{id:"specification"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#specification"}},[e._v("#")]),e._v(" Specification")]),e._v(" "),r("p",[e._v("The API specification is available in the "),r("strong",[r("a",{attrs:{href:"https://github.com/Open-EO/openeo-api",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO API repository"),r("OutboundLink")],1)]),e._v(".\nThe process definitions are available in the "),r("strong",[r("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO processes repository"),r("OutboundLink")],1)]),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"ecosystem"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#ecosystem"}},[e._v("#")]),e._v(" Ecosystem")]),e._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"https://github.com/Open-EO/openeo-backend-validator",target:"_blank",rel:"noopener noreferrer"}},[e._v("Back-end validator"),r("OutboundLink")],1),e._v(" - Validation for back-end implementations. "),r("a",{attrs:{href:"https://github.com/Open-EO/openeo-result-validation-engine",target:"_blank",rel:"noopener noreferrer"}},[e._v("Image-based validation of EO processing results"),r("OutboundLink")],1),e._v(" is also worked on.")]),e._v(" "),r("li",[r("a",{attrs:{href:"https://hub.openeo.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Hub"),r("OutboundLink")],1),e._v(" ("),r("a",{attrs:{href:"https://github.com/Open-EO/openeo-hub",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),r("OutboundLink")],1),e._v(") - The central platform for openEO services.")]),e._v(" "),r("li",[r("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-docgen",target:"_blank",rel:"noopener noreferrer"}},[e._v("Processes DocGen"),r("OutboundLink")],1),e._v(" - A tool to generate a visual interface for openEO process definitions.")]),e._v(" "),r("li",[r("a",{attrs:{href:"https://github.com/Open-EO/openeo-vue-components",target:"_blank",rel:"noopener noreferrer"}},[e._v("Vue.js Components"),r("OutboundLink")],1),e._v(" - Common Vue.js 2 components for openEO")]),e._v(" "),r("li",[r("a",{attrs:{href:"https://github.com/Open-EO/openeo-js-commons",target:"_blank",rel:"noopener noreferrer"}},[e._v("JavaScript Commons"),r("OutboundLink")],1),e._v(" - Common JavaScript functionality for openEO clients and back-ends")])])])}),[],!1,null,null,null);r.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/13.157a6ad4.js b/assets/js/13.157a6ad4.js new file mode 100644 index 000000000..a1a72bc16 --- /dev/null +++ b/assets/js/13.157a6ad4.js @@ -0,0 +1 @@ +(window.webpackJsonp=window.webpackJsonp||[]).push([[13],{292:function(e,t,a){e.exports=a.p+"assets/img/dc_timeseries.c2c7a902.png"},293:function(e,t,a){e.exports=a.p+"assets/img/dc_flat.01dc91de.png"},294:function(e,t,a){e.exports=a.p+"assets/img/vector.201588fe.png"},295:function(e,t,a){e.exports=a.p+"assets/img/dc_filter.bdafa6d0.png"},296:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_unary.f69f840c.png"},297:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_kernel.4013a72c.png"},298:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_ts.4f9449f6.png"},299:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_dim_ts.c895aa81.png"},300:function(e,t,a){e.exports=a.p+"assets/img/dc_resample_time.987e797c.png"},301:function(e,t,a){e.exports=a.p+"assets/img/dc_resample_space.417f814b.png"},302:function(e,t,a){e.exports=a.p+"assets/img/dc_reduce.872f16c2.png"},303:function(e,t,a){e.exports=a.p+"assets/img/dc_aggregate_space.ae550945.png"},471:function(e,t,a){"use strict";a.r(t);var s=a(4),i=Object(s.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#datacubes"}},[e._v("#")]),e._v(" Datacubes")]),e._v(" "),t("h2",{attrs:{id:"what-are-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#what-are-datacubes"}},[e._v("#")]),e._v(" What are Datacubes?")]),e._v(" "),t("p",[e._v("Data is represented as datacubes in openEO, which are multi-dimensional arrays with additional information about their dimensionality. Datacubes can provide a nice and tidy interface for spatiotemporal data as well as for the operations you may want to execute on them. As they are arrays, it might be easiest to look at raster data as an example, even though datacubes can hold vector data as well. Our example data however consists of a 6x7 raster with 4 bands ["),t("code",[e._v("blue")]),e._v(", "),t("code",[e._v("green")]),e._v(", "),t("code",[e._v("red")]),e._v(", "),t("code",[e._v("near-infrared")]),e._v("] and 3 timesteps ["),t("code",[e._v("2020-10-01")]),e._v(", "),t("code",[e._v("2020-10-13")]),e._v(", "),t("code",[e._v("2020-10-25")]),e._v("], displayed here in an orderly, timeseries-like manner:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(292),alt:"Raster datacube timeseries: 12 imagery tiles are depicted, grouped by 3 dates along a timeline (time dimension). Each date has a blue, green, red and near-infrared band (bands dimension). Each single tile has the dimensions x and y (spatial dimensions)."}}),e._v(" "),t("figcaption",[e._v("An examplary raster datacube with 4 dimensions: x, y, bands and time.")])]),e._v(" "),t("p",[e._v("It is important to understand that datacubes are designed to make things easier for us, and are not literally a cube, meaning that the above plot is just as good a representation as any other. That is why we can switch the dimensions around and display them in whatever way we want, including the view below:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(293),alt:"Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates)."}}),e._v(" "),t("figcaption",[e._v("This is the 'raw' data collection that is our example datacube. The grayscale images are colored for understandability, and dimension labels are displayed.")])]),e._v(" "),t("p",[e._v("A vector datacube on the other hand could look like this:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(294),alt:"An examplary vector datacube with 3 dimensions: 2 geometries are given for the `geometries` dimension, along with 3 timesteps for the temporal dimension `time` and 4 bands in the `bands` dimension."}}),e._v(" "),t("figcaption",[e._v("An examplary vector datacube with 3 dimensions: 2 geometries are given for the "),t("code",[e._v("geometries")]),e._v(" dimension, along with 3 timesteps for the temporal dimension "),t("code",[e._v("time")]),e._v(" and 4 bands in the "),t("code",[e._v("bands")]),e._v(" dimension.")])]),e._v(" "),t("p",[t("a",{attrs:{href:"https://r-spatial.org/r/2022/09/12/vdc.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Vector datacubes"),t("OutboundLink")],1),e._v(" and raster datacubes are common cases of datacubes in the EO domain.\nA raster datacube has at least two spatial dimensions (usually named "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(") and a vector datacube has at least one geometry dimension (usually named "),t("code",[e._v("geometry")]),e._v(').\nThe purpose of these distinctions is simply to make it easier to describe "special" cases of datacubes, but you can also define other types such as a temporal datacube that has at least one temporal dimension (usually named '),t("code",[e._v("t")]),e._v(").")]),e._v(" "),t("p",[e._v("The following additional information are usually available for datacubes:")]),e._v(" "),t("ul",[t("li",[e._v("the dimensions (see "),t("a",{attrs:{href:"#dimensions"}},[e._v("below")]),e._v(")")]),e._v(" "),t("li",[e._v("a sampling method (see "),t("a",{attrs:{href:"#values-in-a-datacube"}},[e._v("below")]),e._v(")")]),e._v(" "),t("li",[e._v("a unit for the values")])]),e._v(" "),t("p",[e._v("All these information are usually provided through the datacube metadata.")]),e._v(" "),t("h2",{attrs:{id:"dimensions"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#dimensions"}},[e._v("#")]),e._v(" Dimensions")]),e._v(" "),t("p",[e._v("A dimension refers to a certain axis of a datacube. This includes all variables (e.g. bands), which are represented as dimensions. Our exemplary raster datacube has the spatial dimensions "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(", and the temporal dimension "),t("code",[e._v("t")]),e._v(". Furthermore, it has a "),t("code",[e._v("bands")]),e._v(" dimension, extending into the realm of "),t("em",[e._v("what kind of information")]),e._v(" is contained in the cube.")]),e._v(" "),t("p",[e._v("The following properties are usually available for dimensions:")]),e._v(" "),t("ul",[t("li",[e._v("name")]),e._v(" "),t("li",[e._v("type (potential types include: spatial (raster or vector data), temporal and other data such as bands)")]),e._v(" "),t("li",[e._v("axis (for spatial dimensions) / number")]),e._v(" "),t("li",[e._v("labels (usually exposed through textual or numerical representations, in the metadata as nominal values and/or extents)")]),e._v(" "),t("li",[e._v("reference system / projection")]),e._v(" "),t("li",[e._v("resolution / step size")]),e._v(" "),t("li",[e._v("unit for the labels (either explicitly specified or implicitly provided by the reference system)")]),e._v(" "),t("li",[e._v("additional information specific to the dimension type (e.g. the geometry types for a dimension containing geometries)")])]),e._v(" "),t("p",[e._v("All these information are usually provided through the datacube metadata.")]),e._v(" "),t("p",[e._v("Here is an overview of the dimensions contained in our example raster datacube above:")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("#")]),e._v(" "),t("th",[e._v("name")]),e._v(" "),t("th",[e._v("type")]),e._v(" "),t("th",[e._v("labels")]),e._v(" "),t("th",[e._v("resolution")]),e._v(" "),t("th",[e._v("reference system")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[e._v("1")]),e._v(" "),t("td",[t("code",[e._v("x")])]),e._v(" "),t("td",[e._v("spatial")]),e._v(" "),t("td",[t("code",[e._v("466380")]),e._v(", "),t("code",[e._v("466580")]),e._v(", "),t("code",[e._v("466780")]),e._v(", "),t("code",[e._v("466980")]),e._v(", "),t("code",[e._v("467180")]),e._v(", "),t("code",[e._v("467380")])]),e._v(" "),t("td",[e._v("200m")]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/32627",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:32627"),t("OutboundLink")],1)])]),e._v(" "),t("tr",[t("td",[e._v("2")]),e._v(" "),t("td",[t("code",[e._v("y")])]),e._v(" "),t("td",[e._v("spatial")]),e._v(" "),t("td",[t("code",[e._v("7167130")]),e._v(", "),t("code",[e._v("7166930")]),e._v(", "),t("code",[e._v("7166730")]),e._v(", "),t("code",[e._v("7166530")]),e._v(", "),t("code",[e._v("7166330")]),e._v(", "),t("code",[e._v("7166130")]),e._v(", "),t("code",[e._v("7165930")])]),e._v(" "),t("td",[e._v("200m")]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/32627",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:32627"),t("OutboundLink")],1)])]),e._v(" "),t("tr",[t("td",[e._v("3")]),e._v(" "),t("td",[t("code",[e._v("bands")])]),e._v(" "),t("td",[e._v("bands")]),e._v(" "),t("td",[t("code",[e._v("blue")]),e._v(", "),t("code",[e._v("green")]),e._v(", "),t("code",[e._v("red")]),e._v(", "),t("code",[e._v("nir")])]),e._v(" "),t("td",[e._v("4 bands")]),e._v(" "),t("td",[e._v("-")])]),e._v(" "),t("tr",[t("td",[e._v("4")]),e._v(" "),t("td",[t("code",[e._v("t")])]),e._v(" "),t("td",[e._v("temporal")]),e._v(" "),t("td",[t("code",[e._v("2020-10-01")]),e._v(", "),t("code",[e._v("2020-10-13")]),e._v(", "),t("code",[e._v("2020-10-25")])]),e._v(" "),t("td",[e._v("12 days")]),e._v(" "),t("td",[e._v("Gregorian calendar / UTC")])])])]),e._v(" "),t("p",[e._v('Dimension labels are usually either numerical or text (also known as "strings"), which also includes textual representations of timestamps or geometries for example.\nFor example, temporal labels are usually encoded as '),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/ISO_8601",target:"_blank",rel:"noopener noreferrer"}},[e._v("ISO 8601"),t("OutboundLink")],1),e._v(" compatible dates and/or times and similarly geometries can be encoded as "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry",target:"_blank",rel:"noopener noreferrer"}},[e._v("Well-known Text (WKT)"),t("OutboundLink")],1),e._v(" or be represented by their IDs.")]),e._v(" "),t("p",[e._v("Dimensions with a natural/inherent order (usually all temporal and spatial raster dimensions) are always sorted. Dimensions without inherent order (usually "),t("code",[e._v("bands")]),e._v("), retain the order in which they have been defined in metadata or processes (e.g. through "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_bands",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_bands")]),t("OutboundLink")],1),e._v("), with new labels simply being appended to the existing labels.")]),e._v(" "),t("p",[e._v("A geometry dimension is not included in the example raster datacube above and it is not used in the following examples, but to show how a vector dimension with two polygons could look like:")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("name")]),e._v(" "),t("th",[e._v("type")]),e._v(" "),t("th",[e._v("labels")]),e._v(" "),t("th",[e._v("reference system")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[t("code",[e._v("geometry")])]),e._v(" "),t("td",[e._v("vector")]),e._v(" "),t("td",[t("code",[e._v("POLYGON((-122.4 37.6,-122.35 37.6,-122.35 37.64,-122.4 37.64,-122.4 37.6))")]),e._v(", "),t("code",[e._v("POLYGON((-122.51 37.5,-122.48 37.5,-122.48 37.52,-122.51 37.52,-122.51 37.5))")])]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/4326",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:4326"),t("OutboundLink")],1)])])])]),e._v(" "),t("p",[e._v("A dimension with geometries can consist of points, linestrings, polygons, multi points, multi linestrings, or multi polygons.\nIt is not possible to mix geometry types, but the single geometry type with their corresponding multi type can be combined in a dimension (e.g. points and multi points).\nEmpty geometries (such as GeoJSON features with a "),t("code",[e._v("null")]),e._v(" geometry or GeoJSON geometries with an empty coordinates array) are allowed and can sometimes also be the result of certain vector operations such as a negative buffer.")]),e._v(" "),t("h3",{attrs:{id:"applying-processes-on-dimensions"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#applying-processes-on-dimensions"}},[e._v("#")]),e._v(" Applying Processes on Dimensions")]),e._v(" "),t("p",[e._v('Some processes are typically applied "along a dimension". You can imagine said dimension as an arrow and whatever is happening as a parallel process to that arrow. It simply means: "we focus on '),t("em",[e._v("this")]),e._v(' dimension right now".')]),e._v(" "),t("h3",{attrs:{id:"resolution"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#resolution"}},[e._v("#")]),e._v(" Resolution")]),e._v(" "),t("p",[e._v("The resolution of a dimension gives information about what interval lies between observations. This is most obvious with the temporal resolution, where the intervals depict how often observations were made. Spatial resolution gives information about the pixel spacing, meaning how many 'real world meters' are contained in a pixel. The number of bands and their wavelength intervals give information about the spectral resolution.")]),e._v(" "),t("h3",{attrs:{id:"coordinate-reference-system-as-a-dimension"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#coordinate-reference-system-as-a-dimension"}},[e._v("#")]),e._v(" Coordinate Reference System as a Dimension")]),e._v(" "),t("p",[e._v("In the example above, "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" dimension values have a "),t("em",[e._v("unique")]),e._v(" relationship to world coordinates through their coordinate reference system (CRS). This implies that a single coordinate reference system is associated with these "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" dimensions. If we want to create a data cube from multiple tiles spanning different coordinate reference systems (e.g. Sentinel-2: different UTM zones), we would "),t("em",[e._v("have")]),e._v(" to resample/warp those to a single coordinate reference system. In many cases, this is wanted because we want to be able to "),t("em",[e._v("look")]),e._v(" at the result, meaning it is available in a single coordinate reference system.")]),e._v(" "),t("p",[e._v("Resampling is however costly, involves (some) data loss, and is in general not reversible. Suppose that we want to work only on the spectral and temporal dimensions of a data cube, and do not want to do any resampling. In that case, one could create one data cube for each coordinate reference system. An alternative would be to create one "),t("em",[e._v("single")]),e._v(" data cube containing all tiles that has an "),t("em",[e._v("additional dimension")]),e._v(" with the coordinate reference system. In that data cube, "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" no longer point to a unique world coordinate, because identical "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" coordinate pairs occur in each UTM zone. Now, only the combination ("),t("em",[e._v("x")]),e._v(", "),t("em",[e._v("y")]),e._v(", "),t("em",[e._v("crs")]),e._v(") has a unique relationship to the world coordinates.")]),e._v(" "),t("p",[e._v("On such a "),t("em",[e._v("crs-dimensioned data cube")]),e._v(", several operations make perfect sense, such as "),t("code",[e._v("apply")]),e._v(" or "),t("code",[e._v("reduce_dimension")]),e._v(" on spectral and/or temporal dimensions. A simple reduction over the "),t("code",[e._v("crs")]),e._v(" dimension, using "),t("em",[e._v("sum")]),e._v(" or "),t("em",[e._v("mean")]),e._v(' would typically not make sense. The "reduction" (removal) of the '),t("code",[e._v("crs")]),e._v(" dimension that is meaningful involves the resampling/warping of all sub-cubes for the "),t("code",[e._v("crs")]),e._v(" dimension to a single, common target coordinate reference system.")]),e._v(" "),t("h2",{attrs:{id:"values-in-a-datacube"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#values-in-a-datacube"}},[e._v("#")]),e._v(" Values in a datacube")]),e._v(" "),t("p",[e._v("openEO datacubes contain scalar values (e.g. strings, numbers or boolean values), with all other associated attributes stored in dimensions (e.g. coordinates or timestamps). Attributes such as the CRS or the sensor can also be turned into dimensions. Be advised that in such a case, the uniqueness of pixel coordinates may be affected. When usually, "),t("code",[e._v("(x, y)")]),e._v(" refers to a unique location, that changes to "),t("code",[e._v("(x, y, CRS)")]),e._v(" when "),t("code",[e._v("(x, y)")]),e._v(" values are reused in other coordinate reference systems (e.g. two neighboring UTM zones).")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Be Careful with Data Types")]),e._v(" "),t("p",[e._v("As stated above, datacubes only contain scalar values. However, implementations may differ in their ability to handle or convert them. Implementations may also not allow mixing data types in a datacube. For example, returning a boolean value for a reducer on a numerical datacube may result in an error on some back-ends. The recommendation is to not change the data type of values in a datacube unless the back-end supports it explicitly.")])]),e._v(" "),t("p",[e._v("Data cube values can be sampled in two different ways. The values are either area or point samples.")]),e._v(" "),t("ul",[t("li",[e._v("Area sampling aggregates measurements over defined regions, i.e. the grid cells for raster data or polygons/lines for vector data.")]),e._v(" "),t("li",[e._v("Point sampling collects data at specific locations, providing detailed information for specific points.")])]),e._v(" "),t("h2",{attrs:{id:"processes-on-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#processes-on-datacubes"}},[e._v("#")]),e._v(" Processes on Datacubes")]),e._v(" "),t("p",[e._v("In the following part, the basic processes for manipulating datacubes are introduced.")]),e._v(" "),t("h3",{attrs:{id:"filter"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#filter"}},[e._v("#")]),e._v(" Filter")]),e._v(" "),t("p",[e._v("When filtering data (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_spatial")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_temporal")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_bands",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_bands")]),t("OutboundLink")],1),e._v("), only the data that satisfies a condition is returned. For example, this condition could be a timestamp or interval, (a set of) coordinates, or specific bands. By applying filtering the datacube becomes smaller, according to the selected data.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Filtering vegetarian options from [corn, potato, pig] returns [corn, potato].\n"}},[t("code",[e._v("filter([🌽, 🥔, 🐷], isVegetarian) => [🌽, 🥔]")])])])]),e._v(" "),t("p",[e._v("In the image, the example datacube can be seen at the top with labeled dimensions. The filtering techniques are displayed separately below. On the left, the datacube is filtered temporally with the interval "),t("code",[e._v('["2020-10-15", "2020-10-27"]')]),e._v(". The result is a cube with only the rasters for the timestep that lies within that interval ("),t("code",[e._v('"2020-10-25"')]),e._v(") and unchanged bands and spatial dimensions. Likewise, the original cube is filtered for a specific band "),t("code",[e._v('["nir"]')]),e._v(" in the middle and a specific spatial region "),t("code",[e._v("[Polygon(...)]")]),e._v(" on the right.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(295),alt:"Datacube filtering: From the datacube 4 by 3 grid, arrows depict what happens if the grid is filtered. Temporal filtering results in data for one timestep with all four bands, filtering bands results in data with one band with all three timesteps, and spatial filtering results in all timesteps and bands being preserved, but all with a smaller area."}}),e._v(" "),t("figcaption",[e._v("Filtering the sample datacube. It is displayed at the top with dimensions labels. Filtered results are shown at the bottom.")])]),e._v(" "),t("h3",{attrs:{id:"apply"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#apply"}},[e._v("#")]),e._v(" Apply")]),e._v(" "),t("p",[e._v("The "),t("code",[e._v("apply*")]),e._v(" functions (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#apply",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_neighborhood",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_neighborhood")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_dimension",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_dimension")]),t("OutboundLink")],1),e._v(") employ a process on the datacube that calculates new pixel values for each pixel, based on "),t("code",[e._v("n")]),e._v(" other pixels. Please note that several programming languages use the name "),t("code",[e._v("map")]),e._v(" instead of "),t("code",[e._v("apply")]),e._v(", but they describe the same type of function.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Applying the process 'cook' to [corn, potato, pig] returns [popcorn, fries, meat]."}},[t("code",[e._v("apply([🌽, 🥔, 🐷], cook) => [🍿, 🍟, 🍖]")])])])]),e._v(" "),t("p",[e._v("For the case "),t("code",[e._v("n = 1")]),e._v(" this is called a unary function and means that only the pixel itself is considered when calculating the new pixel value. A prominent example is the "),t("code",[e._v("absolute()")]),e._v(" function, calculating the absolute value of the input pixel value.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(296),alt:"Datacube apply unary: 3 example tiles hold values below and above 0. after applying the process 'absolute', all values in the three example tiles have changed to their absolute values above 0."}}),e._v(" "),t("figcaption",[e._v("Applying an unary process. Only the pixel itself is considered for calculating the new pixel value.")])]),e._v(" "),t("p",[e._v("If "),t("code",[e._v("n")]),e._v(" is larger than 1, the function is called n-ary. In practice, this means that the pixel neighbourhood is taken into account to calculate the new pixel value. Such neighbourhoods can be of spatial and/or temporal nature. A spatial function works on a kernel that weights the surrounding pixels (e.g. smoothing values with nearby observations), a temporal function works on a time series at a certain pixel location (e.g. smoothing values over time). Combinations of types to n-dimensional neighbourhoods are also possible.")]),e._v(" "),t("p",[e._v("In the example below, an example weighted kernel (shown in the middle) is applied to the cube (via "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_kernel",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_kernel")]),t("OutboundLink")],1),e._v("). To avoid edge effects (affecting pixels on the edge of the image with less neighbours), a padding has been added in the background.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(297),alt:"Datacube apply spatial kernel: Three example tiles hold some values with a lot of variance. A spatial kernel (a cell plus it's 4 direct neighbours) is applied to all pixels, and the result appears to be spatially smoothed, with less variance."}}),e._v(" "),t("figcaption",[e._v("Applying a spatial kernel. For calculating each new pixel value, the defined weighted neighbourhood is used.")])]),e._v(" "),t("p",[e._v("Of course this also works for temporal neighbourhoods (timeseries), considering neighbours before and after a pixel. To be able to show the effect, two timesteps were added in this example figure. A moving average of window size 3 is then applied, meaning that for each pixel the average is calculated out of the previous, the next, and the timestep in question (t"),t("sub",[e._v("n-1")]),e._v(", t"),t("sub",[e._v("n")]),e._v(" and t"),t("sub",[e._v("n+1")]),e._v("). No padding was added which is why we observe edge effects (NA values are returned for t"),t("sub",[e._v("1")]),e._v(" and t"),t("sub",[e._v("5")]),e._v(", because their temporal neighbourhood is missing input timesteps).")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(298),alt:"Datacube apply temporal moving average: Smoothing is applied to 5 example tiles by calculating the mean of 3 timesteps of every single pixel. The resulting tiles for the timestamps look much more alike."}}),e._v(" "),t("figcaption",[e._v("Applying a moving average (temporal smoothing) by averaging the direct temporal neighbourhoods of pixels. No padding is used, which leads to edge effects.")])]),e._v(" "),t("p",[e._v("Alternatively, a process can also be applied along a dimension of the datacube, meaning the input is no longer a neighbourhood of some sort but all pixels along that dimension ("),t("code",[e._v("n")]),e._v(" equals the complete dimension). If a process is applied along the "),t("code",[e._v("time")]),e._v(" dimension (e.g. a breakpoint detection), the complete pixel timeseries are the input. If a process is applied along the "),t("code",[e._v("spatial")]),e._v(" dimensions (e.g. a "),t("code",[e._v("mean")]),e._v("), all pixels of an image are the input. The process is then applied to all pixels along that dimension and the dimension continues to exist. This is in contrast to "),t("a",{attrs:{href:"#reduce"}},[e._v("reduce")]),e._v(". In the image below, a "),t("code",[e._v("mean")]),e._v(" is applied to the "),t("code",[e._v("time")]),e._v(" dimension. An example pixel timeseries is highlighted by a green line and processed step-by-step.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(299),alt:"Datacube apply dimension time: The mean of all 5 timesteps is calculated for every single pixel. The resulting 5 tiles look exaclty the same, as they have been averaged."}}),e._v(" "),t("figcaption",[e._v("Applying a mean to the temporal dimension.")])]),e._v(" "),t("h3",{attrs:{id:"resample"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#resample"}},[e._v("#")]),e._v(" Resample")]),e._v(" "),t("p",[e._v("In a resampling processes (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#resample_cube_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("resample_cube_spatial")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#resample_cube_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("resample_cube_temporal")]),t("OutboundLink")],1),e._v("), the "),t("em",[e._v("layout")]),e._v(" of a certain dimension is changed into another "),t("em",[e._v("layout")]),e._v(", most likely also changing the resolution of that dimension. This is done by mapping values of the source (old) datacube to the new layout of the target (new) datacube. During that process, resolutions can be "),t("em",[e._v("upscaled")]),e._v(" or "),t("em",[e._v("downscaled")]),e._v(" (also called "),t("em",[e._v("upsampling")]),e._v(" and "),t("em",[e._v("downsampling")]),e._v("), depending on whether they have a finer or a coarser spacing afterwards. A function is then needed to translate the existing data into the new resolution. A prominent example is to reproject a datacube into the coordinate reference system of another datacube, for example in order to merge the two cubes.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Downscaling a raster image (to infinity) returns one pixel."}},[t("code",[e._v("resample(🖼️, downscale) => 🟦")])])]),e._v(" "),t("p",[t("span",{attrs:{title:"Reprojecting a globe results into a map."}},[t("code",[e._v("resample(🌍, reproject) => 🗺️")])])])]),e._v(" "),t("p",[e._v("The first figure gives an overview of temporal resampling. How exactly the input timesteps are rescaled to the output timesteps depends on the resampling function.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(300),alt:"Datacube temporal resampling (up and down): Downsampling: To a timeline-representation of the example tiles, another timeline with only 2 steps at different dates is applied. The result has tiles only at those new timesteps. In Upsampling, the existing 3 timesteps are sampled into 5 result timesteps."}}),e._v(" "),t("figcaption",[e._v("Temporal downsampling is seen on the left, upsampling on the right. The temporal layout that the cubes are resampled to is displayed in the middle.")])]),e._v(" "),t("p",[e._v("The second figure displays spatial resampling. Observe how in the upsampling process, the output datacube has not gained in information value. The resulting grid still carries the same pixel information, but in higher spatial resolution. Other upsampling methods may yield smoother results, e.g. by using interpolation.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(301),alt:"Datacube spatial resampling (up and down): Downsampling: The resulting tiles have a lower spatial resolution than the input tiles. Upsampling: The resulting tiles have a higher spatial resolution than the input tiles, but contain the same image than before (no information added)."}}),e._v(" "),t("figcaption",[e._v("Spatial resampling. The geometry which the input cube is resampled to is displayed in the middle. The output cube then contains the same information, but in the resampled spatial layout.")])]),e._v(" "),t("h3",{attrs:{id:"reduce"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#reduce"}},[e._v("#")]),e._v(" Reduce")]),e._v(" "),t("p",[e._v("The "),t("a",{attrs:{href:"https://processes.openeo.org/#reduce_dimension",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("reduce_dimension")]),t("OutboundLink")],1),e._v(" process "),t("em",[e._v("collapses")]),e._v(" a whole dimension of the datacube. It does so by using some sort of "),t("strong",[e._v("reducer")]),e._v(", which is a function that calculates a single result from an amount of values, as e.g. "),t("code",[e._v("mean()")]),e._v(", "),t("code",[e._v("min()")]),e._v(" and "),t("code",[e._v("max()")]),e._v(" are. For example we can reduce the time dimension ("),t("code",[e._v("t")]),e._v(") of a timeseries by calculating the mean value of all timesteps for each pixel. We are left with a cube that has no time dimension, because all values of that dimension are compressed into a single mean value. The same goes for e.g. the spatial dimensions: If we calculate the mean along the "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(" dimensions, we are left without any spatial dimensions, but a mean value for each instance that previously was a raster is returned. In the image below, the dimensions that are reduced are crossed out in the result.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Reducing [mixed greens, cucumber, tomato, onion] returns a salad."}},[t("code",[e._v("reduce([🥬, 🥒, 🍅, 🧅], prepare) => 🥗")])])])]),e._v(" "),t("p",[e._v("Think of it as a waste press that does math instead of using brute force. Given a representation of our example datacube, let's see how it is affected.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(302),alt:"Datacube reduce: Three arrows depict what happens to the 12 example tiles, if they are reduced: Reducing timesteps leads to four tiles (one for each band), and the time dimension is deleted. Reducing bands lead to one tile per timestep, and the bands dimension is deleted. Reducing spatially leads to the original 4 by 3 bands by time layout, but the result has no spatial dimension and thus, the tiles have been turned into single values, per tile."}}),e._v(" "),t("figcaption",[e._v("Overview of reducing the sample datacube. Dimensions that are reduced are crossed out in the result images. Their information value has been compressed by a reducer function.")])]),e._v(" "),t("h3",{attrs:{id:"aggregate"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#aggregate"}},[e._v("#")]),e._v(" Aggregate")]),e._v(" "),t("p",[e._v("An aggregation of a datacube can be thought of as a grouped reduce. That means it consists of two steps:")]),e._v(" "),t("ol",[t("li",[e._v("Grouping via a grouping variable, i.e. spatial geometries or temporal intervals")]),e._v(" "),t("li",[e._v("Reducing these groups along the grouped dimension with a certain reducer function, e.g. calculating the mean pixel value per polygon or the maximum pixel values per month")])]),e._v(" "),t("p",[e._v("While the layout of the reduced dimension is changed, other dimensions keep their resolution and geometry. But in contrast to pure "),t("code",[e._v("reduce")]),e._v(", the dimensions along which the reducer function is applied still exist after the operation.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Aggregating different family representations with 3, 2 and 4 members with the function 'countFamilyMembers' returns [3, 2, 4]."}},[t("code",[e._v("aggregate(👪 👩‍👦 👨‍👩‍👦‍👦, countFamilyMembers) => [3️⃣, 2️⃣, 4️⃣]")])])])]),e._v(" "),t("p",[e._v("A temporal aggregation (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#aggregate_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("aggregate_temporal")]),t("OutboundLink")],1),e._v(") is similar to the downsampling process, as it can be seen in the according image above. Intervals for grouping can either be set manually, or periods can be chosen: monthly, yearly, etc. All timesteps in an interval are then collapsed via a reducer function ("),t("code",[e._v("mean")]),e._v(", "),t("code",[e._v("max")]),e._v(", etc.) and assigned to the given new labels.")]),e._v(" "),t("p",[e._v("A spatial aggregation (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#aggregate_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("aggregate_spatial")]),t("OutboundLink")],1),e._v(") works in a similar manner. Polygons, lines and points can be selected for grouping. Their spatial dimension is then reduced by a given process and thus, a vector cube is returned. The vector cube then has dimensions containing features, attributes and time. In the graphic below, the grouping is only shown for the first timestep.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(303),alt:"Datacube spatial aggregation: A line and a polygon are selected from the original example tiles. The pixels covered by these geometries are aggregated and the result consists no longer of imagery tiles but of an array with values for 2 geometries by 4 bands by 3 timesteps."}}),e._v(" "),t("figcaption",[e._v("When aggregating spatially, pixels are grouped (cut out) based on geometries, and then collapsed with a reducer function. A vector datacube is returned (as shown with dimensions names and labels). Please note that this visualization shows aggregation on a data cube with four dimensions, but "),t("code",[e._v("aggregate_spatial")]),e._v(" specifically can only handle data cubes with three dimensions as of now.")])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file diff --git a/assets/js/13.a494e712.js b/assets/js/13.a494e712.js deleted file mode 100644 index e08e8a53e..000000000 --- a/assets/js/13.a494e712.js +++ /dev/null @@ -1 +0,0 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[13],{292:function(e,t,a){e.exports=a.p+"assets/img/dc_timeseries.c2c7a902.png"},293:function(e,t,a){e.exports=a.p+"assets/img/dc_flat.01dc91de.png"},294:function(e,t,a){e.exports=a.p+"assets/img/vector.201588fe.png"},295:function(e,t,a){e.exports=a.p+"assets/img/dc_filter.bdafa6d0.png"},296:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_unary.f69f840c.png"},297:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_kernel.4013a72c.png"},298:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_ts.4f9449f6.png"},299:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_dim_ts.c895aa81.png"},300:function(e,t,a){e.exports=a.p+"assets/img/dc_resample_time.987e797c.png"},301:function(e,t,a){e.exports=a.p+"assets/img/dc_resample_space.417f814b.png"},302:function(e,t,a){e.exports=a.p+"assets/img/dc_reduce.872f16c2.png"},303:function(e,t,a){e.exports=a.p+"assets/img/dc_aggregate_space.ae550945.png"},471:function(e,t,a){"use strict";a.r(t);var s=a(4),i=Object(s.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#datacubes"}},[e._v("#")]),e._v(" Datacubes")]),e._v(" "),t("h2",{attrs:{id:"what-are-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#what-are-datacubes"}},[e._v("#")]),e._v(" What are Datacubes?")]),e._v(" "),t("p",[e._v("Data is represented as datacubes in openEO, which are multi-dimensional arrays with additional information about their dimensionality. Datacubes can provide a nice and tidy interface for spatiotemporal data as well as for the operations you may want to execute on them. As they are arrays, it might be easiest to look at raster data as an example, even though datacubes can hold vector data as well. Our example data however consists of a 6x7 raster with 4 bands ["),t("code",[e._v("blue")]),e._v(", "),t("code",[e._v("green")]),e._v(", "),t("code",[e._v("red")]),e._v(", "),t("code",[e._v("near-infrared")]),e._v("] and 3 timesteps ["),t("code",[e._v("2020-10-01")]),e._v(", "),t("code",[e._v("2020-10-13")]),e._v(", "),t("code",[e._v("2020-10-25")]),e._v("], displayed here in an orderly, timeseries-like manner:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(292),alt:"Raster datacube timeseries: 12 imagery tiles are depicted, grouped by 3 dates along a timeline (time dimension). Each date has a blue, green, red and near-infrared band (bands dimension). Each single tile has the dimensions x and y (spatial dimensions)."}}),e._v(" "),t("figcaption",[e._v("An examplary raster datacube with 4 dimensions: x, y, bands and time.")])]),e._v(" "),t("p",[e._v("It is important to understand that datacubes are designed to make things easier for us, and are not literally a cube, meaning that the above plot is just as good a representation as any other. That is why we can switch the dimensions around and display them in whatever way we want, including the view below:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(293),alt:"Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates)."}}),e._v(" "),t("figcaption",[e._v("This is the 'raw' data collection that is our example datacube. The grayscale images are colored for understandability, and dimension labels are displayed.")])]),e._v(" "),t("p",[e._v("A vector datacube on the other hand could look like this:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(294),alt:"An examplary vector datacube with 3 dimensions: 2 geometries are given for the `geometries` dimension, along with 3 timesteps for the temporal dimension `time` and 4 bands in the `bands` dimension."}}),e._v(" "),t("figcaption",[e._v("An examplary vector datacube with 3 dimensions: 2 geometries are given for the "),t("code",[e._v("geometries")]),e._v(" dimension, along with 3 timesteps for the temporal dimension "),t("code",[e._v("time")]),e._v(" and 4 bands in the "),t("code",[e._v("bands")]),e._v(" dimension.")])]),e._v(" "),t("p",[t("a",{attrs:{href:"https://r-spatial.org/r/2022/09/12/vdc.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Vector datacubes"),t("OutboundLink")],1),e._v(" and raster datacubes are common cases of datacubes in the EO domain.\nA raster datacube has at least two spatial dimensions (usually named "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(") and a vector datacube has at least one geometry dimension (usually named "),t("code",[e._v("geometry")]),e._v(').\nThe purpose of these distinctions is simply to make it easier to describe "special" cases of datacubes, but you can also define other types such as a temporal datacube that has at least one temporal dimension (usually named '),t("code",[e._v("t")]),e._v(").")]),e._v(" "),t("p",[e._v("The following additional information are usually available for datacubes:")]),e._v(" "),t("ul",[t("li",[e._v("the dimensions (see "),t("a",{attrs:{href:"#dimensions"}},[e._v("below")]),e._v(")")]),e._v(" "),t("li",[e._v("a sampling method (see "),t("a",{attrs:{href:"#values-in-a-data-cube"}},[e._v("below")]),e._v(")")]),e._v(" "),t("li",[e._v("a unit for the values")])]),e._v(" "),t("p",[e._v("All these information are usually provided through the datacube metadata.")]),e._v(" "),t("h2",{attrs:{id:"dimensions"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#dimensions"}},[e._v("#")]),e._v(" Dimensions")]),e._v(" "),t("p",[e._v("A dimension refers to a certain axis of a datacube. This includes all variables (e.g. bands), which are represented as dimensions. Our exemplary raster datacube has the spatial dimensions "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(", and the temporal dimension "),t("code",[e._v("t")]),e._v(". Furthermore, it has a "),t("code",[e._v("bands")]),e._v(" dimension, extending into the realm of "),t("em",[e._v("what kind of information")]),e._v(" is contained in the cube.")]),e._v(" "),t("p",[e._v("The following properties are usually available for dimensions:")]),e._v(" "),t("ul",[t("li",[e._v("name")]),e._v(" "),t("li",[e._v("type (potential types include: spatial (raster or vector data), temporal and other data such as bands)")]),e._v(" "),t("li",[e._v("axis (for spatial dimensions) / number")]),e._v(" "),t("li",[e._v("labels (usually exposed through textual or numerical representations, in the metadata as nominal values and/or extents)")]),e._v(" "),t("li",[e._v("reference system / projection")]),e._v(" "),t("li",[e._v("resolution / step size")]),e._v(" "),t("li",[e._v("unit for the labels (either explicitly specified or implicitly provided by the reference system)")]),e._v(" "),t("li",[e._v("additional information specific to the dimension type (e.g. the geometry types for a dimension containing geometries)")])]),e._v(" "),t("p",[e._v("All these information are usually provided through the datacube metadata.")]),e._v(" "),t("p",[e._v("Here is an overview of the dimensions contained in our example raster datacube above:")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("#")]),e._v(" "),t("th",[e._v("name")]),e._v(" "),t("th",[e._v("type")]),e._v(" "),t("th",[e._v("labels")]),e._v(" "),t("th",[e._v("resolution")]),e._v(" "),t("th",[e._v("reference system")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[e._v("1")]),e._v(" "),t("td",[t("code",[e._v("x")])]),e._v(" "),t("td",[e._v("spatial")]),e._v(" "),t("td",[t("code",[e._v("466380")]),e._v(", "),t("code",[e._v("466580")]),e._v(", "),t("code",[e._v("466780")]),e._v(", "),t("code",[e._v("466980")]),e._v(", "),t("code",[e._v("467180")]),e._v(", "),t("code",[e._v("467380")])]),e._v(" "),t("td",[e._v("200m")]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/32627",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:32627"),t("OutboundLink")],1)])]),e._v(" "),t("tr",[t("td",[e._v("2")]),e._v(" "),t("td",[t("code",[e._v("y")])]),e._v(" "),t("td",[e._v("spatial")]),e._v(" "),t("td",[t("code",[e._v("7167130")]),e._v(", "),t("code",[e._v("7166930")]),e._v(", "),t("code",[e._v("7166730")]),e._v(", "),t("code",[e._v("7166530")]),e._v(", "),t("code",[e._v("7166330")]),e._v(", "),t("code",[e._v("7166130")]),e._v(", "),t("code",[e._v("7165930")])]),e._v(" "),t("td",[e._v("200m")]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/32627",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:32627"),t("OutboundLink")],1)])]),e._v(" "),t("tr",[t("td",[e._v("3")]),e._v(" "),t("td",[t("code",[e._v("bands")])]),e._v(" "),t("td",[e._v("bands")]),e._v(" "),t("td",[t("code",[e._v("blue")]),e._v(", "),t("code",[e._v("green")]),e._v(", "),t("code",[e._v("red")]),e._v(", "),t("code",[e._v("nir")])]),e._v(" "),t("td",[e._v("4 bands")]),e._v(" "),t("td",[e._v("-")])]),e._v(" "),t("tr",[t("td",[e._v("4")]),e._v(" "),t("td",[t("code",[e._v("t")])]),e._v(" "),t("td",[e._v("temporal")]),e._v(" "),t("td",[t("code",[e._v("2020-10-01")]),e._v(", "),t("code",[e._v("2020-10-13")]),e._v(", "),t("code",[e._v("2020-10-25")])]),e._v(" "),t("td",[e._v("12 days")]),e._v(" "),t("td",[e._v("Gregorian calendar / UTC")])])])]),e._v(" "),t("p",[e._v('Dimension labels are usually either numerical or text (also known as "strings"), which also includes textual representations of timestamps or geometries for example.\nFor example, temporal labels are usually encoded as '),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/ISO_8601",target:"_blank",rel:"noopener noreferrer"}},[e._v("ISO 8601"),t("OutboundLink")],1),e._v(" compatible dates and/or times and similarly geometries can be encoded as "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry",target:"_blank",rel:"noopener noreferrer"}},[e._v("Well-known Text (WKT)"),t("OutboundLink")],1),e._v(" or be represented by their IDs.")]),e._v(" "),t("p",[e._v("Dimensions with a natural/inherent order (usually all temporal and spatial raster dimensions) are always sorted. Dimensions without inherent order (usually "),t("code",[e._v("bands")]),e._v("), retain the order in which they have been defined in metadata or processes (e.g. through "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_bands",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_bands")]),t("OutboundLink")],1),e._v("), with new labels simply being appended to the existing labels.")]),e._v(" "),t("p",[e._v("A geometry dimension is not included in the example raster datacube above and it is not used in the following examples, but to show how a vector dimension with two polygons could look like:")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("name")]),e._v(" "),t("th",[e._v("type")]),e._v(" "),t("th",[e._v("labels")]),e._v(" "),t("th",[e._v("reference system")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[t("code",[e._v("geometry")])]),e._v(" "),t("td",[e._v("vector")]),e._v(" "),t("td",[t("code",[e._v("POLYGON((-122.4 37.6,-122.35 37.6,-122.35 37.64,-122.4 37.64,-122.4 37.6))")]),e._v(", "),t("code",[e._v("POLYGON((-122.51 37.5,-122.48 37.5,-122.48 37.52,-122.51 37.52,-122.51 37.5))")])]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/4326",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:4326"),t("OutboundLink")],1)])])])]),e._v(" "),t("p",[e._v("A dimension with geometries can consist of points, linestrings, polygons, multi points, multi linestrings, or multi polygons.\nIt is not possible to mix geometry types, but the single geometry type with their corresponding multi type can be combined in a dimension (e.g. points and multi points).\nEmpty geometries (such as GeoJSON features with a "),t("code",[e._v("null")]),e._v(" geometry or GeoJSON geometries with an empty coordinates array) are allowed and can sometimes also be the result of certain vector operations such as a negative buffer.")]),e._v(" "),t("h3",{attrs:{id:"applying-processes-on-dimensions"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#applying-processes-on-dimensions"}},[e._v("#")]),e._v(" Applying Processes on Dimensions")]),e._v(" "),t("p",[e._v('Some processes are typically applied "along a dimension". You can imagine said dimension as an arrow and whatever is happening as a parallel process to that arrow. It simply means: "we focus on '),t("em",[e._v("this")]),e._v(' dimension right now".')]),e._v(" "),t("h3",{attrs:{id:"resolution"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#resolution"}},[e._v("#")]),e._v(" Resolution")]),e._v(" "),t("p",[e._v("The resolution of a dimension gives information about what interval lies between observations. This is most obvious with the temporal resolution, where the intervals depict how often observations were made. Spatial resolution gives information about the pixel spacing, meaning how many 'real world meters' are contained in a pixel. The number of bands and their wavelength intervals give information about the spectral resolution.")]),e._v(" "),t("h3",{attrs:{id:"coordinate-reference-system-as-a-dimension"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#coordinate-reference-system-as-a-dimension"}},[e._v("#")]),e._v(" Coordinate Reference System as a Dimension")]),e._v(" "),t("p",[e._v("In the example above, "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" dimension values have a "),t("em",[e._v("unique")]),e._v(" relationship to world coordinates through their coordinate reference system (CRS). This implies that a single coordinate reference system is associated with these "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" dimensions. If we want to create a data cube from multiple tiles spanning different coordinate reference systems (e.g. Sentinel-2: different UTM zones), we would "),t("em",[e._v("have")]),e._v(" to resample/warp those to a single coordinate reference system. In many cases, this is wanted because we want to be able to "),t("em",[e._v("look")]),e._v(" at the result, meaning it is available in a single coordinate reference system.")]),e._v(" "),t("p",[e._v("Resampling is however costly, involves (some) data loss, and is in general not reversible. Suppose that we want to work only on the spectral and temporal dimensions of a data cube, and do not want to do any resampling. In that case, one could create one data cube for each coordinate reference system. An alternative would be to create one "),t("em",[e._v("single")]),e._v(" data cube containing all tiles that has an "),t("em",[e._v("additional dimension")]),e._v(" with the coordinate reference system. In that data cube, "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" no longer point to a unique world coordinate, because identical "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" coordinate pairs occur in each UTM zone. Now, only the combination ("),t("em",[e._v("x")]),e._v(", "),t("em",[e._v("y")]),e._v(", "),t("em",[e._v("crs")]),e._v(") has a unique relationship to the world coordinates.")]),e._v(" "),t("p",[e._v("On such a "),t("em",[e._v("crs-dimensioned data cube")]),e._v(", several operations make perfect sense, such as "),t("code",[e._v("apply")]),e._v(" or "),t("code",[e._v("reduce_dimension")]),e._v(" on spectral and/or temporal dimensions. A simple reduction over the "),t("code",[e._v("crs")]),e._v(" dimension, using "),t("em",[e._v("sum")]),e._v(" or "),t("em",[e._v("mean")]),e._v(' would typically not make sense. The "reduction" (removal) of the '),t("code",[e._v("crs")]),e._v(" dimension that is meaningful involves the resampling/warping of all sub-cubes for the "),t("code",[e._v("crs")]),e._v(" dimension to a single, common target coordinate reference system.")]),e._v(" "),t("h2",{attrs:{id:"values-in-a-datacube"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#values-in-a-datacube"}},[e._v("#")]),e._v(" Values in a datacube")]),e._v(" "),t("p",[e._v("openEO datacubes contain scalar values (e.g. strings, numbers or boolean values), with all other associated attributes stored in dimensions (e.g. coordinates or timestamps). Attributes such as the CRS or the sensor can also be turned into dimensions. Be advised that in such a case, the uniqueness of pixel coordinates may be affected. When usually, "),t("code",[e._v("(x, y)")]),e._v(" refers to a unique location, that changes to "),t("code",[e._v("(x, y, CRS)")]),e._v(" when "),t("code",[e._v("(x, y)")]),e._v(" values are reused in other coordinate reference systems (e.g. two neighboring UTM zones).")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Be Careful with Data Types")]),e._v(" "),t("p",[e._v("As stated above, datacubes only contain scalar values. However, implementations may differ in their ability to handle or convert them. Implementations may also not allow mixing data types in a datacube. For example, returning a boolean value for a reducer on a numerical datacube may result in an error on some back-ends. The recommendation is to not change the data type of values in a datacube unless the back-end supports it explicitly.")])]),e._v(" "),t("p",[e._v("Data cube values can be sampled in two different ways. The values are either area or point samples.")]),e._v(" "),t("ul",[t("li",[e._v("Area sampling aggregates measurements over defined regions, i.e. the grid cells for raster data or polygons/lines for vector data.")]),e._v(" "),t("li",[e._v("Point sampling collects data at specific locations, providing detailed information for specific points.")])]),e._v(" "),t("h2",{attrs:{id:"processes-on-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#processes-on-datacubes"}},[e._v("#")]),e._v(" Processes on Datacubes")]),e._v(" "),t("p",[e._v("In the following part, the basic processes for manipulating datacubes are introduced.")]),e._v(" "),t("h3",{attrs:{id:"filter"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#filter"}},[e._v("#")]),e._v(" Filter")]),e._v(" "),t("p",[e._v("When filtering data (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_spatial")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_temporal")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_bands",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_bands")]),t("OutboundLink")],1),e._v("), only the data that satisfies a condition is returned. For example, this condition could be a timestamp or interval, (a set of) coordinates, or specific bands. By applying filtering the datacube becomes smaller, according to the selected data.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Filtering vegetarian options from [corn, potato, pig] returns [corn, potato].\n"}},[t("code",[e._v("filter([🌽, 🥔, 🐷], isVegetarian) => [🌽, 🥔]")])])])]),e._v(" "),t("p",[e._v("In the image, the example datacube can be seen at the top with labeled dimensions. The filtering techniques are displayed separately below. On the left, the datacube is filtered temporally with the interval "),t("code",[e._v('["2020-10-15", "2020-10-27"]')]),e._v(". The result is a cube with only the rasters for the timestep that lies within that interval ("),t("code",[e._v('"2020-10-25"')]),e._v(") and unchanged bands and spatial dimensions. Likewise, the original cube is filtered for a specific band "),t("code",[e._v('["nir"]')]),e._v(" in the middle and a specific spatial region "),t("code",[e._v("[Polygon(...)]")]),e._v(" on the right.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(295),alt:"Datacube filtering: From the datacube 4 by 3 grid, arrows depict what happens if the grid is filtered. Temporal filtering results in data for one timestep with all four bands, filtering bands results in data with one band with all three timesteps, and spatial filtering results in all timesteps and bands being preserved, but all with a smaller area."}}),e._v(" "),t("figcaption",[e._v("Filtering the sample datacube. It is displayed at the top with dimensions labels. Filtered results are shown at the bottom.")])]),e._v(" "),t("h3",{attrs:{id:"apply"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#apply"}},[e._v("#")]),e._v(" Apply")]),e._v(" "),t("p",[e._v("The "),t("code",[e._v("apply*")]),e._v(" functions (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#apply",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_neighborhood",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_neighborhood")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_dimension",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_dimension")]),t("OutboundLink")],1),e._v(") employ a process on the datacube that calculates new pixel values for each pixel, based on "),t("code",[e._v("n")]),e._v(" other pixels. Please note that several programming languages use the name "),t("code",[e._v("map")]),e._v(" instead of "),t("code",[e._v("apply")]),e._v(", but they describe the same type of function.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Applying the process 'cook' to [corn, potato, pig] returns [popcorn, fries, meat]."}},[t("code",[e._v("apply([🌽, 🥔, 🐷], cook) => [🍿, 🍟, 🍖]")])])])]),e._v(" "),t("p",[e._v("For the case "),t("code",[e._v("n = 1")]),e._v(" this is called a unary function and means that only the pixel itself is considered when calculating the new pixel value. A prominent example is the "),t("code",[e._v("absolute()")]),e._v(" function, calculating the absolute value of the input pixel value.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(296),alt:"Datacube apply unary: 3 example tiles hold values below and above 0. after applying the process 'absolute', all values in the three example tiles have changed to their absolute values above 0."}}),e._v(" "),t("figcaption",[e._v("Applying an unary process. Only the pixel itself is considered for calculating the new pixel value.")])]),e._v(" "),t("p",[e._v("If "),t("code",[e._v("n")]),e._v(" is larger than 1, the function is called n-ary. In practice, this means that the pixel neighbourhood is taken into account to calculate the new pixel value. Such neighbourhoods can be of spatial and/or temporal nature. A spatial function works on a kernel that weights the surrounding pixels (e.g. smoothing values with nearby observations), a temporal function works on a time series at a certain pixel location (e.g. smoothing values over time). Combinations of types to n-dimensional neighbourhoods are also possible.")]),e._v(" "),t("p",[e._v("In the example below, an example weighted kernel (shown in the middle) is applied to the cube (via "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_kernel",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_kernel")]),t("OutboundLink")],1),e._v("). To avoid edge effects (affecting pixels on the edge of the image with less neighbours), a padding has been added in the background.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(297),alt:"Datacube apply spatial kernel: Three example tiles hold some values with a lot of variance. A spatial kernel (a cell plus it's 4 direct neighbours) is applied to all pixels, and the result appears to be spatially smoothed, with less variance."}}),e._v(" "),t("figcaption",[e._v("Applying a spatial kernel. For calculating each new pixel value, the defined weighted neighbourhood is used.")])]),e._v(" "),t("p",[e._v("Of course this also works for temporal neighbourhoods (timeseries), considering neighbours before and after a pixel. To be able to show the effect, two timesteps were added in this example figure. A moving average of window size 3 is then applied, meaning that for each pixel the average is calculated out of the previous, the next, and the timestep in question (t"),t("sub",[e._v("n-1")]),e._v(", t"),t("sub",[e._v("n")]),e._v(" and t"),t("sub",[e._v("n+1")]),e._v("). No padding was added which is why we observe edge effects (NA values are returned for t"),t("sub",[e._v("1")]),e._v(" and t"),t("sub",[e._v("5")]),e._v(", because their temporal neighbourhood is missing input timesteps).")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(298),alt:"Datacube apply temporal moving average: Smoothing is applied to 5 example tiles by calculating the mean of 3 timesteps of every single pixel. The resulting tiles for the timestamps look much more alike."}}),e._v(" "),t("figcaption",[e._v("Applying a moving average (temporal smoothing) by averaging the direct temporal neighbourhoods of pixels. No padding is used, which leads to edge effects.")])]),e._v(" "),t("p",[e._v("Alternatively, a process can also be applied along a dimension of the datacube, meaning the input is no longer a neighbourhood of some sort but all pixels along that dimension ("),t("code",[e._v("n")]),e._v(" equals the complete dimension). If a process is applied along the "),t("code",[e._v("time")]),e._v(" dimension (e.g. a breakpoint detection), the complete pixel timeseries are the input. If a process is applied along the "),t("code",[e._v("spatial")]),e._v(" dimensions (e.g. a "),t("code",[e._v("mean")]),e._v("), all pixels of an image are the input. The process is then applied to all pixels along that dimension and the dimension continues to exist. This is in contrast to "),t("a",{attrs:{href:"#reduce"}},[e._v("reduce")]),e._v(". In the image below, a "),t("code",[e._v("mean")]),e._v(" is applied to the "),t("code",[e._v("time")]),e._v(" dimension. An example pixel timeseries is highlighted by a green line and processed step-by-step.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(299),alt:"Datacube apply dimension time: The mean of all 5 timesteps is calculated for every single pixel. The resulting 5 tiles look exaclty the same, as they have been averaged."}}),e._v(" "),t("figcaption",[e._v("Applying a mean to the temporal dimension.")])]),e._v(" "),t("h3",{attrs:{id:"resample"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#resample"}},[e._v("#")]),e._v(" Resample")]),e._v(" "),t("p",[e._v("In a resampling processes (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#resample_cube_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("resample_cube_spatial")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#resample_cube_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("resample_cube_temporal")]),t("OutboundLink")],1),e._v("), the "),t("em",[e._v("layout")]),e._v(" of a certain dimension is changed into another "),t("em",[e._v("layout")]),e._v(", most likely also changing the resolution of that dimension. This is done by mapping values of the source (old) datacube to the new layout of the target (new) datacube. During that process, resolutions can be "),t("em",[e._v("upscaled")]),e._v(" or "),t("em",[e._v("downscaled")]),e._v(" (also called "),t("em",[e._v("upsampling")]),e._v(" and "),t("em",[e._v("downsampling")]),e._v("), depending on whether they have a finer or a coarser spacing afterwards. A function is then needed to translate the existing data into the new resolution. A prominent example is to reproject a datacube into the coordinate reference system of another datacube, for example in order to merge the two cubes.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Downscaling a raster image (to infinity) returns one pixel."}},[t("code",[e._v("resample(🖼️, downscale) => 🟦")])])]),e._v(" "),t("p",[t("span",{attrs:{title:"Reprojecting a globe results into a map."}},[t("code",[e._v("resample(🌍, reproject) => 🗺️")])])])]),e._v(" "),t("p",[e._v("The first figure gives an overview of temporal resampling. How exactly the input timesteps are rescaled to the output timesteps depends on the resampling function.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(300),alt:"Datacube temporal resampling (up and down): Downsampling: To a timeline-representation of the example tiles, another timeline with only 2 steps at different dates is applied. The result has tiles only at those new timesteps. In Upsampling, the existing 3 timesteps are sampled into 5 result timesteps."}}),e._v(" "),t("figcaption",[e._v("Temporal downsampling is seen on the left, upsampling on the right. The temporal layout that the cubes are resampled to is displayed in the middle.")])]),e._v(" "),t("p",[e._v("The second figure displays spatial resampling. Observe how in the upsampling process, the output datacube has not gained in information value. The resulting grid still carries the same pixel information, but in higher spatial resolution. Other upsampling methods may yield smoother results, e.g. by using interpolation.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(301),alt:"Datacube spatial resampling (up and down): Downsampling: The resulting tiles have a lower spatial resolution than the input tiles. Upsampling: The resulting tiles have a higher spatial resolution than the input tiles, but contain the same image than before (no information added)."}}),e._v(" "),t("figcaption",[e._v("Spatial resampling. The geometry which the input cube is resampled to is displayed in the middle. The output cube then contains the same information, but in the resampled spatial layout.")])]),e._v(" "),t("h3",{attrs:{id:"reduce"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#reduce"}},[e._v("#")]),e._v(" Reduce")]),e._v(" "),t("p",[e._v("The "),t("a",{attrs:{href:"https://processes.openeo.org/#reduce_dimension",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("reduce_dimension")]),t("OutboundLink")],1),e._v(" process "),t("em",[e._v("collapses")]),e._v(" a whole dimension of the datacube. It does so by using some sort of "),t("strong",[e._v("reducer")]),e._v(", which is a function that calculates a single result from an amount of values, as e.g. "),t("code",[e._v("mean()")]),e._v(", "),t("code",[e._v("min()")]),e._v(" and "),t("code",[e._v("max()")]),e._v(" are. For example we can reduce the time dimension ("),t("code",[e._v("t")]),e._v(") of a timeseries by calculating the mean value of all timesteps for each pixel. We are left with a cube that has no time dimension, because all values of that dimension are compressed into a single mean value. The same goes for e.g. the spatial dimensions: If we calculate the mean along the "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(" dimensions, we are left without any spatial dimensions, but a mean value for each instance that previously was a raster is returned. In the image below, the dimensions that are reduced are crossed out in the result.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Reducing [mixed greens, cucumber, tomato, onion] returns a salad."}},[t("code",[e._v("reduce([🥬, 🥒, 🍅, 🧅], prepare) => 🥗")])])])]),e._v(" "),t("p",[e._v("Think of it as a waste press that does math instead of using brute force. Given a representation of our example datacube, let's see how it is affected.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(302),alt:"Datacube reduce: Three arrows depict what happens to the 12 example tiles, if they are reduced: Reducing timesteps leads to four tiles (one for each band), and the time dimension is deleted. Reducing bands lead to one tile per timestep, and the bands dimension is deleted. Reducing spatially leads to the original 4 by 3 bands by time layout, but the result has no spatial dimension and thus, the tiles have been turned into single values, per tile."}}),e._v(" "),t("figcaption",[e._v("Overview of reducing the sample datacube. Dimensions that are reduced are crossed out in the result images. Their information value has been compressed by a reducer function.")])]),e._v(" "),t("h3",{attrs:{id:"aggregate"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#aggregate"}},[e._v("#")]),e._v(" Aggregate")]),e._v(" "),t("p",[e._v("An aggregation of a datacube can be thought of as a grouped reduce. That means it consists of two steps:")]),e._v(" "),t("ol",[t("li",[e._v("Grouping via a grouping variable, i.e. spatial geometries or temporal intervals")]),e._v(" "),t("li",[e._v("Reducing these groups along the grouped dimension with a certain reducer function, e.g. calculating the mean pixel value per polygon or the maximum pixel values per month")])]),e._v(" "),t("p",[e._v("While the layout of the reduced dimension is changed, other dimensions keep their resolution and geometry. But in contrast to pure "),t("code",[e._v("reduce")]),e._v(", the dimensions along which the reducer function is applied still exist after the operation.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Aggregating different family representations with 3, 2 and 4 members with the function 'countFamilyMembers' returns [3, 2, 4]."}},[t("code",[e._v("aggregate(👪 👩‍👦 👨‍👩‍👦‍👦, countFamilyMembers) => [3️⃣, 2️⃣, 4️⃣]")])])])]),e._v(" "),t("p",[e._v("A temporal aggregation (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#aggregate_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("aggregate_temporal")]),t("OutboundLink")],1),e._v(") is similar to the downsampling process, as it can be seen in the according image above. Intervals for grouping can either be set manually, or periods can be chosen: monthly, yearly, etc. All timesteps in an interval are then collapsed via a reducer function ("),t("code",[e._v("mean")]),e._v(", "),t("code",[e._v("max")]),e._v(", etc.) and assigned to the given new labels.")]),e._v(" "),t("p",[e._v("A spatial aggregation (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#aggregate_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("aggregate_spatial")]),t("OutboundLink")],1),e._v(") works in a similar manner. Polygons, lines and points can be selected for grouping. Their spatial dimension is then reduced by a given process and thus, a vector cube is returned. The vector cube then has dimensions containing features, attributes and time. In the graphic below, the grouping is only shown for the first timestep.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(303),alt:"Datacube spatial aggregation: A line and a polygon are selected from the original example tiles. The pixels covered by these geometries are aggregated and the result consists no longer of imagery tiles but of an array with values for 2 geometries by 4 bands by 3 timesteps."}}),e._v(" "),t("figcaption",[e._v("When aggregating spatially, pixels are grouped (cut out) based on geometries, and then collapsed with a reducer function. A vector datacube is returned (as shown with dimensions names and labels). Please note that this visualization shows aggregation on a data cube with four dimensions, but "),t("code",[e._v("aggregate_spatial")]),e._v(" specifically can only handle data cubes with three dimensions as of now.")])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file diff --git a/assets/js/19.288519b3.js b/assets/js/19.71262d67.js similarity index 99% rename from assets/js/19.288519b3.js rename to assets/js/19.71262d67.js index eea71b7f6..d511c6758 100644 --- a/assets/js/19.288519b3.js +++ b/assets/js/19.71262d67.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[19,52],{164:function(e,t,s){"use strict";s.r(t);var a={computed:{unreleased(){return null!==this.version.apiTag.match(/(draft|alpha|beta)/i)},versioned(){return null!==this.$page.regularPath.match(/\/documentation\/([^\/]+)\//)},otherVersions(){return this.$site.themeConfig.versions.map((e,t)=>{var s=this.$site.themeConfig.versions[this.versionIndex],a=this.$site.themeConfig.versions[t];return e.regularPath=this.$page.regularPath.replace(s.path,a.path),e}).filter((e,t)=>t!==this.versionIndex&&this.$site.pages.findIndex(t=>t.regularPath==e.regularPath)>=0)},version(){return this.$site.themeConfig.versions[this.versionIndex]},defaultVersion(){return this.$site.themeConfig.versions[this.$site.themeConfig.defaultVersion]},versionIndex(){const{themeConfig:e}=this.$site;var t=this.$page.regularPath.match(/\/documentation\/([^\/]+)\//);if(Array.isArray(t)&&"string"==typeof t[1])for(var s in e.versions)if(e.versions[s].folder===t[1])return parseInt(s);return 0}}},i=s(4),r=Object(i.a)(a,void 0,void 0,!1,null,null,null);t.default=r.exports},322:function(e,t){function s(e){var t=new Error("Cannot find module '"+e+"'");throw t.code="MODULE_NOT_FOUND",t}s.keys=function(){return[]},s.resolve=s,e.exports=s,s.id=322},323:function(e,t,s){var a={"./0.4/developers/api/errors.json":324,"./1.0/developers/api/errors.json":325};function i(e){var t=r(e);return s(t)}function r(e){if(!s.o(a,e)){var t=new Error("Cannot find module '"+e+"'");throw t.code="MODULE_NOT_FOUND",t}return a[e]}i.keys=function(){return Object.keys(a)},i.resolve=r,e.exports=i,i.id=323},324:function(e){e.exports=JSON.parse('{"Internal":{"description":"An internal server error with a proprietary message.","message":"Server error: {message}","http":500,"tags":["General"]},"NotFound":{"description":"To be used if the requested resource does not exist. 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An example is computing a statistical operation, such as mean or median, on selected EO data. A process is similar to a function or method in programming languages.")]),e._v(" "),a("p",[e._v("A "),a("strong",[e._v("process graph")]),e._v(" chains specific process calls together. Similarly to scripts in the context of programming, process graphs organize and automate the execution of one or more processes that could alternatively be executed individually. In a process graph, processes need to be specific, i.e. concrete values for input parameters need to be specified. These arguments can again be process graphs, scalar values, arrays or objects.")]),e._v(" "),a("h2",{attrs:{id:"eo-data-collections"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#eo-data-collections"}},[e._v("#")]),e._v(" EO data (Collections)")]),e._v(" "),a("p",[e._v("In our domain, different terms are used to describe EO data(sets). Within openEO, a "),a("strong",[e._v("granule")]),e._v(" (sometimes also called "),a("em",[e._v("item")]),e._v(" or "),a("em",[e._v("asset")]),e._v(" in the specification) typically refers to a limited area and a single overpass leading to a very short observation period (seconds) or a temporal aggregation of such data (e.g. for 16-day MODIS composites). A "),a("strong",[e._v("collection")]),e._v(" is a sequence of granules sharing the same product specification. It typically corresponds to the series of products derived from data acquired by a sensor on board a satellite and having the same mode of operation.")]),e._v(" "),a("p",[e._v("The "),a("a",{attrs:{href:"http://ceos.org/ourwork/workinggroups/wgiss/access/opensearch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CEOS OpenSearch Best Practice Document v1.2"),a("OutboundLink")],1),e._v(" lists the following synonyms used by other organizations:")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("granule")]),e._v(": dataset (ESA, ISO 19115), granule (NASA), product (ESA, CNES), scene (JAXA)")]),e._v(" "),a("li",[a("strong",[e._v("collection")]),e._v(": dataset series (ESA, ISO 19115), collection (CNES, NASA), dataset (JAXA), product (JAXA)")])]),e._v(" "),a("p",[e._v("In openEO, a back-end offers a set of collections to be processed. All collections can be requested using a client and are described using the "),a("a",{attrs:{href:"https://github.com/radiantearth/stac-spec",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC (SpatioTemporal Asset Catalog) metadata specification"),a("OutboundLink")],1),e._v(" as STAC collections. A user can load (a subset of) a collection using a special process, which returns a (spatial) data cube. All further processing is then applied to the data cube on the back-end.")]),e._v(" "),a("h2",{attrs:{id:"spatial-data-cubes"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#spatial-data-cubes"}},[e._v("#")]),e._v(" Spatial data cubes")]),e._v(" "),a("p",[e._v("A "),a("em",[e._v("spatiotemporal")]),e._v(" data cube is a multidimensional array with one or more spatial or temporal dimensions.\nIn the EO domain, it is common to be implicit about the temporal dimension and just refer to them as "),a("em",[e._v("spatial")]),e._v(" data cubes in short.\nSpecial cases are raster and vector data cubes.")]),e._v(" "),a("p",[e._v("The figure below shows the data of\na four-dimensional (8 x 8 x 2 x 2) raster data cube, with dimension names\nand values:")]),e._v(" "),a("table",[a("thead",[a("tr",[a("th",[e._v("#")]),e._v(" "),a("th",[e._v("dimension name")]),e._v(" "),a("th",[e._v("dimension values")])])]),e._v(" "),a("tbody",[a("tr",[a("td",[e._v("1")]),e._v(" "),a("td",[e._v("x")]),e._v(" "),a("td",[e._v("288790.5, 288819, 288847.5, 288876, 288904.5, 288933, 288961.5, 288990")])]),e._v(" "),a("tr",[a("td",[e._v("2")]),e._v(" "),a("td",[e._v("y")]),e._v(" "),a("td",[e._v("9120747, 9120718, 9120690, 9120661, 9120633, 9120604, 9120576, 9120547")])]),e._v(" "),a("tr",[a("td",[e._v("3")]),e._v(" "),a("td",[e._v("band")]),e._v(" "),a("td",[a("code",[e._v("red")]),e._v(", "),a("code",[e._v("green")])])]),e._v(" "),a("tr",[a("td",[e._v("4")]),e._v(" "),a("td",[e._v("time")]),e._v(" "),a("td",[a("code",[e._v("2018-02-10")]),e._v(", "),a("code",[e._v("2018-02-17")])])])])]),e._v(" "),a("p",[e._v("dimensions x and time are aligned along the x-axis; y and band are aligned along the y-axis.")]),e._v(" "),a("p",[a("img",{attrs:{src:t(284),alt:"Data cube example"}})]),e._v(" "),a("p",[e._v("Data cubes as defined here have a "),a("em",[e._v("single value")]),e._v(" (scalar) for each\nunique combination of dimension values. The value pointed to by\narrows corresponds to the combination of x=288847.5 (red arrow),\ny=9120661 (yellow arrow), band=red (blue arrow), time=2018-02-17 (green arrow),\nand its value is 84 (brown arrow).")]),e._v(" "),a("p",[e._v("If the data concerns grayscale imagery, we could call this "),a("em",[e._v("single")]),e._v("\nvalue a "),a("em",[e._v("pixel value")]),e._v(". One should keep in mind that it is "),a("em",[e._v("never")]),e._v("\na tuple of, say, "),a("code",[e._v("{red, green, blue}")]),e._v(' values. "Cell value of a\nsingle raster layer" would be a better analogy; '),a("em",[e._v("data cube cell\nvalue")]),e._v(" may be a good compromise.")]),e._v(" "),a("p",[e._v("A data cube stores some additional properties per dimension such as:")]),e._v(" "),a("ul",[a("li",[e._v("name")]),e._v(" "),a("li",[e._v("axis / number")]),e._v(" "),a("li",[e._v("type")]),e._v(" "),a("li",[e._v("extents "),a("em",[e._v("or")]),e._v(" nominal dimension values")]),e._v(" "),a("li",[e._v("reference systems / projections")]),e._v(" "),a("li",[e._v("resolutions")])]),e._v(" "),a("p",[e._v("Having these properties available allows to easily resample from one data cube to another for example.")]),e._v(" "),a("h3",{attrs:{id:"apply-processes-that-do-not-change-dimensions"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#apply-processes-that-do-not-change-dimensions"}},[e._v("#")]),e._v(" "),a("code",[e._v("apply")]),e._v(": processes that do not change dimensions")]),e._v(" "),a("p",[e._v("Math process that does not reduce or change anything to the array\ndimensions. The process "),a("code",[e._v("apply")]),e._v(" can be used to apply unary functions\nsuch as "),a("code",[e._v("abs")]),e._v(" or "),a("code",[e._v("sqrt")]),e._v(" to all values in a data cube.")]),e._v(" "),a("p",[e._v("The process "),a("code",[e._v("apply_dimension")]),e._v(" applies (maps) an n-ary function to a particular\ndimension. An example along the time dimension is to apply a moving\naverage filter to implement temporal smoothing.\nAn example of "),a("code",[e._v("apply_dimension")]),e._v(" to the spatial dimensions\nis to do a historgram stretch for every spatial (grayscale) image\nof an image time series.")]),e._v(" "),a("h3",{attrs:{id:"filter-subsetting-dimensions-by-dimension-value-selection"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#filter-subsetting-dimensions-by-dimension-value-selection"}},[e._v("#")]),e._v(" "),a("code",[e._v("filter")]),e._v(": subsetting dimensions by dimension value selection")]),e._v(" "),a("p",[e._v("The "),a("code",[e._v("filter")]),e._v(" process makes a cube smaller by selecting specific\nvalue ranges for a particular dimension.")]),e._v(" "),a("p",[e._v("Examples:")]),e._v(" "),a("ul",[a("li",[e._v("a band filter that selects the "),a("code",[e._v("red")]),e._v(" band")]),e._v(" "),a("li",[e._v('a bounding box filter "crops" the collection to a spatial extent')])]),e._v(" "),a("h3",{attrs:{id:"reduce-removing-dimensions-entirely-by-computation"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#reduce-removing-dimensions-entirely-by-computation"}},[e._v("#")]),e._v(" "),a("code",[e._v("reduce")]),e._v(": removing dimensions entirely by computation")]),e._v(" "),a("p",[e._v("The "),a("code",[e._v("reduce")]),e._v(' process removes a dimension by "rolling up" or summarizing\nthe values along that dimension to a single value.\nFor example: eliminate the time dimension by taking the '),a("code",[e._v("mean")]),e._v(" along that dimension.\nAnother example is taking the "),a("code",[e._v("sum")]),e._v(" or "),a("code",[e._v("max")]),e._v(" along the band dimension.")]),e._v(" "),a("h3",{attrs:{id:"aggregate-reducing-resolution"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#aggregate-reducing-resolution"}},[e._v("#")]),e._v(" "),a("code",[e._v("aggregate")]),e._v(": reducing resolution")]),e._v(" "),a("p",[e._v("Aggregation computes new values from sets of values that are "),a("em",[e._v("uniquely")]),e._v(" assigned to groups. It involves a grouping predicate (e.g. monthly, 100 m x 100 m grid cells, or a set of non-overlapping spatial polygons), and an reducer (e.g., "),a("code",[e._v("mean")]),e._v(") that computes one or more new values from the original ones.")]),e._v(" "),a("p",[e._v("In effect, "),a("code",[e._v("aggregate")]),e._v(" combines the following three steps:")]),e._v(" "),a("ul",[a("li",[a("em",[e._v("split")]),e._v(" the data cube in groups, based on dimension constraints (time intervals, band groups, spatial polygons)")]),e._v(" "),a("li",[a("em",[e._v("apply")]),e._v(" a reducer to each group (similar to the "),a("code",[e._v("reduce")]),e._v(" process, but reducing a group rather than an entire dimension)")]),e._v(" "),a("li",[a("em",[e._v("combine")]),e._v(" the result to a new data cube, with some dimensions having reduced resolution (or e.g. raster to vector converted)")])]),e._v(" "),a("p",[e._v("Examples:")]),e._v(" "),a("ul",[a("li",[e._v("a weekly time series may be aggregated to monthly values by computing the "),a("code",[e._v("mean")]),e._v(" for all values in a month (grouping predicate: months)")]),e._v(" "),a("li",[a("em",[e._v("spatial")]),e._v(" aggregation involves computing e.g. "),a("em",[e._v("mean")]),e._v(" pixel values on a 100 x 100 m grid, from 10 m x 10 m pixels, where each original pixel is assigned uniquely to a larger pixel (grouping predicate: 100 m x 100 m grid cells)")])]),e._v(" "),a("h3",{attrs:{id:"resample-changing-data-cube-geometry"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#resample-changing-data-cube-geometry"}},[e._v("#")]),e._v(" "),a("code",[e._v("resample")]),e._v(": changing data cube geometry")]),e._v(" "),a("p",[e._v("Resampling considers the case where we have data at one resolution and coordinate reference system, and need values at another. In case we have values at a 100 m x 100 m grid and need values at a 10 m x 10 m grid, the original values will be reused many times, and may be simply assigned to the nearest high resolution grid cells (nearest neighbor method), or may be interpolated using various methods (e.g. by bilinear interpolation). This is often called "),a("em",[e._v("upsampling")]),e._v(" or "),a("em",[e._v("upscaling")]),e._v(".")]),e._v(" "),a("p",[e._v("Resampling from finer to coarser grid is a special case of aggregation often called "),a("em",[e._v("downsampling")]),e._v(" or "),a("em",[e._v("downscaling")]),e._v(".")]),e._v(" "),a("p",[e._v("When the target grid or time series has a lower resolution (larger grid cells) or lower frequency (longer time intervals) than the source grid, aggregation might be used for resampling. For example, if the resolutions are similar, (e.g. the source collection provides 10 day intervals and the target needs values for 16 day intervals), then some form of interpolation may be more appropriate than aggregation as defined here.")]),e._v(" "),a("h2",{attrs:{id:"user-defined-function-udf"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-function-udf"}},[e._v("#")]),e._v(" User-defined function (UDF)")]),e._v(" "),a("p",[e._v("The abbreviation "),a("strong",[e._v("UDF")]),e._v(" stands for "),a("strong",[e._v("user-defined function")]),e._v(". With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, or applied to a particular dimension or set of dimensions, allowing custom server-side calculations. See the section on "),a("RouterLink",{attrs:{to:"/documentation/0.4/udfs.html"}},[e._v("UDFs")]),e._v(" for more information.")],1),e._v(" "),a("h2",{attrs:{id:"data-processing-modes"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#data-processing-modes"}},[e._v("#")]),e._v(" Data Processing modes")]),e._v(" "),a("p",[e._v("Process graphs can be processed in three different ways:")]),e._v(" "),a("ol",[a("li",[e._v("Results can be pre-computed by creating a "),a("em",[a("strong",[e._v("batch job")])]),e._v(" using "),a("code",[e._v("POST /jobs")]),e._v(". They are submitted to the back-end's processing system, but will remain inactive until "),a("code",[e._v("POST /jobs/{job_id}/results")]),e._v(" has been called. They will run only once and store results after execution. Results can be downloaded. Batch jobs are typically time consuming and user interaction is not possible. This is the only mode that allows to get an estimate about time, volume and costs beforehand.")]),e._v(" "),a("li",[e._v("A more dynamic way of processing and accessing data is to create a "),a("strong",[e._v("secondary web service")]),e._v(". They allow web-based access using different protocols such as "),a("a",{attrs:{href:"http://www.opengeospatial.org/standards/wms",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WMS"),a("OutboundLink")],1),e._v(" (Open Geospatial Consortium Web Map Service), "),a("a",{attrs:{href:"http://www.opengeospatial.org/standards/wcs",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WCS"),a("OutboundLink")],1),e._v(" (Web Coverage Service) or "),a("a",{attrs:{href:"https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames",target:"_blank",rel:"noopener noreferrer"}},[e._v("XYZ tiles"),a("OutboundLink")],1),e._v(". These protocols usually allow users to change the viewing extent or level of detail (zoom level). Therefore, computations often run "),a("em",[e._v("on demand")]),e._v(" so that the requested data is calculated during the request. Back-ends should make sure to cache processed data to avoid additional/high costs and reduce waiting times for the user.")]),e._v(" "),a("li",[e._v("Process graphs can also be executed "),a("strong",[e._v("on-demand")]),e._v(" (i.e. synchronously) by sending the process graph to "),a("code",[e._v("POST /result")]),e._v(". Results are delivered with the request itself and no job is created. Only lightweight computations, for example small previews, should be executed using this approach as timeouts are to be expected for "),a("a",{attrs:{href:"https://www.pubnub.com/blog/2014-12-01-http-long-polling/",target:"_blank",rel:"noopener noreferrer"}},[e._v("long-polling HTTP requests"),a("OutboundLink")],1),e._v(".")])])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[42],{284:function(e,a,t){e.exports=t.p+"assets/img/datacube-example.bab870f7.png"},466:function(e,a,t){"use strict";t.r(a);var s=t(4),o=Object(s.a)({},(function(){var e=this,a=e._self._c;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h1",{attrs:{id:"glossary"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#glossary"}},[e._v("#")]),e._v(" Glossary")]),e._v(" "),a("p",[e._v("This glossary introduces the major technical terms used in the openEO project.")]),e._v(" "),a("h2",{attrs:{id:"general-terms"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#general-terms"}},[e._v("#")]),e._v(" General terms")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("EO")]),e._v(": Earth observation")]),e._v(" "),a("li",[a("strong",[e._v("API")]),e._v(": application programming interface ("),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Application_programming_interface",target:"_blank",rel:"noopener noreferrer"}},[e._v("wikipedia"),a("OutboundLink")],1),e._v("); a communication protocol between client and back-end")]),e._v(" "),a("li",[a("strong",[e._v("client")]),e._v(": software tool or environment that end-users directly interact with, e.g. R (RStudio), Python (Jupyter notebook), and JavaScript (web browser); R and Python are two major data science platforms; JavaScript is a major language for web development")]),e._v(" "),a("li",[a("strong",[e._v("(cloud) back-end")]),e._v(": server; computer infrastructure (one or more physical computers or virtual machines) used for storing EO data and processing it")]),e._v(" "),a("li",[a("strong",[e._v("big Earth observation cloud back-end")]),e._v(": server infrastructure where industry and researchers analyse large amounts of EO data")])]),e._v(" "),a("h2",{attrs:{id:"processes-and-process-graphs"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#processes-and-process-graphs"}},[e._v("#")]),e._v(" Processes and process graphs")]),e._v(" "),a("p",[e._v("The terms "),a("em",[e._v("process")]),e._v(" and "),a("em",[e._v("process graph")]),e._v(" have specific meanings in the openEO API specification.")]),e._v(" "),a("p",[e._v("A "),a("strong",[e._v("process")]),e._v(" is an operation provided by the back end that performs a specific task on a set of parameters and returns a result. An example is computing a statistical operation, such as mean or median, on selected EO data. A process is similar to a function or method in programming languages.")]),e._v(" "),a("p",[e._v("A "),a("strong",[e._v("process graph")]),e._v(" chains specific process calls together. Similarly to scripts in the context of programming, process graphs organize and automate the execution of one or more processes that could alternatively be executed individually. In a process graph, processes need to be specific, i.e. concrete values for input parameters need to be specified. These arguments can again be process graphs, scalar values, arrays or objects.")]),e._v(" "),a("h2",{attrs:{id:"eo-data-collections"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#eo-data-collections"}},[e._v("#")]),e._v(" EO data (Collections)")]),e._v(" "),a("p",[e._v("In our domain, different terms are used to describe EO data(sets). Within openEO, a "),a("strong",[e._v("granule")]),e._v(" (sometimes also called "),a("em",[e._v("item")]),e._v(" or "),a("em",[e._v("asset")]),e._v(" in the specification) typically refers to a limited area and a single overpass leading to a very short observation period (seconds) or a temporal aggregation of such data (e.g. for 16-day MODIS composites). A "),a("strong",[e._v("collection")]),e._v(" is a sequence of granules sharing the same product specification. It typically corresponds to the series of products derived from data acquired by a sensor on board a satellite and having the same mode of operation.")]),e._v(" "),a("p",[e._v("The "),a("a",{attrs:{href:"http://ceos.org/ourwork/workinggroups/wgiss/access/opensearch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CEOS OpenSearch Best Practice Document v1.2"),a("OutboundLink")],1),e._v(" lists the following synonyms used by other organizations:")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("granule")]),e._v(": dataset (ESA, ISO 19115), granule (NASA), product (ESA, CNES), scene (JAXA)")]),e._v(" "),a("li",[a("strong",[e._v("collection")]),e._v(": dataset series (ESA, ISO 19115), collection (CNES, NASA), dataset (JAXA), product (JAXA)")])]),e._v(" "),a("p",[e._v("In openEO, a back-end offers a set of collections to be processed. All collections can be requested using a client and are described using the "),a("a",{attrs:{href:"https://github.com/radiantearth/stac-spec",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC (SpatioTemporal Asset Catalog) metadata specification"),a("OutboundLink")],1),e._v(" as STAC collections. A user can load (a subset of) a collection using a special process, which returns a (spatial) data cube. All further processing is then applied to the data cube on the back-end.")]),e._v(" "),a("h2",{attrs:{id:"spatial-data-cubes"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#spatial-data-cubes"}},[e._v("#")]),e._v(" Spatial data cubes")]),e._v(" "),a("p",[e._v("A "),a("em",[e._v("spatiotemporal")]),e._v(" data cube is a multidimensional array with one or more spatial or temporal dimensions.\nIn the EO domain, it is common to be implicit about the temporal dimension and just refer to them as "),a("em",[e._v("spatial")]),e._v(" data cubes in short.\nSpecial cases are raster and vector data cubes.")]),e._v(" "),a("p",[e._v("The figure below shows the data of\na four-dimensional (8 x 8 x 2 x 2) raster data cube, with dimension names\nand values:")]),e._v(" "),a("table",[a("thead",[a("tr",[a("th",[e._v("#")]),e._v(" "),a("th",[e._v("dimension name")]),e._v(" "),a("th",[e._v("dimension values")])])]),e._v(" "),a("tbody",[a("tr",[a("td",[e._v("1")]),e._v(" "),a("td",[e._v("x")]),e._v(" "),a("td",[e._v("288790.5, 288819, 288847.5, 288876, 288904.5, 288933, 288961.5, 288990")])]),e._v(" "),a("tr",[a("td",[e._v("2")]),e._v(" "),a("td",[e._v("y")]),e._v(" "),a("td",[e._v("9120747, 9120718, 9120690, 9120661, 9120633, 9120604, 9120576, 9120547")])]),e._v(" "),a("tr",[a("td",[e._v("3")]),e._v(" "),a("td",[e._v("band")]),e._v(" "),a("td",[a("code",[e._v("red")]),e._v(", "),a("code",[e._v("green")])])]),e._v(" "),a("tr",[a("td",[e._v("4")]),e._v(" "),a("td",[e._v("time")]),e._v(" "),a("td",[a("code",[e._v("2018-02-10")]),e._v(", "),a("code",[e._v("2018-02-17")])])])])]),e._v(" "),a("p",[e._v("dimensions x and time are aligned along the x-axis; y and band are aligned along the y-axis.")]),e._v(" "),a("p",[a("img",{attrs:{src:t(284),alt:"Data cube example"}})]),e._v(" "),a("p",[e._v("Data cubes as defined here have a "),a("em",[e._v("single value")]),e._v(" (scalar) for each\nunique combination of dimension values. The value pointed to by\narrows corresponds to the combination of x=288847.5 (red arrow),\ny=9120661 (yellow arrow), band=red (blue arrow), time=2018-02-17 (green arrow),\nand its value is 84 (brown arrow).")]),e._v(" "),a("p",[e._v("If the data concerns grayscale imagery, we could call this "),a("em",[e._v("single")]),e._v("\nvalue a "),a("em",[e._v("pixel value")]),e._v(". One should keep in mind that it is "),a("em",[e._v("never")]),e._v("\na tuple of, say, "),a("code",[e._v("{red, green, blue}")]),e._v(' values. "Cell value of a\nsingle raster layer" would be a better analogy; '),a("em",[e._v("data cube cell\nvalue")]),e._v(" may be a good compromise.")]),e._v(" "),a("p",[e._v("A data cube stores some additional properties per dimension such as:")]),e._v(" "),a("ul",[a("li",[e._v("name")]),e._v(" "),a("li",[e._v("axis / number")]),e._v(" "),a("li",[e._v("type")]),e._v(" "),a("li",[e._v("extents "),a("em",[e._v("or")]),e._v(" nominal dimension values")]),e._v(" "),a("li",[e._v("reference systems / projections")]),e._v(" "),a("li",[e._v("resolutions")])]),e._v(" "),a("p",[e._v("Having these properties available allows to easily resample from one data cube to another for example.")]),e._v(" "),a("h3",{attrs:{id:"apply-processes-that-do-not-change-dimensions"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#apply-processes-that-do-not-change-dimensions"}},[e._v("#")]),e._v(" "),a("code",[e._v("apply")]),e._v(": processes that do not change dimensions")]),e._v(" "),a("p",[e._v("Math process that does not reduce or change anything to the array\ndimensions. The process "),a("code",[e._v("apply")]),e._v(" can be used to apply unary functions\nsuch as "),a("code",[e._v("abs")]),e._v(" or "),a("code",[e._v("sqrt")]),e._v(" to all values in a data cube.")]),e._v(" "),a("p",[e._v("The process "),a("code",[e._v("apply_dimension")]),e._v(" applies (maps) an n-ary function to a particular\ndimension. An example along the time dimension is to apply a moving\naverage filter to implement temporal smoothing.\nAn example of "),a("code",[e._v("apply_dimension")]),e._v(" to the spatial dimensions\nis to do a historgram stretch for every spatial (grayscale) image\nof an image time series.")]),e._v(" "),a("h3",{attrs:{id:"filter-subsetting-dimensions-by-dimension-value-selection"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#filter-subsetting-dimensions-by-dimension-value-selection"}},[e._v("#")]),e._v(" "),a("code",[e._v("filter")]),e._v(": subsetting dimensions by dimension value selection")]),e._v(" "),a("p",[e._v("The "),a("code",[e._v("filter")]),e._v(" process makes a cube smaller by selecting specific\nvalue ranges for a particular dimension.")]),e._v(" "),a("p",[e._v("Examples:")]),e._v(" "),a("ul",[a("li",[e._v("a band filter that selects the "),a("code",[e._v("red")]),e._v(" band")]),e._v(" "),a("li",[e._v('a bounding box filter "crops" the collection to a spatial extent')])]),e._v(" "),a("h3",{attrs:{id:"reduce-removing-dimensions-entirely-by-computation"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#reduce-removing-dimensions-entirely-by-computation"}},[e._v("#")]),e._v(" "),a("code",[e._v("reduce")]),e._v(": removing dimensions entirely by computation")]),e._v(" "),a("p",[e._v("The "),a("code",[e._v("reduce")]),e._v(' process removes a dimension by "rolling up" or summarizing\nthe values along that dimension to a single value.\nFor example: eliminate the time dimension by taking the '),a("code",[e._v("mean")]),e._v(" along that dimension.\nAnother example is taking the "),a("code",[e._v("sum")]),e._v(" or "),a("code",[e._v("max")]),e._v(" along the band dimension.")]),e._v(" "),a("h3",{attrs:{id:"aggregate-reducing-resolution"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#aggregate-reducing-resolution"}},[e._v("#")]),e._v(" "),a("code",[e._v("aggregate")]),e._v(": reducing resolution")]),e._v(" "),a("p",[e._v("Aggregation computes new values from sets of values that are "),a("em",[e._v("uniquely")]),e._v(" assigned to groups. It involves a grouping predicate (e.g. monthly, 100 m x 100 m grid cells, or a set of non-overlapping spatial polygons), and an reducer (e.g., "),a("code",[e._v("mean")]),e._v(") that computes one or more new values from the original ones.")]),e._v(" "),a("p",[e._v("In effect, "),a("code",[e._v("aggregate")]),e._v(" combines the following three steps:")]),e._v(" "),a("ul",[a("li",[a("em",[e._v("split")]),e._v(" the data cube in groups, based on dimension constraints (time intervals, band groups, spatial polygons)")]),e._v(" "),a("li",[a("em",[e._v("apply")]),e._v(" a reducer to each group (similar to the "),a("code",[e._v("reduce")]),e._v(" process, but reducing a group rather than an entire dimension)")]),e._v(" "),a("li",[a("em",[e._v("combine")]),e._v(" the result to a new data cube, with some dimensions having reduced resolution (or e.g. raster to vector converted)")])]),e._v(" "),a("p",[e._v("Examples:")]),e._v(" "),a("ul",[a("li",[e._v("a weekly time series may be aggregated to monthly values by computing the "),a("code",[e._v("mean")]),e._v(" for all values in a month (grouping predicate: months)")]),e._v(" "),a("li",[a("em",[e._v("spatial")]),e._v(" aggregation involves computing e.g. "),a("em",[e._v("mean")]),e._v(" pixel values on a 100 x 100 m grid, from 10 m x 10 m pixels, where each original pixel is assigned uniquely to a larger pixel (grouping predicate: 100 m x 100 m grid cells)")])]),e._v(" "),a("h3",{attrs:{id:"resample-changing-data-cube-geometry"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#resample-changing-data-cube-geometry"}},[e._v("#")]),e._v(" "),a("code",[e._v("resample")]),e._v(": changing data cube geometry")]),e._v(" "),a("p",[e._v("Resampling considers the case where we have data at one resolution and coordinate reference system, and need values at another. In case we have values at a 100 m x 100 m grid and need values at a 10 m x 10 m grid, the original values will be reused many times, and may be simply assigned to the nearest high resolution grid cells (nearest neighbor method), or may be interpolated using various methods (e.g. by bilinear interpolation). This is often called "),a("em",[e._v("upsampling")]),e._v(" or "),a("em",[e._v("upscaling")]),e._v(".")]),e._v(" "),a("p",[e._v("Resampling from finer to coarser grid is a special case of aggregation often called "),a("em",[e._v("downsampling")]),e._v(" or "),a("em",[e._v("downscaling")]),e._v(".")]),e._v(" "),a("p",[e._v("When the target grid or time series has a lower resolution (larger grid cells) or lower frequency (longer time intervals) than the source grid, aggregation might be used for resampling. For example, if the resolutions are similar, (e.g. the source collection provides 10 day intervals and the target needs values for 16 day intervals), then some form of interpolation may be more appropriate than aggregation as defined here.")]),e._v(" "),a("h2",{attrs:{id:"user-defined-function-udf"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-function-udf"}},[e._v("#")]),e._v(" User-defined function (UDF)")]),e._v(" "),a("p",[e._v("The abbreviation "),a("strong",[e._v("UDF")]),e._v(" stands for "),a("strong",[e._v("user-defined function")]),e._v(". With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, or applied to a particular dimension or set of dimensions, allowing custom server-side calculations. See the section on "),a("RouterLink",{attrs:{to:"/documentation/0.4/udfs.html"}},[e._v("UDFs")]),e._v(" for more information.")],1),e._v(" "),a("h2",{attrs:{id:"data-processing-modes"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#data-processing-modes"}},[e._v("#")]),e._v(" Data Processing modes")]),e._v(" "),a("p",[e._v("Process graphs can be processed in three different ways:")]),e._v(" "),a("ol",[a("li",[e._v("Results can be pre-computed by creating a "),a("em",[a("strong",[e._v("batch job")])]),e._v(" using "),a("code",[e._v("POST /jobs")]),e._v(". They are submitted to the back-end's processing system, but will remain inactive until "),a("code",[e._v("POST /jobs/{job_id}/results")]),e._v(" has been called. They will run only once and store results after execution. Results can be downloaded. Batch jobs are typically time consuming and user interaction is not possible. This is the only mode that allows to get an estimate about time, volume and costs beforehand.")]),e._v(" "),a("li",[e._v("A more dynamic way of processing and accessing data is to create a "),a("strong",[e._v("secondary web service")]),e._v(". They allow web-based access using different protocols such as "),a("a",{attrs:{href:"http://www.opengeospatial.org/standards/wms",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WMS"),a("OutboundLink")],1),e._v(" (Open Geospatial Consortium Web Map Service), "),a("a",{attrs:{href:"http://www.opengeospatial.org/standards/wcs",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WCS"),a("OutboundLink")],1),e._v(" (Web Coverage Service) or "),a("a",{attrs:{href:"https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames",target:"_blank",rel:"noopener noreferrer"}},[e._v("XYZ tiles"),a("OutboundLink")],1),e._v(". These protocols usually allow users to change the viewing extent or level of detail (zoom level). Therefore, computations often run "),a("em",[e._v("on demand")]),e._v(" so that the requested data is calculated during the request. Back-ends should make sure to cache processed data to avoid additional/high costs and reduce waiting times for the user.")]),e._v(" "),a("li",[e._v("Process graphs can also be executed "),a("strong",[e._v("on-demand")]),e._v(" (i.e. synchronously) by sending the process graph to "),a("code",[e._v("POST /result")]),e._v(". Results are delivered with the request itself and no job is created. Only lightweight computations, for example small previews, should be executed using this approach as timeouts are to be expected for "),a("a",{attrs:{href:"https://www.pubnub.com/blog/2014-12-01-http-long-polling/",target:"_blank",rel:"noopener noreferrer"}},[e._v("long-polling HTTP requests"),a("OutboundLink")],1),e._v(".")])])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/43.5b081dec.js b/assets/js/43.bc3c8de3.js similarity index 98% rename from assets/js/43.5b081dec.js rename to assets/js/43.bc3c8de3.js index 37804bee5..5512b6ce5 100644 --- a/assets/js/43.5b081dec.js +++ b/assets/js/43.bc3c8de3.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[43],{304:function(e,t,a){e.exports=a.p+"assets/img/arch.e75ccb9a.png"},477:function(e,t,a){"use strict";a.r(t);var i=a(4),n=Object(i.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"architecture"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#architecture"}},[e._v("#")]),e._v(" Architecture")]),e._v(" "),t("p",[e._v("The openEO API defines a language for how clients communicate to back-ends in order to analyze large Earth observation datasets. The API will be implemented by drivers for specific back-ends. Some first architecture considerations are listed below.")]),e._v(" "),t("ol",[t("li",[e._v("The openEO API is a contract between clients and back-ends that describes the communication only")]),e._v(" "),t("li",[e._v("Each back-end runs its own API instance including the specific back-end driver. There is no API instance that runs more than one driver.")]),e._v(" "),t("li",[e._v("Clients in R, Python, and JavaScript connect directly to the back-ends and communicate with the back-ends over "),t("em",[e._v("HTTPS")]),e._v(" according to the openEO API specification.")]),e._v(" "),t("li",[e._v("API instances can run on back-end servers or additional intermediate layers, which then communicate to back-ends in a back-end specific way.")]),e._v(" "),t("li",[e._v("Back-ends may add functionality and extend the API wherever there is need.")]),e._v(" "),t("li",[e._v("There will be a central back-end registry service (openEO Hub), to allow users to search for back-ends with specific functionality and or data.")]),e._v(" "),t("li",[e._v("The openEO API may define "),t("em",[e._v("profiles")]),e._v(" in order to group specific functionality.")])]),e._v(" "),t("p",[t("img",{attrs:{src:a(304),alt:"Architecture - openEO API shown in dark blue"}})]),e._v(" "),t("h1",{attrs:{id:"microservices"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#microservices"}},[e._v("#")]),e._v(" Microservices")]),e._v(" "),t("p",[e._v("To simplify and structure the development, the API is divided into a few microservices.")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("Microservice")]),e._v(" "),t("th",[e._v("Description")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[e._v("Capabilities")]),e._v(" "),t("td",[e._v("This microservice reports on the capabilities of the back-end, i.e. which API endpoints are implemented, which authentication methods are supported, and whether and how UDFs can be executed at the back-end.")])]),e._v(" "),t("tr",[t("td",[e._v("EO Data Discovery")]),e._v(" "),t("td",[e._v("Describes which collections are available at the back-end.")])]),e._v(" "),t("tr",[t("td",[e._v("Process Discovery")]),e._v(" "),t("td",[e._v("Provides services to find out which processes a back-end provides, i.e., what users can do with the available data.")])]),e._v(" "),t("tr",[t("td",[e._v("Data Processing")]),e._v(" "),t("td",[e._v("Organizes and manages data processing on the back-end, either as synchronous on-demand computation or batch jobs.")])]),e._v(" "),t("tr",[t("td",[e._v("File Storage")]),e._v(" "),t("td",[e._v("Organizes and manages user-uploaded files.")])]),e._v(" "),t("tr",[t("td",[e._v("User-Defined Processes")]),e._v(" "),t("td",[e._v("Organizes and manages user-defined processes (process graphs).")])]),e._v(" "),t("tr",[t("td",[e._v("Secondary Services")]),e._v(" "),t("td",[e._v("External web services to access data and job results such as a OGC WMTS service.")])]),e._v(" "),t("tr",[t("td",[e._v("Account Management")]),e._v(" "),t("td",[e._v("User management, accounting and authentication.")])]),e._v(" "),t("tr",[t("td",[e._v("UDF Runtime")]),e._v(" "),t("td",[e._v("Execution of user-defined functions, not part of the Core API. See the "),t("RouterLink",{attrs:{to:"/documentation/1.0/udfs.html"}},[e._v("UDF documentation")]),e._v(" for more information.")],1)])])])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[43],{304:function(e,t,a){e.exports=a.p+"assets/img/arch.e75ccb9a.png"},476:function(e,t,a){"use strict";a.r(t);var i=a(4),n=Object(i.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"architecture"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#architecture"}},[e._v("#")]),e._v(" Architecture")]),e._v(" "),t("p",[e._v("The openEO API defines a language for how clients communicate to back-ends in order to analyze large Earth observation datasets. The API will be implemented by drivers for specific back-ends. Some first architecture considerations are listed below.")]),e._v(" "),t("ol",[t("li",[e._v("The openEO API is a contract between clients and back-ends that describes the communication only")]),e._v(" "),t("li",[e._v("Each back-end runs its own API instance including the specific back-end driver. 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"),e("td",[e("code",[t._v("GET /jobs/{id}")]),t._v(" > status")]),t._v(" "),e("td",[t._v("Correctly reports status (and progress, if supported)")])]),t._v(" "),e("tr",[e("td",[t._v("780")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("781")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}")])]),t._v(" "),e("td",[t._v("Deletes the job and returns a HTTP 204 on success, otherwise a 4XX.")])])])]),t._v(" "),e("h4",{attrs:{id:"batch-jobs-results"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs-results"}},[t._v("#")]),t._v(" Batch Jobs > Results")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("850")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Supports queueing/starting jobs 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"),e("tbody",[e("tr",[e("td",[t._v("20")]),t._v(" "),e("td",[e("code",[t._v("GET /.well-known/openeo")])]),t._v(" "),e("td",[t._v("Not part of the main API tree / not versioned")])])])]),t._v(" "),e("h4",{attrs:{id:"capabilities-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#capabilities-2"}},[t._v("#")]),t._v(" Capabilities")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("35")]),t._v(" "),e("td",[e("code",[t._v("GET /")]),t._v(" > billing")]),t._v(" "),e("td",[t._v("Billing information are provided")])])])]),t._v(" "),e("h4",{attrs:{id:"file-formats-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#file-formats-2"}},[t._v("#")]),t._v(" File Formats")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("44")]),t._v(" "),e("td",[e("code",[t._v("GET /file_formats")])]),t._v(" "),e("td",[t._v("File format names aligned with GDAL")])]),t._v(" "),e("tr",[e("td",[t._v("47")]),t._v(" "),e("td",[e("code",[t._v("GET /file_formats")])]),t._v(" "),e("td",[t._v("Each format has a description describing their structure and relation to data cubes.")])]),t._v(" "),e("tr",[e("td",[t._v("49")]),t._v(" "),e("td",[e("code",[t._v("GET /file_formats")])]),t._v(" "),e("td",[t._v("title and description are provided for all file formats")])])])]),t._v(" "),e("h4",{attrs:{id:"authentication-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication-2"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("112")]),t._v(" "),e("td",[e("code",[t._v("GET /credentials/oidc")])]),t._v(" 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"),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("206")]),t._v(" "),e("td",[e("code",[t._v("GET /processes")]),t._v(" > processes")]),t._v(" "),e("td",[t._v("All processes are customized to reflect the actual implementation status (removal of unsupported parameters, updates to parameter schemas, etc.)")])]),t._v(" "),e("tr",[e("td",[t._v("207")]),t._v(" "),e("td",[e("code",[t._v("GET /processes")]),t._v(" > processes")]),t._v(" "),e("td",[t._v("Summaries and categories are provided for all processes")])])])]),t._v(" "),e("h4",{attrs:{id:"collections-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#collections-2"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("304")]),t._v(" "),e("td",[e("code",[t._v("GET /collections")])]),t._v(" "),e("td",[t._v("Implements STAC API - Collections (https://api.stacspec.org/v1.0.0/collections)")])]),t._v(" "),e("tr",[e("td",[t._v("308")]),t._v(" "),e("td",[e("code",[t._v("GET /collections")]),t._v(" > collections")]),t._v(" "),e("td",[t._v("License links are provided for non-SPDX license identifiers")])]),t._v(" "),e("tr",[e("td",[t._v("309")]),t._v(" "),e("td",[e("code",[t._v("GET /collections")]),t._v(" > collections")]),t._v(" "),e("td",[t._v("All collections provide a title and keywords")])]),t._v(" "),e("tr",[e("td",[t._v("310")]),t._v(" "),e("td",[e("code",[t._v("GET /collections")]),t._v(" > collections")]),t._v(" "),e("td",[t._v("Extents report the actual availability of data and don't overpromise")])]),t._v(" "),e("tr",[e("td",[t._v("325")]),t._v(" "),e("td",[e("code",[t._v("GET /collections/{id}")]),t._v(" > stac_extensions")]),t._v(" "),e("td",[t._v("Lists all extensions that are used in the document")])])])]),t._v(" "),e("h4",{attrs:{id:"user-defined-processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-processes"}},[t._v("#")]),t._v(" User-defined processes")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("400")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("401")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs")])]),t._v(" "),e("td",[t._v("Valid response with at least processes and links as arrays")])]),t._v(" "),e("tr",[e("td",[t._v("403")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs")]),t._v(" > limit parameter")]),t._v(" "),e("td",[t._v("All processes are returned if no limit parameter is provided")])]),t._v(" "),e("tr",[e("td",[t._v("404")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs")]),t._v(" > processes")]),t._v(" "),e("td",[t._v("All stored processes are valid according to the specification (id is required)")])]),t._v(" "),e("tr",[e("td",[t._v("405")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs")]),t._v(" > processes")]),t._v(" "),e("td",[t._v("Missing properties in the process objects are not set to null if missing (allowed only for: summary, description, parameters, returns)")])]),t._v(" "),e("tr",[e("td",[t._v("410")]),t._v(" "),e("td",[e("code",[t._v("PUT /process_graphs/{id}")]),t._v(" > create")]),t._v(" "),e("td",[t._v("Supports creating new processes (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("411")]),t._v(" "),e("td",[e("code",[t._v("PUT /process_graphs/{id}")]),t._v(" > update")]),t._v(" "),e("td",[t._v("Supports updating existing processes (required only if creating new processes is supported)")])]),t._v(" "),e("tr",[e("td",[t._v("412")]),t._v(" "),e("td",[e("code",[t._v("PUT /process_graphs/{id}")])]),t._v(" "),e("td",[t._v("Only accepts valid process submissions (id and process_graph required, given process must be valid)")])]),t._v(" "),e("tr",[e("td",[t._v("413")]),t._v(" "),e("td",[e("code",[t._v("PUT /process_graphs/{id}")]),t._v(" > id")]),t._v(" "),e("td",[t._v("If the id given in the request body is not equal to the given process_graph_id in the path, updates the id given in the request body.")])]),t._v(" "),e("tr",[e("td",[t._v("414")]),t._v(" "),e("td",[e("code",[t._v("PUT /process_graphs/{id}")])]),t._v(" "),e("td",[t._v("Fully replaces the process if a user-defined process with the given ID exists.")])]),t._v(" "),e("tr",[e("td",[t._v("420")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs/{id}")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("421")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs/{id}")])]),t._v(" "),e("td",[t._v("Valid response with at least id and 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"),e("tbody",[e("tr",[e("td",[t._v("702")]),t._v(" "),e("td"),t._v(" "),e("td",[t._v("At least two of batch jobs, secondary web services or synchronous processing are implemented")])])])]),t._v(" "),e("h4",{attrs:{id:"batch-jobs-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs-2"}},[t._v("#")]),t._v(" Batch Jobs")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("710")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("711")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs")])]),t._v(" "),e("td",[t._v("Valid response with at least jobs and links as arrays")])]),t._v(" "),e("tr",[e("td",[t._v("713")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs")]),t._v(" > limit parameter")]),t._v(" "),e("td",[t._v("All jobs are returned if no limit parameter is provided")])]),t._v(" "),e("tr",[e("td",[t._v("714")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs")]),t._v(" > jobs")]),t._v(" "),e("td",[t._v("The served job metadata is valid according to the specification (id, status and created are required)")])]),t._v(" "),e("tr",[e("td",[t._v("715")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs")]),t._v(" > jobs")]),t._v(" "),e("td",[t._v("title is provided for all jobs (if provided by the user)")])]),t._v(" "),e("tr",[e("td",[t._v("730")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs")])]),t._v(" "),e("td",[t._v("Supports creating batch jobs (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("731")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs")]),t._v(" > process")]),t._v(" "),e("td",[t._v("Only accepts valid process submissions")])]),t._v(" "),e("tr",[e("td",[t._v("734")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs")])]),t._v(" "),e("td",[t._v("Supports storing title and description")])]),t._v(" "),e("tr",[e("td",[t._v("736")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs")])]),t._v(" "),e("td",[t._v("Returns HTTP status 201 and OpenEO-Identifier + Location header if successful")])]),t._v(" "),e("tr",[e("td",[t._v("737")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs")]),t._v(" > status")]),t._v(" "),e("td",[t._v('Job status is "created" after successful creation of the job, the data processing does not start yet')])]),t._v(" "),e("tr",[e("td",[t._v("750")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("751")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}")])]),t._v(" "),e("td",[t._v("Returns at least id, process, status and created")])]),t._v(" "),e("tr",[e("td",[t._v("752")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}")])]),t._v(" "),e("td",[t._v("title and description are available if provided by the user")])]),t._v(" "),e("tr",[e("td",[t._v("754")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}")]),t._v(" > status")]),t._v(" "),e("td",[t._v("Correctly reports status (and progress, if supported)")])]),t._v(" "),e("tr",[e("td",[t._v("780")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("781")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}")])]),t._v(" "),e("td",[t._v("Deletes the job and returns a HTTP 204 on success, otherwise a 4XX.")])])])]),t._v(" "),e("h4",{attrs:{id:"batch-jobs-logs"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs-logs"}},[t._v("#")]),t._v(" Batch Jobs > Logs")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("800")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("801")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > offset parameter")]),t._v(" "),e("td",[t._v("Supports query parameter offset, only returns log entries that were logged after the given offset")])]),t._v(" "),e("tr",[e("td",[t._v("804")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")])]),t._v(" "),e("td",[t._v("Valid response with at least logs and links as arrays")])]),t._v(" "),e("tr",[e("td",[t._v("805")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > level")]),t._v(" "),e("td",[t._v("level property is available if log_level has been set for the job or the level parameter has been provided")])]),t._v(" "),e("tr",[e("td",[t._v("806")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > logs")]),t._v(" "),e("td",[t._v("The served log entries are valid according to the specification (id, level and message are required)")])]),t._v(" "),e("tr",[e("td",[t._v("807")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > logs")]),t._v(" "),e("td",[t._v("time is supported, core and data is supported if applicable")])]),t._v(" "),e("tr",[e("td",[t._v("808")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > logs")]),t._v(" "),e("td",[t._v("The message is short and concise, doesn't contain full stack traces.")])])])]),t._v(" "),e("h4",{attrs:{id:"batch-jobs-results-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs-results-2"}},[t._v("#")]),t._v(" Batch Jobs > Results")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("850")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Supports queueing/starting jobs (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("851")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Queues or starts the processing, which is reflected in the job status. Does nothing if already queued or running. Responds with HTTP status 202.")])]),t._v(" "),e("tr",[e("td",[t._v("852")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Rejects queueing a job if a payment is required.")])]),t._v(" "),e("tr",[e("td",[t._v("860")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("861")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")])]),t._v(" "),e("td",[t._v('Returns HTTP status code 200 if the result is available, HTTP status code 424 with error message if the job status is "error", 4XX/5XX otherwise.')])]),t._v(" "),e("tr",[e("td",[t._v("863")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > partial parameter")]),t._v(" "),e("td",[t._v("If processing has not finished and the partial parameter is not set to true, requests are rejected with openEO error JobNotFinished.")])]),t._v(" "),e("tr",[e("td",[t._v("865")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Response is a valid STAC Item or Collection")])]),t._v(" "),e("tr",[e("td",[t._v("866")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Supports a valid STAC version (0.9.0 or later, preferrably 1.0.0).")])]),t._v(" "),e("tr",[e("td",[t._v("869")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Assets all have an href and type, ideally also a title")])]),t._v(" "),e("tr",[e("td",[t._v("870")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Additional metadata is provided for the results (e.g. extents, projection, bands, dimensions). Please provide details in the comments.")])]),t._v(" "),e("tr",[e("td",[t._v("871")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > public access link")]),t._v(" "),e("td",[t._v("Support canonical links (a public access signed URL, exposed as link with relation type canonical)")])]),t._v(" "),e("tr",[e("td",[t._v("872")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > public access link")]),t._v(" "),e("td",[t._v("Exposes the expires property in the response body")])]),t._v(" "),e("tr",[e("td",[t._v("890")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("891")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Cancels data processing (if queued or running), optionally deletes the batch job results and returns a HTTP 202 on success.")])])])]),t._v(" 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"),e("td",[e("code",[t._v("POST /result")]),t._v(" > Content-Type header")]),t._v(" "),e("td",[t._v("Returns suitable media type for the requested file format (not always application/octet-stream) in the Content-Type header")])]),t._v(" "),e("tr",[e("td",[t._v("910")]),t._v(" "),e("td",[e("code",[t._v("POST /result")])]),t._v(" "),e("td",[t._v("Rejects processing if a payment is required.")])])])]),t._v(" "),e("h3",{attrs:{id:"l3-advanced"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#l3-advanced"}},[t._v("#")]),t._v(" L3: Advanced")]),t._v(" "),e("h4",{attrs:{id:"api-fundamentals-3"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#api-fundamentals-3"}},[t._v("#")]),t._v(" API fundamentals")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("8")]),t._v(" "),e("td",[t._v("All > Error Handling")]),t._v(" "),e("td",[t._v("Additionally returns 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"),e("tr",[e("td",[t._v("46")]),t._v(" "),e("td",[e("code",[t._v("GET /file_formats")])]),t._v(" "),e("td",[t._v("File format names get accepted case Insensitive")])]),t._v(" "),e("tr",[e("td",[t._v("48")]),t._v(" "),e("td",[e("code",[t._v("GET /file_formats")])]),t._v(" "),e("td",[t._v("Each format has at least gis_data_types and parameters")])])])]),t._v(" "),e("h4",{attrs:{id:"authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("100")]),t._v(" "),e("td"),t._v(" "),e("td",[t._v("Bearer token follows the specified format (method/identityProviderId/token)")])]),t._v(" "),e("tr",[e("td",[t._v("101")]),t._v(" "),e("td",[t._v("All that require a Bearer token > Error Handling")]),t._v(" "),e("td",[t._v("Returns HTTP status codes 401 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Responds with HTTP status 202.")])]),t._v(" "),e("tr",[e("td",[t._v("852")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Rejects queueing a job if a payment is required.")])]),t._v(" "),e("tr",[e("td",[t._v("860")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("861")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")])]),t._v(" "),e("td",[t._v('Returns HTTP status code 200 if the result is available, HTTP status code 424 with error message if the job status is "error", 4XX/5XX otherwise.')])]),t._v(" "),e("tr",[e("td",[t._v("863")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > partial parameter")]),t._v(" "),e("td",[t._v("If processing has not finished and the partial parameter is not set to true, requests are rejected with openEO error JobNotFinished.")])]),t._v(" "),e("tr",[e("td",[t._v("865")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Response is a valid STAC Item or Collection")])]),t._v(" "),e("tr",[e("td",[t._v("866")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Supports a valid STAC version (0.9.0 or later, preferrably 1.0.0).")])]),t._v(" "),e("tr",[e("td",[t._v("869")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Assets all have an href and type, ideally also a title")])]),t._v(" "),e("tr",[e("td",[t._v("870")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Additional metadata is provided for the results (e.g. extents, projection, bands, dimensions). Please provide details in the comments.")])]),t._v(" "),e("tr",[e("td",[t._v("871")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > public access link")]),t._v(" "),e("td",[t._v("Support canonical links (a public access signed URL, exposed as link with relation type canonical)")])]),t._v(" "),e("tr",[e("td",[t._v("872")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > public access link")]),t._v(" "),e("td",[t._v("Exposes the expires property in the response body")])]),t._v(" "),e("tr",[e("td",[t._v("890")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("891")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Cancels data processing (if queued or running), optionally deletes the batch job results and returns a HTTP 202 on success.")])])])]),t._v(" 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with authentication")])]),t._v(" "),e("tr",[e("td",[t._v("603")]),t._v(" "),e("td",[e("code",[t._v("GET /service_types")])]),t._v(" "),e("td",[t._v("Valid response with at least configuration and process_parameters per service")])])])]),t._v(" "),e("h4",{attrs:{id:"secondary-web-services"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#secondary-web-services"}},[t._v("#")]),t._v(" Secondary Web Services")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("610")]),t._v(" "),e("td",[e("code",[t._v("GET /services")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("611")]),t._v(" "),e("td",[e("code",[t._v("GET /services")])]),t._v(" "),e("td",[t._v("Valid response with at least services and links as arrays")])]),t._v(" "),e("tr",[e("td",[t._v("613")]),t._v(" "),e("td",[e("code",[t._v("GET 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"),e("td",[e("code",[t._v("POST /services")]),t._v(" > configuration")]),t._v(" "),e("td",[t._v("Supports configuration parameters, rejects invalid configuration parameters")])]),t._v(" "),e("tr",[e("td",[t._v("633")]),t._v(" "),e("td",[e("code",[t._v("POST /services")]),t._v(" > type")]),t._v(" "),e("td",[t._v("Rejects invalid types, accepts types case insensitive")])]),t._v(" "),e("tr",[e("td",[t._v("636")]),t._v(" "),e("td",[e("code",[t._v("POST /services")])]),t._v(" "),e("td",[t._v("Supports storing title, description and enabled")])]),t._v(" "),e("tr",[e("td",[t._v("638")]),t._v(" "),e("td",[e("code",[t._v("POST /services")])]),t._v(" "),e("td",[t._v("Returns HTTP status 201 and OpenEO-Identifier + Location header if successful")])]),t._v(" "),e("tr",[e("td",[t._v("650")]),t._v(" "),e("td",[e("code",[t._v("GET /services/{id}")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("651")]),t._v(" "),e("td",[e("code",[t._v("GET 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"),e("tbody",[e("tr",[e("td",[t._v("20")]),t._v(" "),e("td",[e("code",[t._v("GET /.well-known/openeo")])]),t._v(" "),e("td",[t._v("Not part of the main API tree / not versioned")])])])]),t._v(" "),e("h4",{attrs:{id:"capabilities-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#capabilities-2"}},[t._v("#")]),t._v(" Capabilities")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("35")]),t._v(" "),e("td",[e("code",[t._v("GET /")]),t._v(" > billing")]),t._v(" "),e("td",[t._v("Billing information are provided")])])])]),t._v(" "),e("h4",{attrs:{id:"file-formats-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#file-formats-2"}},[t._v("#")]),t._v(" File Formats")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("44")]),t._v(" "),e("td",[e("code",[t._v("GET /file_formats")])]),t._v(" "),e("td",[t._v("File format names aligned with GDAL")])]),t._v(" "),e("tr",[e("td",[t._v("47")]),t._v(" "),e("td",[e("code",[t._v("GET /file_formats")])]),t._v(" "),e("td",[t._v("Each format has a description describing their structure and relation to data cubes.")])]),t._v(" "),e("tr",[e("td",[t._v("49")]),t._v(" "),e("td",[e("code",[t._v("GET /file_formats")])]),t._v(" "),e("td",[t._v("title and description are provided for all file formats")])])])]),t._v(" "),e("h4",{attrs:{id:"authentication-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication-2"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("112")]),t._v(" "),e("td",[e("code",[t._v("GET /credentials/oidc")])]),t._v(" "),e("td",[t._v("OIDC is supported (without authentication for discovery requests)")])])])]),t._v(" "),e("h4",{attrs:{id:"user-account"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#user-account"}},[t._v("#")]),t._v(" User Account")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("120")]),t._v(" "),e("td",[e("code",[t._v("GET /me")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("121")]),t._v(" "),e("td",[e("code",[t._v("GET /me")]),t._v(" > user_id")]),t._v(" "),e("td",[t._v("Returns a unique user ID")])])])]),t._v(" "),e("h4",{attrs:{id:"pre-defined-processes-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#pre-defined-processes-2"}},[t._v("#")]),t._v(" Pre-defined Processes")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("206")]),t._v(" "),e("td",[e("code",[t._v("GET /processes")]),t._v(" > processes")]),t._v(" "),e("td",[t._v("All processes are customized to reflect the actual implementation status (removal of unsupported parameters, updates to parameter schemas, etc.)")])]),t._v(" "),e("tr",[e("td",[t._v("207")]),t._v(" "),e("td",[e("code",[t._v("GET /processes")]),t._v(" > processes")]),t._v(" "),e("td",[t._v("Summaries and categories are provided for all processes")])])])]),t._v(" "),e("h4",{attrs:{id:"collections-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#collections-2"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("304")]),t._v(" "),e("td",[e("code",[t._v("GET /collections")])]),t._v(" "),e("td",[t._v("Implements STAC API - Collections (https://api.stacspec.org/v1.0.0/collections)")])]),t._v(" "),e("tr",[e("td",[t._v("308")]),t._v(" "),e("td",[e("code",[t._v("GET /collections")]),t._v(" > collections")]),t._v(" "),e("td",[t._v("License links are provided for non-SPDX license identifiers")])]),t._v(" "),e("tr",[e("td",[t._v("309")]),t._v(" "),e("td",[e("code",[t._v("GET /collections")]),t._v(" > collections")]),t._v(" "),e("td",[t._v("All collections provide a title and keywords")])]),t._v(" "),e("tr",[e("td",[t._v("310")]),t._v(" "),e("td",[e("code",[t._v("GET /collections")]),t._v(" > collections")]),t._v(" "),e("td",[t._v("Extents report the actual availability of data and don't overpromise")])]),t._v(" "),e("tr",[e("td",[t._v("325")]),t._v(" "),e("td",[e("code",[t._v("GET /collections/{id}")]),t._v(" > stac_extensions")]),t._v(" "),e("td",[t._v("Lists all extensions that are used in the document")])])])]),t._v(" "),e("h4",{attrs:{id:"user-defined-processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-processes"}},[t._v("#")]),t._v(" User-defined processes")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("400")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("401")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs")])]),t._v(" "),e("td",[t._v("Valid response with at least processes and links as arrays")])]),t._v(" "),e("tr",[e("td",[t._v("403")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs")]),t._v(" > limit parameter")]),t._v(" "),e("td",[t._v("All processes are returned if no limit parameter is provided")])]),t._v(" "),e("tr",[e("td",[t._v("404")]),t._v(" "),e("td",[e("code",[t._v("GET 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process_graph")])]),t._v(" "),e("tr",[e("td",[t._v("422")]),t._v(" "),e("td",[e("code",[t._v("GET /process_graphs/{id}")])]),t._v(" "),e("td",[t._v("Missing properties in the process objects are not set to null if missing (allowed only for: summary, description, parameters, returns)")])]),t._v(" "),e("tr",[e("td",[t._v("430")]),t._v(" "),e("td",[e("code",[t._v("DELETE /process_graphs/{id}")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("431")]),t._v(" "),e("td",[e("code",[t._v("DELETE /process_graphs/{id}")])]),t._v(" "),e("td",[t._v("Deletes the process and returns a HTTP 204 on success, otherwise a 4XX.")])])])]),t._v(" "),e("h4",{attrs:{id:"data-processing-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#data-processing-2"}},[t._v("#")]),t._v(" Data Processing")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("702")]),t._v(" "),e("td"),t._v(" "),e("td",[t._v("At least two of batch jobs, secondary web services or synchronous processing are implemented")])])])]),t._v(" "),e("h4",{attrs:{id:"batch-jobs-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs-2"}},[t._v("#")]),t._v(" Batch Jobs")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("710")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("711")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs")])]),t._v(" "),e("td",[t._v("Valid response with at least jobs and links as arrays")])]),t._v(" "),e("tr",[e("td",[t._v("713")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs")]),t._v(" > limit parameter")]),t._v(" "),e("td",[t._v("All jobs are returned if no limit 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"),e("td",[e("code",[t._v("GET /jobs/{id}")]),t._v(" > status")]),t._v(" "),e("td",[t._v("Correctly reports status (and progress, if supported)")])]),t._v(" "),e("tr",[e("td",[t._v("780")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("781")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}")])]),t._v(" "),e("td",[t._v("Deletes the job and returns a HTTP 204 on success, otherwise a 4XX.")])])])]),t._v(" "),e("h4",{attrs:{id:"batch-jobs-logs"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs-logs"}},[t._v("#")]),t._v(" Batch Jobs > Logs")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("800")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("801")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > offset parameter")]),t._v(" "),e("td",[t._v("Supports query parameter offset, only returns log entries that were logged after the given offset")])]),t._v(" "),e("tr",[e("td",[t._v("804")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")])]),t._v(" "),e("td",[t._v("Valid response with at least logs and links as arrays")])]),t._v(" "),e("tr",[e("td",[t._v("805")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > level")]),t._v(" "),e("td",[t._v("level property is available if log_level has been set for the job or the level parameter has been provided")])]),t._v(" "),e("tr",[e("td",[t._v("806")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > logs")]),t._v(" "),e("td",[t._v("The served log entries are valid according to the specification (id, level and message are required)")])]),t._v(" "),e("tr",[e("td",[t._v("807")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > logs")]),t._v(" "),e("td",[t._v("time is supported, core and data is supported if applicable")])]),t._v(" "),e("tr",[e("td",[t._v("808")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/logs")]),t._v(" > logs")]),t._v(" "),e("td",[t._v("The message is short and concise, doesn't contain full stack traces.")])])])]),t._v(" "),e("h4",{attrs:{id:"batch-jobs-results-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs-results-2"}},[t._v("#")]),t._v(" Batch Jobs > Results")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("850")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Supports queueing/starting jobs (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("851")]),t._v(" "),e("td",[e("code",[t._v("POST 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Does nothing if already queued or running. Responds with HTTP status 202.")])]),t._v(" "),e("tr",[e("td",[t._v("852")]),t._v(" "),e("td",[e("code",[t._v("POST /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Rejects queueing a job if a payment is required.")])]),t._v(" "),e("tr",[e("td",[t._v("860")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("861")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")])]),t._v(" "),e("td",[t._v('Returns HTTP status code 200 if the result is available, HTTP status code 424 with error message if the job status is "error", 4XX/5XX otherwise.')])]),t._v(" "),e("tr",[e("td",[t._v("863")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > partial parameter")]),t._v(" "),e("td",[t._v("If processing has not finished and the partial parameter is not set to true, requests are rejected with openEO error JobNotFinished.")])]),t._v(" "),e("tr",[e("td",[t._v("865")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Response is a valid STAC Item or Collection")])]),t._v(" "),e("tr",[e("td",[t._v("866")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Supports a valid STAC version (0.9.0 or later, preferrably 1.0.0).")])]),t._v(" "),e("tr",[e("td",[t._v("869")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Assets all have an href and type, ideally also a title")])]),t._v(" "),e("tr",[e("td",[t._v("870")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > STAC")]),t._v(" "),e("td",[t._v("Additional metadata is provided for the results (e.g. extents, projection, bands, dimensions). Please provide details in the comments.")])]),t._v(" "),e("tr",[e("td",[t._v("871")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > public access link")]),t._v(" "),e("td",[t._v("Support canonical links (a public access signed URL, exposed as link with relation type canonical)")])]),t._v(" "),e("tr",[e("td",[t._v("872")]),t._v(" "),e("td",[e("code",[t._v("GET /jobs/{id}/results")]),t._v(" > public access link")]),t._v(" "),e("td",[t._v("Exposes the expires property in the response body")])]),t._v(" "),e("tr",[e("td",[t._v("890")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Is supported (with authentication)")])]),t._v(" "),e("tr",[e("td",[t._v("891")]),t._v(" "),e("td",[e("code",[t._v("DELETE /jobs/{id}/results")])]),t._v(" "),e("td",[t._v("Cancels data processing (if queued or running), optionally deletes the batch job results and returns a HTTP 202 on success.")])])])]),t._v(" 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"),e("td",[e("code",[t._v("POST /result")]),t._v(" > Content-Type header")]),t._v(" "),e("td",[t._v("Returns suitable media type for the requested file format (not always application/octet-stream) in the Content-Type header")])]),t._v(" "),e("tr",[e("td",[t._v("910")]),t._v(" "),e("td",[e("code",[t._v("POST /result")])]),t._v(" "),e("td",[t._v("Rejects processing if a payment is required.")])])])]),t._v(" "),e("h3",{attrs:{id:"l3-advanced"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#l3-advanced"}},[t._v("#")]),t._v(" L3: Advanced")]),t._v(" "),e("h4",{attrs:{id:"api-fundamentals-3"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#api-fundamentals-3"}},[t._v("#")]),t._v(" API fundamentals")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("#")]),t._v(" "),e("th",[t._v("Functionality")]),t._v(" "),e("th",[t._v("Description")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("8")]),t._v(" "),e("td",[t._v("All > Error Handling")]),t._v(" "),e("td",[t._v("Additionally returns 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Each objects contains at least code and message.")])]),t._v(" "),e("tr",[e("td",[t._v("493")]),t._v(" "),e("td",[e("code",[t._v("POST /validation")])]),t._v(" "),e("td",[t._v("HTTP status codes 4XX and 5XX are not used to indicate errors in the process. 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Keep in mind that you don't need to implement all endpoints in the first iteration and that you can specify in the Capabilities, which endpoints you are supporting.")],1),e._v(" "),t("p",[e._v("For example, you could start by implementing the following endpoints in the first iteration:")]),e._v(" "),t("ul",[t("li",[e._v("Well-Known Document: "),t("code",[e._v("GET /.well-known/openeo")])]),e._v(" "),t("li",[e._v("Capabilities: "),t("code",[e._v("GET /")]),e._v(" and "),t("code",[e._v("GET /file_formats")])]),e._v(" "),t("li",[e._v("Data discovery: "),t("code",[e._v("GET /collections")]),e._v(" and "),t("code",[e._v("GET /collections/{collection_id}")])]),e._v(" "),t("li",[e._v("Process discovery: "),t("code",[e._v("GET /processes")])]),e._v(" "),t("li",[e._v("Data processing: "),t("code",[e._v("POST /result")])]),e._v(" "),t("li",[e._v("Authentication (if required): "),t("code",[e._v("GET /credentials/basic")])])]),e._v(" "),t("p",[e._v("A more detailed recommendation about the endpoints and processes to implement can be found in the "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/profiles/"}},[e._v("API and Processes profiles")]),e._v(".")],1),e._v(" "),t("p",[e._v("Afterwards you can already start experimenting with your first process graphs and process EO data with our client libraries on your back-end.")]),e._v(" "),t("p",[t("em",[e._v("More information will follow soon, for example about back-end compliance testing.")])])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/79.263341a3.js b/assets/js/79.50b0dc75.js similarity index 99% rename from assets/js/79.263341a3.js rename to assets/js/79.50b0dc75.js index f911f5512..8b0e9a669 100644 --- a/assets/js/79.263341a3.js +++ b/assets/js/79.50b0dc75.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[79],{480:function(e,a,t){"use strict";t.r(a);var o=t(4),s=Object(o.a)({},(function(){var e=this,a=e._self._c;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h1",{attrs:{id:"performance-guide-for-openeo-backends"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#performance-guide-for-openeo-backends"}},[e._v("#")]),e._v(" Performance guide for openEO backends")]),e._v(" "),a("h2",{attrs:{id:"openeo-api-vision-on-performance-scalability"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#openeo-api-vision-on-performance-scalability"}},[e._v("#")]),e._v(" openEO API vision on performance & scalability")]),e._v(" "),a("p",[e._v("Given that the openEO API only defines a web service, it can in no way ensure the performance or scalability of an\nimplementation. What it can do however, is avoiding API definitions that prevent an implementation from being efficient.\nWhen openEO was designed, performance was one of the key design drivers, so here we try to explain how that is achieved.")]),e._v(" "),a("h3",{attrs:{id:"bringing-the-processing-to-the-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#bringing-the-processing-to-the-data"}},[e._v("#")]),e._v(" Bringing the processing to the data")]),e._v(" "),a("p",[e._v("Two evolutions created the need for an API designed for performance and scalability: EO programmes like Copernicus that\npushed data volumes into the petabyte range, and a move towards ever larger cloud infrastructure and HPC processing capacity\nto analyze these volumes of data. OpenEO supports this by defining data access and processing into a single specification.\nAs a result, the openEO process graph allows the backend to choose any data access pattern that is optimal for\nthe processing that is to be executed, and the dataset that is to be read.")]),e._v(" "),a("p",[e._v("A popular example of such a case, is infrastructures that store the data on the same machines that do the processing.\nIn such a case, an openEO backend can choose to load and process the data directly on the machine that has the data stored.\nAnother example is adjusting and aligning the data chunks for the processing to the internal layout of the file format that\nstores the EO data. IO performance optimizations like this are only possible if the processing engine has deep knowledge\nof the data organization from storage system over networks to file formats.")]),e._v(" "),a("h3",{attrs:{id:"datacube-processing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#datacube-processing"}},[e._v("#")]),e._v(" Datacube processing")]),e._v(" "),a("p",[e._v("The datacube view that openEO uses as a model to represent the data as it is transformed by various processes also has\nimportant performance and scalability implications.")]),e._v(" "),a("p",[e._v("The easiest way to understand this is to contrast it with a more traditional 'product-based' view of building workflows.\nIn a product or file based workflow, a process operates on a set of input files and generates a set of output files. Many\nEO workflows have been written like this, but the consequence is that every process spends time on reading date into memory, and\nwriting it back to disk. Persistent storage is often the slowest component in a processing system, and thus these workflows\nspend a lot of time on IO. In the openEO specification, a process transforms one datacube into another datacube.\nBackend implementations are encouraged to avoid writing data to disk in between processing steps whenever possible. By\nkeeping the datacubes into memory, this avoids those costly IO operations.")]),e._v(" "),a("p",[e._v("Here it is important to note that openEO does not enforce or define how the datacube should look like on the backend. The\ndatacube can be a set of files, or arrays in memory distributed over a cluster. These choices are left to the\nbackend implementor, this guide only tries to highlight the possibilities.")]),e._v(" "),a("p",[e._v("For scalability, the openEO processes clearly define along which set of dimensions of the datacube they operate. When\na user writes a process graph, it should never instruct the backend to apply a black box algorithm or function on the\nentire datacube. For most algorithms, this is not necessary, and loading the complete datacube of a Copernicus mission at once\nis simply not possible. Hence, users run '"),a("a",{attrs:{href:"https://openeo.org/documentation/1.0/developers/api/reference.html#section/Processes/Process-Graphs",target:"_blank",rel:"noopener noreferrer"}},[e._v("user-defined (child) processes"),a("OutboundLink")],1),e._v("' over a 1-dimensional array, or even multidimensional arrays or 'chunks'\nof the datacube. Based on this information, the backend is able to define both a data access and processing strategy that is\noptimal for the given process graph.")]),e._v(" "),a("h2",{attrs:{id:"process-graph-execution"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#process-graph-execution"}},[e._v("#")]),e._v(" Process graph execution")]),e._v(" "),a("p",[e._v("Here we go a bit more into detail about how a backend evaluates a process graph. Again, this is not normative or\nmandated by the specification, but rather an explanation of one way to achieve optimal performance.")]),e._v(" "),a("p",[e._v("In general, process graphs are first analyzed as a whole before the actual processing starts. The analysis phase serves\nto reveal the optimal processing strategy and parameters.")]),e._v(" "),a("p",[e._v("These are a few examples of things that can be derived from a process graph and subsequent optimizations:")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("Masking:")]),e._v(" when a raster dataset is masked with another raster or polygons, then often the loading of the datacube to\nwhich the mask is applied can be limited to unmasked values.")]),e._v(" "),a("li",[a("strong",[e._v("Vector filtering:")]),e._v(" various operations (aggregate_spatial, filter_spatial, mask_polygon) can restrict the datacube to a\nset of polygons, resulting in a rather sparse cube. Loading and processing of sparse cubes can be rather different from dense data cubes.")]),e._v(" "),a("li",[a("strong",[e._v("Resampling:")]),e._v(" resampling operations can allow data to be loaded from overviews rather than original resolution. Applying\nresampling and reprojection at load time can also be faster and save memory.")]),e._v(" "),a("li",[a("strong",[e._v("Multitemporal processing:")]),e._v(" many EO algorithms work over the temporal dimension rather than spatial dimensions. The\ntype of algorithm can be inferred from the process graph, allowing to adjust the processing strategy accordingly.")])]),e._v(" "),a("h2",{attrs:{id:"performance-faq"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#performance-faq"}},[e._v("#")]),e._v(" Performance FAQ")]),e._v(" "),a("h3",{attrs:{id:"i-have-a-highly-optimized-workflow-can-openeo-expose-it"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-have-a-highly-optimized-workflow-can-openeo-expose-it"}},[e._v("#")]),e._v(" I have a highly optimized workflow, can openEO expose it?")]),e._v(" "),a("p",[e._v("Basically openEO can expose anything as a custom 'process'. If your algorithm can not be expressed as an openEO process graph,\nthen you can just let your backend advertise your custom process. By doing this, you still benefit from a lot of the standardized\nfeatures in the openEO API, and most tools for openEO will also support working with custom processes. This is fairly similar\nto exposing your process in other standards such as OGC Processes.")]),e._v(" "),a("p",[e._v("We do expect however that it is much more likely that your workflow can still reuse a few standardized processes. For instance,\nif it can be run on a geographical bounding box, the filter_bbox process would be a standardized way to specify that. Or\nperhaps it can operate on any set of Sentinel-2 products, in which case you might fit in a load_collection to let your users\ncustomize the input data. So usually, while you might start from a fully custom process, you'll notice that openEO offers\nways to gradually standardize your workflow further in a stepwise manner.")]),e._v(" "),a("h3",{attrs:{id:"can-openeo-be-as-fast-as-a-hand-written-workflow"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#can-openeo-be-as-fast-as-a-hand-written-workflow"}},[e._v("#")]),e._v(" Can openEO be as fast as a hand-written workflow?")]),e._v(" "),a("p",[e._v("This question depends on which backend implementation you compare to which workflow, so there is no generic answer.\nWe do note that for writing non-trivial workflows in a cloud environment, you require a combination of algorithmic programming skills\nand cloud engineering that usually requires a team of skilled persons spending (in total) multiple person months to years on the same workflow.\nSo if you know that many workflow patterns in the operational openEO backends have already been highly optimized, you may\nwant to consider if the potential of reducing processing cost with a few percentages justifies the effort.")]),e._v(" "),a("p",[e._v("Also consider that next to the openEO API, there's also a community of open source backend implementations. So if you have\nthe skills to optimize processing pipelines to perfection, why don't you consider contributing to a backend that matches your\npreferred technology stack?")])])}),[],!1,null,null,null);a.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[79],{478:function(e,a,t){"use strict";t.r(a);var o=t(4),s=Object(o.a)({},(function(){var e=this,a=e._self._c;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h1",{attrs:{id:"performance-guide-for-openeo-backends"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#performance-guide-for-openeo-backends"}},[e._v("#")]),e._v(" Performance guide for openEO backends")]),e._v(" "),a("h2",{attrs:{id:"openeo-api-vision-on-performance-scalability"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#openeo-api-vision-on-performance-scalability"}},[e._v("#")]),e._v(" openEO API vision on performance & scalability")]),e._v(" "),a("p",[e._v("Given that the openEO API only defines a web service, it can in no way ensure the performance or scalability of an\nimplementation. What it can do however, is avoiding API definitions that prevent an implementation from being efficient.\nWhen openEO was designed, performance was one of the key design drivers, so here we try to explain how that is achieved.")]),e._v(" "),a("h3",{attrs:{id:"bringing-the-processing-to-the-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#bringing-the-processing-to-the-data"}},[e._v("#")]),e._v(" Bringing the processing to the data")]),e._v(" "),a("p",[e._v("Two evolutions created the need for an API designed for performance and scalability: EO programmes like Copernicus that\npushed data volumes into the petabyte range, and a move towards ever larger cloud infrastructure and HPC processing capacity\nto analyze these volumes of data. OpenEO supports this by defining data access and processing into a single specification.\nAs a result, the openEO process graph allows the backend to choose any data access pattern that is optimal for\nthe processing that is to be executed, and the dataset that is to be read.")]),e._v(" "),a("p",[e._v("A popular example of such a case, is infrastructures that store the data on the same machines that do the processing.\nIn such a case, an openEO backend can choose to load and process the data directly on the machine that has the data stored.\nAnother example is adjusting and aligning the data chunks for the processing to the internal layout of the file format that\nstores the EO data. IO performance optimizations like this are only possible if the processing engine has deep knowledge\nof the data organization from storage system over networks to file formats.")]),e._v(" "),a("h3",{attrs:{id:"datacube-processing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#datacube-processing"}},[e._v("#")]),e._v(" Datacube processing")]),e._v(" "),a("p",[e._v("The datacube view that openEO uses as a model to represent the data as it is transformed by various processes also has\nimportant performance and scalability implications.")]),e._v(" "),a("p",[e._v("The easiest way to understand this is to contrast it with a more traditional 'product-based' view of building workflows.\nIn a product or file based workflow, a process operates on a set of input files and generates a set of output files. Many\nEO workflows have been written like this, but the consequence is that every process spends time on reading date into memory, and\nwriting it back to disk. Persistent storage is often the slowest component in a processing system, and thus these workflows\nspend a lot of time on IO. In the openEO specification, a process transforms one datacube into another datacube.\nBackend implementations are encouraged to avoid writing data to disk in between processing steps whenever possible. By\nkeeping the datacubes into memory, this avoids those costly IO operations.")]),e._v(" "),a("p",[e._v("Here it is important to note that openEO does not enforce or define how the datacube should look like on the backend. The\ndatacube can be a set of files, or arrays in memory distributed over a cluster. These choices are left to the\nbackend implementor, this guide only tries to highlight the possibilities.")]),e._v(" "),a("p",[e._v("For scalability, the openEO processes clearly define along which set of dimensions of the datacube they operate. When\na user writes a process graph, it should never instruct the backend to apply a black box algorithm or function on the\nentire datacube. For most algorithms, this is not necessary, and loading the complete datacube of a Copernicus mission at once\nis simply not possible. Hence, users run '"),a("a",{attrs:{href:"https://openeo.org/documentation/1.0/developers/api/reference.html#section/Processes/Process-Graphs",target:"_blank",rel:"noopener noreferrer"}},[e._v("user-defined (child) processes"),a("OutboundLink")],1),e._v("' over a 1-dimensional array, or even multidimensional arrays or 'chunks'\nof the datacube. Based on this information, the backend is able to define both a data access and processing strategy that is\noptimal for the given process graph.")]),e._v(" "),a("h2",{attrs:{id:"process-graph-execution"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#process-graph-execution"}},[e._v("#")]),e._v(" Process graph execution")]),e._v(" "),a("p",[e._v("Here we go a bit more into detail about how a backend evaluates a process graph. Again, this is not normative or\nmandated by the specification, but rather an explanation of one way to achieve optimal performance.")]),e._v(" "),a("p",[e._v("In general, process graphs are first analyzed as a whole before the actual processing starts. The analysis phase serves\nto reveal the optimal processing strategy and parameters.")]),e._v(" "),a("p",[e._v("These are a few examples of things that can be derived from a process graph and subsequent optimizations:")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("Masking:")]),e._v(" when a raster dataset is masked with another raster or polygons, then often the loading of the datacube to\nwhich the mask is applied can be limited to unmasked values.")]),e._v(" "),a("li",[a("strong",[e._v("Vector filtering:")]),e._v(" various operations (aggregate_spatial, filter_spatial, mask_polygon) can restrict the datacube to a\nset of polygons, resulting in a rather sparse cube. Loading and processing of sparse cubes can be rather different from dense data cubes.")]),e._v(" "),a("li",[a("strong",[e._v("Resampling:")]),e._v(" resampling operations can allow data to be loaded from overviews rather than original resolution. 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If your algorithm can not be expressed as an openEO process graph,\nthen you can just let your backend advertise your custom process. By doing this, you still benefit from a lot of the standardized\nfeatures in the openEO API, and most tools for openEO will also support working with custom processes. This is fairly similar\nto exposing your process in other standards such as OGC Processes.")]),e._v(" "),a("p",[e._v("We do expect however that it is much more likely that your workflow can still reuse a few standardized processes. For instance,\nif it can be run on a geographical bounding box, the filter_bbox process would be a standardized way to specify that. Or\nperhaps it can operate on any set of Sentinel-2 products, in which case you might fit in a load_collection to let your users\ncustomize the input data. So usually, while you might start from a fully custom process, you'll notice that openEO offers\nways to gradually standardize your workflow further in a stepwise manner.")]),e._v(" "),a("h3",{attrs:{id:"can-openeo-be-as-fast-as-a-hand-written-workflow"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#can-openeo-be-as-fast-as-a-hand-written-workflow"}},[e._v("#")]),e._v(" Can openEO be as fast as a hand-written workflow?")]),e._v(" "),a("p",[e._v("This question depends on which backend implementation you compare to which workflow, so there is no generic answer.\nWe do note that for writing non-trivial workflows in a cloud environment, you require a combination of algorithmic programming skills\nand cloud engineering that usually requires a team of skilled persons spending (in total) multiple person months to years on the same workflow.\nSo if you know that many workflow patterns in the operational openEO backends have already been highly optimized, you may\nwant to consider if the potential of reducing processing cost with a few percentages justifies the effort.")]),e._v(" "),a("p",[e._v("Also consider that next to the openEO API, there's also a community of open source backend implementations. So if you have\nthe skills to optimize processing pipelines to perfection, why don't you consider contributing to a backend that matches your\npreferred technology stack?")])])}),[],!1,null,null,null);a.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/80.ae6e4278.js b/assets/js/80.d01ec16d.js similarity index 99% rename from assets/js/80.ae6e4278.js rename to assets/js/80.d01ec16d.js index feaea9cc9..df03492a3 100644 --- a/assets/js/80.ae6e4278.js +++ b/assets/js/80.d01ec16d.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[80],{479:function(e,o,t){"use strict";t.r(o);var a=t(4),r=Object(a.a)({},(function(){var e=this,o=e._self._c;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h1",{attrs:{id:"getting-started-with-openeo-and-xarray-and-dask"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#getting-started-with-openeo-and-xarray-and-dask"}},[e._v("#")]),e._v(" Getting started with openEO and Xarray and Dask")]),e._v(" "),o("p",[e._v("As a back-end provider who wants to provide its datasets, processes and infrastructure to a broader audience through a\nstandardized interface you may want to implement a driver for openEO.")]),e._v(" "),o("p",[e._v("First of all, you should read carefully the "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/backends/getting-started.html"}},[e._v("getting started guide for service providers")]),e._v(".")],1),e._v(" "),o("div",{staticClass:"custom-block tip"},[o("p",{staticClass:"custom-block-title"},[e._v("Note")]),e._v(" "),o("p",[e._v("The Xarray-Dask implementation for openEO is not a full-fledged out-of-the-box openEO back-end,\nbut can be part of the infrastructure for the data management and processing part.\nIn detail it can be used as data source for "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/EO-Data-Discovery"}},[e._v("EO Data Discovery")]),e._v(" and e.g.\nin combination with a Dask cluster as 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Make sure you properly handle all parameters defined for this process. Add a test for your process in "),o("code",[e._v("openeo-processes-dask/tests")]),e._v(" ideally using dask. The "),o("code",[e._v("create_fake_rastercube")]),e._v(" from the "),o("code",[e._v("openeo-processes-dask/tests/mockdata")]),e._v(" can be used for testing, with the "),o("code",[e._v("backend")]),e._v(" parameter set to "),o("code",[e._v("numpy")]),e._v(" or "),o("code",[e._v("dask")]),e._v(".")]),e._v(" "),o("li",[e._v("Push your code and open a PR.")])]),e._v(" "),o("h2",{attrs:{id:"http-rest-interface"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#http-rest-interface"}},[e._v("#")]),e._v(" HTTP REST Interface")]),e._v(" "),o("p",[e._v("The next step would be to set up a HTTP REST interface (i.e. an implementation of the openEO HTTP API) for the new openEO environment.\nIt must be available in front of the process implementations to properly answer openEO client requests.\nCurrently, the "),o("a",{attrs:{href:"https://openeo.eodc.eu/v1.0",target:"_blank",rel:"noopener noreferrer"}},[e._v("EODC"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"https://openeo.eurac.edu/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Eurac Research"),o("OutboundLink")],1),e._v(" back-ends use Xarray and Dask and thus\nare the first implementations of back-ends to look at.")]),e._v(" "),o("ul",[o("li",[e._v("EODC is using a Python implementation, the "),o("a",{attrs:{href:"https://github.com/eodcgmbh/openeo-fastapi",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-fastapi"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("li",[e._v("Eurac Research relies on a Java based implementation, the "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-spring-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-spring-driver"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("If you have any questions, please "),o("RouterLink",{attrs:{to:"/contact.html"}},[e._v("contact us")]),e._v(".")],1)])}),[],!1,null,null,null);o.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[80],{482:function(e,o,t){"use strict";t.r(o);var a=t(4),r=Object(a.a)({},(function(){var e=this,o=e._self._c;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h1",{attrs:{id:"getting-started-with-openeo-and-xarray-and-dask"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#getting-started-with-openeo-and-xarray-and-dask"}},[e._v("#")]),e._v(" Getting started with openEO and Xarray and Dask")]),e._v(" "),o("p",[e._v("As a back-end provider who wants to provide its datasets, processes and infrastructure to a broader audience through a\nstandardized interface you may want to implement a driver for openEO.")]),e._v(" "),o("p",[e._v("First of all, you should read carefully the "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/backends/getting-started.html"}},[e._v("getting started guide for service providers")]),e._v(".")],1),e._v(" "),o("div",{staticClass:"custom-block tip"},[o("p",{staticClass:"custom-block-title"},[e._v("Note")]),e._v(" "),o("p",[e._v("The Xarray-Dask implementation for openEO is not a full-fledged out-of-the-box openEO back-end,\nbut can be part of the infrastructure for the data management and processing part.\nIn detail it can be used as data source for "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/EO-Data-Discovery"}},[e._v("EO Data Discovery")]),e._v(" and e.g.\nin combination with a Dask cluster as processing back-end for "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/Data-Processing"}},[e._v("Data Processing")]),e._v(".\nIn any case, a "),o("a",{attrs:{href:"#http-rest-interface"}},[e._v("HTTP REST interface must be available in front of process implementations to properly answer openEO requests")]),e._v(".")],1)]),e._v(" "),o("p",[e._v("There are two main components involved with openEO and Xarray:")]),e._v(" "),o("ol",[o("li",[o("a",{attrs:{href:"#process-graph-parser-for-python"}},[e._v("Process Graph Parser for Python")])]),e._v(" "),o("li",[o("a",{attrs:{href:"#python-processes-for-openeo"}},[e._v("Python Processes for openEO")])])]),e._v(" "),o("h2",{attrs:{id:"process-graph-parser-for-python"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#process-graph-parser-for-python"}},[e._v("#")]),e._v(" Process Graph Parser for Python")]),e._v(" "),o("ul",[o("li",[e._v("Repository: "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-pg-parser-networkx",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-pg-parser-networkx"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("This pg-parser parses OpenEO process graphs from raw JSON into fully traversible networkx graph objects.")]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("ProcessRegistry")]),e._v(" can be imported from the pg-parser and includes "),o("code",[e._v("Process")]),e._v(" objects, that include a")]),e._v(" "),o("ul",[o("li",[e._v("spec: Process definition (e.g. https://github.com/Open-EO/openeo-processes)")]),e._v(" "),o("li",[e._v("implementation: Callable process implementation (https://github.com/Open-EO/openeo-processes-dask/tree/main/openeo_processes_dask/process_implementations)")]),e._v(" "),o("li",[e._v("namespace")])]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("ProcessRegistry")]),e._v(" automatically maps from the name of a process to the "),o("code",[e._v("spec")]),e._v(" and to the "),o("code",[e._v("implementation")]),e._v(".\nEvery "),o("code",[e._v("Process")]),e._v(" in the "),o("code",[e._v("ProcessRegistry")]),e._v(" requires a "),o("code",[e._v("spec")]),e._v(", while "),o("code",[e._v("implementation")]),e._v(" and "),o("code",[e._v("namespace")]),e._v(" are optional.")]),e._v(" "),o("p",[e._v("An example on how to use the pg-parser can be found "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-pg-parser-networkx/blob/main/examples/01_minibackend_demo.ipynb",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h2",{attrs:{id:"python-processes-for-openeo"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#python-processes-for-openeo"}},[e._v("#")]),e._v(" Python Processes for openEO")]),e._v(" "),o("ul",[o("li",[e._v("Repository: "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("This package includes the implementations of openEO processes, using Xarray and Dask. 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This means clients can...")]),t._v(" "),e("ul",[e("li",[t._v("choose which kind of casing they use (see below).")]),t._v(" "),e("li",[t._v("feel free to implement aliases for methods.")])]),t._v(" "),e("h2",{attrs:{id:"conventions"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#conventions"}},[t._v("#")]),t._v(" Conventions")]),t._v(" "),e("h3",{attrs:{id:"casing"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#casing"}},[t._v("#")]),t._v(" Casing")]),t._v(" "),e("p",[t._v("Clients can use "),e("code",[t._v("snake_case")]),t._v(", "),e("code",[t._v("camelCase")]),t._v(" or any method used commonly in their environment. For example, the API request to get a list of collections can either be names "),e("code",[t._v("get_collections")]),t._v(" or "),e("code",[t._v("getCollections")]),t._v(". 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Best practices for this will likely evolve over time.')]),t._v(" "),e("p",[t._v("Example for the "),e("code",[t._v("clientVersion")]),t._v(" method in "),e("code",[t._v("openEO")]),t._v(":")]),t._v(" "),e("ul",[e("li",[t._v("Procedural style: "),e("code",[t._v("openeo_client_version()")])]),t._v(" "),e("li",[t._v("Object-oriented style:"),e("div",{staticClass:"language-java extra-class"},[e("pre",{pre:!0,attrs:{class:"language-java"}},[e("code",[e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("OpenEO")]),t._v(" obj "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("new")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("OpenEO")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\nobj"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("clientVersion")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])])])]),t._v(" "),e("p",[t._v("If you can't store scope data in an object, you may need to pass these information as argument(s) to the method.")]),t._v(" "),e("p",[t._v("Example:")]),t._v(" "),e("ul",[e("li",[t._v("Procedural style:"),e("div",{staticClass:"language-php extra-class"},[e("pre",{pre:!0,attrs:{class:"language-php"}},[e("code",[e("span",{pre:!0,attrs:{class:"token variable"}},[t._v("$connection")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("openeo_connect")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string double-quoted-string"}},[t._v('"https://openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("openeo_capabilities")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token variable"}},[t._v("$connection")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])])]),t._v(" "),e("li",[t._v("Object-oriented style:"),e("div",{staticClass:"language-java extra-class"},[e("pre",{pre:!0,attrs:{class:"language-java"}},[e("code",[e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("OpenEO")]),t._v(" obj "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("new")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("OpenEO")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("Connection")]),t._v(" con "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" obj"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("connect")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\ncon"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("capabilities")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])])])]),t._v(" "),e("h3",{attrs:{id:"scope-categories"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#scope-categories"}},[t._v("#")]),t._v(" Scope categories")]),t._v(" "),e("p",[t._v("Each scope is assigned to a scope category, of which there are three:")]),t._v(" "),e("ul",[e("li",[e("em",[t._v("Root")]),t._v(" category: Contains only the scope "),e("code",[t._v("openEO")]),t._v(".")]),t._v(" "),e("li",[e("em",[t._v("API")]),t._v(" category: Mostly methods hiding API calls to the back-ends. 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This is the case because the parameter in "),e("code",[t._v("hasFeature(method_name)")]),t._v(" must be unambiguous.")]),t._v(" "),e("p",[t._v("Method names of scopes in the "),e("em",[t._v("Content")]),t._v(" category may collide with method names of scopes in the "),e("em",[t._v("root")]),t._v("/"),e("em",[t._v("API")]),t._v(" categories and names should be prefixed if collisions of names between different scope categories are to be expected.")]),t._v(" "),e("h3",{attrs:{id:"parameters"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#parameters"}},[t._v("#")]),t._v(" Parameters")]),t._v(" "),e("p",[t._v("The parameters usually follow the request schemes in the openAPI specification. The parameters should follow their characteristics, for example regarding the default values.")]),t._v(" "),e("p",[t._v("Some methods have a long list of (optional) parameters. This is easy to implement in languages that support named parameters such as R. For example, creating a job in R with a budget would lead to this method call:")]),t._v(" "),e("div",{staticClass:"language-R extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("createJob"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("process_graph "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),e("span",{pre:!0,attrs:{class:"token ellipsis"}},[t._v("...")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" budget "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("123")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Other languages that only support non-named parameters (i.e. the order of parameters is fixed) need to fill many parameters with default values, which is not convenient for a user. The example above in PHP would be:")]),t._v(" "),e("div",{staticClass:"language-php extra-class"},[e("pre",{pre:!0,attrs:{class:"language-php"}},[e("code",[e("span",{pre:!0,attrs:{class:"token function"}},[t._v("createJob")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("...")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("123")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("To avoid such method calls client developers should consider to pass either")]),t._v(" "),e("ul",[e("li",[t._v("an instance of a class, which contains all parameters as member variables or")]),t._v(" "),e("li",[t._v("the required parameters directly and the optional parameters as a dictionary (see example below).")])]),t._v(" "),e("p",[t._v("This basically emulates named parameters. The member variables / dictionary keys should use the same names as the parameters. The exemplary method call in PHP could be improved as follows:")]),t._v(" "),e("div",{staticClass:"language-php extra-class"},[e("pre",{pre:!0,attrs:{class:"language-php"}},[e("code",[e("span",{pre:!0,attrs:{class:"token function"}},[t._v("createJob")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("...")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),t._v("budget "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("123")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("h2",{attrs:{id:"method-mappings"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#method-mappings"}},[t._v("#")]),t._v(" Method mappings")]),t._v(" "),e("p",[e("strong",[t._v("Note:")]),t._v(" Some scopes for response JSON objects are still missing. We are open for proposals.")]),t._v(" "),e("p",[t._v("Parameters with a leading "),e("code",[t._v("?")]),t._v(" are optional.")]),t._v(" "),e("h3",{attrs:{id:"scope-openeo-root-category"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#scope-openeo-root-category"}},[t._v("#")]),t._v(" Scope: "),e("code",[t._v("openEO")]),t._v(" (root category)")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("Description")]),t._v(" "),e("th",[t._v("Client method")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("Connect to a back-end, includes version discovery ("),e("code",[t._v("GET /.well-known/openeo")]),t._v("), requesting capabilities and authentication where required. 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Best practices for this will likely evolve over time.')]),t._v(" "),e("p",[t._v("Example for the "),e("code",[t._v("clientVersion")]),t._v(" method in "),e("code",[t._v("openEO")]),t._v(":")]),t._v(" "),e("ul",[e("li",[t._v("Procedural style: "),e("code",[t._v("openeo_client_version()")])]),t._v(" "),e("li",[t._v("Object-oriented style:"),e("div",{staticClass:"language-java extra-class"},[e("pre",{pre:!0,attrs:{class:"language-java"}},[e("code",[e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("OpenEO")]),t._v(" obj "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("new")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("OpenEO")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\nobj"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("clientVersion")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])])])]),t._v(" "),e("p",[t._v("If you can't store scope data in an object, you may need to pass these information as argument(s) to the method.")]),t._v(" "),e("p",[t._v("Example:")]),t._v(" "),e("ul",[e("li",[t._v("Procedural style:"),e("div",{staticClass:"language-php extra-class"},[e("pre",{pre:!0,attrs:{class:"language-php"}},[e("code",[e("span",{pre:!0,attrs:{class:"token variable"}},[t._v("$connection")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("openeo_connect")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string double-quoted-string"}},[t._v('"https://openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("openeo_capabilities")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token variable"}},[t._v("$connection")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])])]),t._v(" "),e("li",[t._v("Object-oriented style:"),e("div",{staticClass:"language-java extra-class"},[e("pre",{pre:!0,attrs:{class:"language-java"}},[e("code",[e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("OpenEO")]),t._v(" obj "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("new")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("OpenEO")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("Connection")]),t._v(" con "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" obj"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("connect")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\ncon"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("capabilities")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])])])]),t._v(" "),e("h3",{attrs:{id:"scope-categories"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#scope-categories"}},[t._v("#")]),t._v(" Scope categories")]),t._v(" "),e("p",[t._v("Each scope is assigned to a scope category, of which there are three:")]),t._v(" "),e("ul",[e("li",[e("em",[t._v("Root")]),t._v(" category: Contains only the scope "),e("code",[t._v("openEO")]),t._v(".")]),t._v(" "),e("li",[e("em",[t._v("API")]),t._v(" category: Mostly methods hiding API calls to the back-ends. 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This is the case because the parameter in "),e("code",[t._v("hasFeature(method_name)")]),t._v(" must be unambiguous.")]),t._v(" "),e("p",[t._v("Method names of scopes in the "),e("em",[t._v("Content")]),t._v(" category may collide with method names of scopes in the "),e("em",[t._v("root")]),t._v("/"),e("em",[t._v("API")]),t._v(" categories and names should be prefixed if collisions of names between different scope categories are to be expected.")]),t._v(" "),e("h3",{attrs:{id:"parameters"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#parameters"}},[t._v("#")]),t._v(" Parameters")]),t._v(" "),e("p",[t._v("The parameters usually follow the request schemes in the openAPI specification. The parameters should follow their characteristics, for example regarding the default values.")]),t._v(" "),e("p",[t._v("Some methods have a long list of (optional) parameters. This is easy to implement in languages that support named parameters such as R. For example, creating a job in R with a budget would lead to this method call:")]),t._v(" "),e("div",{staticClass:"language-R extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("createJob"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("process_graph "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),e("span",{pre:!0,attrs:{class:"token ellipsis"}},[t._v("...")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" budget "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("123")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Other languages that only support non-named parameters (i.e. the order of parameters is fixed) need to fill many parameters with default values, which is not convenient for a user. The example above in PHP would be:")]),t._v(" "),e("div",{staticClass:"language-php extra-class"},[e("pre",{pre:!0,attrs:{class:"language-php"}},[e("code",[e("span",{pre:!0,attrs:{class:"token function"}},[t._v("createJob")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("...")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token constant"}},[t._v("null")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("123")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("To avoid such method calls client developers should consider to pass either")]),t._v(" "),e("ul",[e("li",[t._v("an instance of a class, which contains all parameters as member variables or")]),t._v(" "),e("li",[t._v("the required parameters directly and the optional parameters as a dictionary (see example below).")])]),t._v(" "),e("p",[t._v("This basically emulates named parameters. The member variables / dictionary keys should use the same names as the parameters. The exemplary method call in PHP could be improved as follows:")]),t._v(" "),e("div",{staticClass:"language-php extra-class"},[e("pre",{pre:!0,attrs:{class:"language-php"}},[e("code",[e("span",{pre:!0,attrs:{class:"token function"}},[t._v("createJob")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("...")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),t._v("budget "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("123")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("h2",{attrs:{id:"method-mappings"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#method-mappings"}},[t._v("#")]),t._v(" Method mappings")]),t._v(" "),e("p",[e("strong",[t._v("Note:")]),t._v(" Some scopes for response JSON objects are still missing. We are open for proposals.")]),t._v(" "),e("p",[t._v("Parameters with a leading "),e("code",[t._v("?")]),t._v(" are optional.")]),t._v(" "),e("h3",{attrs:{id:"scope-openeo-root-category"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#scope-openeo-root-category"}},[t._v("#")]),t._v(" Scope: "),e("code",[t._v("openEO")]),t._v(" (root category)")]),t._v(" "),e("table",[e("thead",[e("tr",[e("th",[t._v("Description")]),t._v(" "),e("th",[t._v("Client method")])])]),t._v(" "),e("tbody",[e("tr",[e("td",[t._v("Connect to a back-end, includes version discovery ("),e("code",[t._v("GET /.well-known/openeo")]),t._v("), requesting capabilities and authentication where required. 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")]),e._v("\n\t"),t("span",{pre:!0,attrs:{class:"token comment"}},[e._v("// You need to replace `Python` and `3.7` with the respective runtime identifier and version as returned in UDF discovery")]),e._v("\n\t"),t("span",{pre:!0,attrs:{class:"token keyword"}},[e._v("return")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token keyword"}},[e._v("this")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),t("span",{pre:!0,attrs:{class:"token function"}},[e._v("run_udf")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("data"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"udf.py"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"Python"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(";")]),e._v("\n"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("}")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(";")]),e._v("\n\n"),t("span",{pre:!0,attrs:{class:"token comment"}},[e._v("// Call the UDF as part of the reduce_dimension process to reduce the bands to a single value")]),e._v("\ndatacube "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" builder"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),t("span",{pre:!0,attrs:{class:"token function"}},[e._v("reduce_dimension")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("datacube"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v(" udfProcess"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"bands"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(";")]),e._v("\n\n"),t("span",{pre:!0,attrs:{class:"token comment"}},[e._v("// Further process the data cube or save it using save_result...")]),e._v("\n")])])]),t("h3",{attrs:{id:"python"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#python"}},[e._v("#")]),e._v(" Python")]),e._v(" "),t("p",[e._v("The Python client has "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/udf.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("separate documentation on UDFs"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h3",{attrs:{id:"qgis"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#qgis"}},[e._v("#")]),e._v(" QGIS")]),e._v(" "),t("p",[e._v("QGIS doesn't natively support running UDFs yet, but you can use copy and paste user-defined processes that include UDFs.")]),e._v(" "),t("h3",{attrs:{id:"r"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#r"}},[e._v("#")]),e._v(" R")]),e._v(" "),t("p",[e._v("There's no dedicated UDF documentation for R yet, but you can have a look at the "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-udf#usage",target:"_blank",rel:"noopener noreferrer"}},[e._v("getting started guide and examples in the UDF server repository"),t("OutboundLink")],1),e._v(" for now.")]),e._v(" "),t("h3",{attrs:{id:"web-editor"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#web-editor"}},[e._v("#")]),e._v(" Web Editor")]),e._v(" "),t("p",[e._v("Running UDFs in the Web Editor just requires you to drag the "),t("code",[e._v("run_udf")]),e._v(" process into the model builder. Afterwards you can edit the process parameters to your needs. You have to set the environment / programming language to run the UDF with and then you can either directly program a UDF in the code editor or select an UDF file you have previously uploaded to the user workspace.")]),e._v(" "),t("h2",{attrs:{id:"back-ends"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#back-ends"}},[e._v("#")]),e._v(" Back-ends")]),e._v(" "),t("p",[e._v("If you'd like to offer user-defined functions for your users, you need to implement either the process "),t("RouterLink",{attrs:{to:"/documentation/1.0/processes.html#run_udf"}},[t("code",[e._v("run_udf")])]),e._v(" or "),t("RouterLink",{attrs:{to:"/documentation/1.0/processes.html#run_udf_externally"}},[t("code",[e._v("run_udf_externally")])]),e._v(" (or both). Both usually require you to access UDF servers via the "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-udf/api_docs/",target:"_blank",rel:"noopener noreferrer"}},[e._v("UDF API"),t("OutboundLink")],1),e._v(". For the process "),t("code",[e._v("run_udf")]),e._v(" you may bypass the API and exchange data directly between the openEO back-end and the openEO UDF servers for performance reasons.")],1),e._v(" "),t("p",[t("code",[e._v("run_udf")]),e._v(" allows users to run their code on your infrastructure. This can be done using one of the UDF server implementations in "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-udf",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python"),t("OutboundLink")],1),e._v(" or "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-udf",target:"_blank",rel:"noopener noreferrer"}},[e._v("R"),t("OutboundLink")],1),e._v(". Please note that both UDF servers are "),t("strong",[e._v("drafts")]),e._v(" and still need work to improve performance and make them secure! This is not yet an out-of-the-box solution for running UDFs securely and fast.")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/88.3f39c999.js b/assets/js/88.af6d0cdf.js similarity index 97% rename from assets/js/88.3f39c999.js rename to assets/js/88.af6d0cdf.js index 932ddf29a..3a7ea460c 100644 --- a/assets/js/88.3f39c999.js +++ b/assets/js/88.af6d0cdf.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[88],{494:function(e,t,o){"use strict";o.r(t);var n=o(4),r=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"contributor-code-of-conduct"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#contributor-code-of-conduct"}},[e._v("#")]),e._v(" Contributor Code of Conduct")]),e._v(" "),t("p",[e._v("As contributors and maintainers of this project, we pledge to respect all people who\ncontribute through reporting issues, posting feature requests, updating documentation,\nsubmitting pull requests or patches, and other activities.")]),e._v(" "),t("p",[e._v("We are committed to making participation in this project a harassment-free experience for\neveryone, regardless of level of experience, gender, gender identity and expression,\nsexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.")]),e._v(" "),t("p",[e._v("Examples of unacceptable behavior by participants include the use of sexual language or\nimagery, derogatory comments or personal attacks, trolling, public or private harassment,\ninsults, or other unprofessional conduct.")]),e._v(" "),t("p",[e._v("Project maintainers have the right and responsibility to remove, edit, or reject comments,\ncommits, code, wiki edits, issues, and other contributions that are not aligned to this\nCode of Conduct. Project maintainers who do not follow the Code of Conduct may be removed\nfrom the project team.")]),e._v(" "),t("p",[e._v("Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by\nopening an issue or contacting one or more of the project maintainers.")]),e._v(" "),t("p",[e._v("This Code of Conduct is adapted from the "),t("a",{attrs:{href:"http://contributor-covenant.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Contributor Covenant"),t("OutboundLink")],1),e._v(", version 1.0.0, available at\n"),t("a",{attrs:{href:"http://contributor-covenant.org/version/1/0/0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://contributor-covenant.org/version/1/0/0/"),t("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[88],{493:function(e,t,o){"use strict";o.r(t);var n=o(4),r=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"contributor-code-of-conduct"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#contributor-code-of-conduct"}},[e._v("#")]),e._v(" Contributor Code of Conduct")]),e._v(" "),t("p",[e._v("As contributors and maintainers of this project, we pledge to respect all people who\ncontribute through reporting issues, posting feature requests, updating documentation,\nsubmitting pull requests or patches, and other activities.")]),e._v(" "),t("p",[e._v("We are committed to making participation in this project a harassment-free experience for\neveryone, regardless of level of experience, gender, gender identity and expression,\nsexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.")]),e._v(" "),t("p",[e._v("Examples of unacceptable behavior by participants include the use of sexual language or\nimagery, derogatory comments or personal attacks, trolling, public or private harassment,\ninsults, or other unprofessional conduct.")]),e._v(" "),t("p",[e._v("Project maintainers have the right and responsibility to remove, edit, or reject comments,\ncommits, code, wiki edits, issues, and other contributions that are not aligned to this\nCode of Conduct. Project maintainers who do not follow the Code of Conduct may be removed\nfrom the project team.")]),e._v(" "),t("p",[e._v("Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by\nopening an issue or contacting one or more of the project maintainers.")]),e._v(" "),t("p",[e._v("This Code of Conduct is adapted from the "),t("a",{attrs:{href:"http://contributor-covenant.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Contributor Covenant"),t("OutboundLink")],1),e._v(", version 1.0.0, available at\n"),t("a",{attrs:{href:"http://contributor-covenant.org/version/1/0/0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://contributor-covenant.org/version/1/0/0/"),t("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/90.c1ca903d.js b/assets/js/90.53479db4.js similarity index 99% rename from assets/js/90.c1ca903d.js rename to assets/js/90.53479db4.js index 8718ef791..b98c1a8a9 100644 --- a/assets/js/90.c1ca903d.js +++ b/assets/js/90.53479db4.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[90],{496:function(e,t,o){"use strict";o.r(t);var r=o(4),n=Object(r.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"ieee-igarrs-2021-hd-3-hands-on-openeo-access-cloud-platforms-using-your-preferred-programming-language"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#ieee-igarrs-2021-hd-3-hands-on-openeo-access-cloud-platforms-using-your-preferred-programming-language"}},[e._v("#")]),e._v(" IEEE IGARRS 2021 HD-3: Hands-on openEO: access cloud platforms using your preferred programming language")]),e._v(" "),t("p",[e._v("This page contains the agenda, and links to used material for the\nIEEE IGARRS 2021 openEO tutorial.")]),e._v(" "),t("p",[e._v("Details: "),t("a",{attrs:{href:"https://igarss2021.com/tutorials.php#tut9",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://igarss2021.com/tutorials.php#tut9"),t("OutboundLink")],1)]),e._v(" "),t("h2",{attrs:{id:"agenda"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#agenda"}},[e._v("#")]),e._v(" Agenda")]),e._v(" "),t("ul",[t("li",[e._v("14:00-14:20: Introduction to openEO (Pieter Kempeneers - JRC)")]),e._v(" "),t("li",[e._v("14:20-14:30: Q&A")]),e._v(" "),t("li",[e._v("14:30-15:30: Python client (VITO) (Jeroen Dries - VITO)")]),e._v(" "),t("li",[e._v("15:30-16:15: R client (WUR) (Dainius Masiliunas - WUR)")]),e._v(" "),t("li",[e._v("16:15-16:30: Break")]),e._v(" "),t("li",[e._v("16:30-16:45: openEO UIs (Jeroen Dries - VITO)")]),e._v(" "),t("li",[e._v("16:45-17:15: Use case demo (Jeroen Dries - VITO)")]),e._v(" "),t("li",[e._v("17:15-18:00: Q&A")])]),e._v(" "),t("h2",{attrs:{id:"python-client"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#python-client"}},[e._v("#")]),e._v(" Python client")]),e._v(" "),t("h3",{attrs:{id:"logging-in"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#logging-in"}},[e._v("#")]),e._v(" Logging in")]),e._v(" "),t("p",[e._v("For participants interested in trying thing out, try logging into Terrascope openEO backend:")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://docs.terrascope.be/#/Developers/WebServices/OpenEO/OpenEO",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://docs.terrascope.be/#/Developers/WebServices/OpenEO/OpenEO"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("Jupyter notebooks with the openEO Python client installed:\n"),t("a",{attrs:{href:"https://notebooks.terrascope.be",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://notebooks.terrascope.be"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("This jupyter environment also has the notebook samples in this git 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"),e("a",{attrs:{href:"https://twitter.com/MundialisInfo?ref_src=twsrc%5Etfw"}},[t._v("@MundialisInfo")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/EODC_GmbH?ref_src=twsrc%5Etfw"}},[t._v("@EODC_GmbH")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/hashtag/ifgi?src=hash&ref_src=twsrc%5Etfw"}},[t._v("#ifgi")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/CopernicusEU?ref_src=twsrc%5Etfw"}},[t._v("@CopernicusEU")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/sinergise?ref_src=twsrc%5Etfw"}},[t._v("@sinergise")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/EURAC?ref_src=twsrc%5Etfw"}},[t._v("@EURAC")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/hashtag/JRC?src=hash&ref_src=twsrc%5Etfw"}},[t._v("#JRC")]),t._v(" "),e("a",{attrs:{href:"https://t.co/9uCYd9A96S"}},[t._v("pic.twitter.com/9uCYd9A96S")])]),t._v("— openEO (@open_EO) "),e("a",{attrs:{href:"https://twitter.com/open_EO/status/938000627078230016?ref_src=twsrc%5Etfw"}},[t._v("December 5, 2017")])]),t._v(" "),e("script",{attrs:{async:"",src:"https://platform.twitter.com/widgets.js",charset:"utf-8"}}),t._v(" "),e("p",[t._v("We worked on the following back-ends:")]),t._v(" "),e("ul",[e("li",[t._v("Sentinel hub")]),t._v(" "),e("li",[t._v("EODC file-based")]),t._v(" "),e("li",[t._v("Rasdaman")]),t._v(" "),e("li",[t._v("GRASS")]),t._v(" "),e("li",[t._v("GeoTrellis")])]),t._v(" "),e("p",[t._v("and on the R and python clients, and started working on a "),e("a",{attrs:{href:t.$site.themeConfig.docPath+"glossary.html"}},[t._v("glossary")]),t._v(".")]),t._v(" "),e("p",[t._v("Among the many insights we gathered by sitting together and talk, we found that")]),t._v(" "),e("ul",[e("li",[t._v('the "core API" as described in the proposal is not so much a software layer on itself, but rather an API in front of every compute back-end; this simplifies the whole architecture pretty much')]),t._v(" "),e("li",[t._v("OpenSearch should have the ability to describe "),e("em",[t._v("collections")]),t._v(" of granules (or images, tiles) in addition to describing individual granules")]),t._v(" "),e("li",[t._v("band can be seen as array dimension as well as attributes of array records, but seeing it as a dimension may make life easier")]),t._v(" "),e("li",[t._v("use case 1 can be described as a sequence of filter operations (on image collection, bounding box, date range, and bands) followed by two aggregate operations (compute division over bands, compute mininum over time)")])]),t._v(" "),e("p",[t._v("Intermediate results can be found in a bunch of repositories, mostly proof-of-concept, on the openEO "),e("a",{attrs:{href:"https://github.com/Open-EO/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Github organisation"),e("OutboundLink")],1),t._v(".")])])}),[],!1,null,null,null);e.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[92],{497:function(t,e,r){"use strict";r.r(e);var s=r(4),a=Object(s.a)({},(function(){var t=this,e=t._self._c;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("p",[t._v("On Dec 4-6, VITO's "),e("a",{attrs:{href:"https://remotesensing.vito.be/",target:"_blank",rel:"noopener noreferrer"}},[t._v("remote sensing\nlab"),e("OutboundLink")],1),t._v(" hosted the first\nopenEO "),e("em",[t._v("week of intensive collaboration")]),t._v(", in Mol,\nBelgium. 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"),e("a",{attrs:{href:"https://twitter.com/MundialisInfo?ref_src=twsrc%5Etfw"}},[t._v("@MundialisInfo")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/EODC_GmbH?ref_src=twsrc%5Etfw"}},[t._v("@EODC_GmbH")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/hashtag/ifgi?src=hash&ref_src=twsrc%5Etfw"}},[t._v("#ifgi")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/CopernicusEU?ref_src=twsrc%5Etfw"}},[t._v("@CopernicusEU")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/sinergise?ref_src=twsrc%5Etfw"}},[t._v("@sinergise")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/EURAC?ref_src=twsrc%5Etfw"}},[t._v("@EURAC")]),t._v(" "),e("a",{attrs:{href:"https://twitter.com/hashtag/JRC?src=hash&ref_src=twsrc%5Etfw"}},[t._v("#JRC")]),t._v(" "),e("a",{attrs:{href:"https://t.co/9uCYd9A96S"}},[t._v("pic.twitter.com/9uCYd9A96S")])]),t._v("— openEO (@open_EO) "),e("a",{attrs:{href:"https://twitter.com/open_EO/status/938000627078230016?ref_src=twsrc%5Etfw"}},[t._v("December 5, 2017")])]),t._v(" "),e("script",{attrs:{async:"",src:"https://platform.twitter.com/widgets.js",charset:"utf-8"}}),t._v(" "),e("p",[t._v("We worked on the following back-ends:")]),t._v(" "),e("ul",[e("li",[t._v("Sentinel hub")]),t._v(" "),e("li",[t._v("EODC file-based")]),t._v(" "),e("li",[t._v("Rasdaman")]),t._v(" "),e("li",[t._v("GRASS")]),t._v(" "),e("li",[t._v("GeoTrellis")])]),t._v(" "),e("p",[t._v("and on the R and python clients, and started working on a "),e("a",{attrs:{href:t.$site.themeConfig.docPath+"glossary.html"}},[t._v("glossary")]),t._v(".")]),t._v(" "),e("p",[t._v("Among the many insights we gathered by sitting together and talk, we found that")]),t._v(" "),e("ul",[e("li",[t._v('the "core API" as described in the proposal is not so much a software layer on itself, but rather an API in front of every compute back-end; this simplifies the whole architecture pretty much')]),t._v(" "),e("li",[t._v("OpenSearch should have the ability to describe "),e("em",[t._v("collections")]),t._v(" of granules (or images, tiles) in addition to describing individual granules")]),t._v(" "),e("li",[t._v("band can be seen as array dimension as well as attributes of array records, but seeing it as a dimension may make life easier")]),t._v(" "),e("li",[t._v("use case 1 can be described as a sequence of filter operations (on image collection, bounding box, date range, and bands) followed by two aggregate operations (compute division over bands, compute mininum over time)")])]),t._v(" "),e("p",[t._v("Intermediate results can be found in a bunch of repositories, mostly proof-of-concept, on the openEO "),e("a",{attrs:{href:"https://github.com/Open-EO/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Github organisation"),e("OutboundLink")],1),t._v(".")])])}),[],!1,null,null,null);e.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/97.73b48ae7.js b/assets/js/97.2cb43740.js similarity index 99% rename from assets/js/97.73b48ae7.js rename to assets/js/97.2cb43740.js index 255215fd0..3456184d4 100644 --- a/assets/js/97.73b48ae7.js +++ b/assets/js/97.2cb43740.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[97],{506:function(e,t,o){"use strict";o.r(t);var n=o(4),a=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("With openEO, we are digging into new grounds regarding the interoperability of big Earth observation clouds. While we could address a lot of problems that came up, some issues remained that we couldn't solve. This article explains "),t("em",[e._v("some")]),e._v(" of the lessons learned and thus also gives an honest overview about current limitations of the openEO API.")]),e._v(" "),t("h2",{attrs:{id:"_1-collection-names"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_1-collection-names"}},[e._v("#")]),e._v(" 1. Collection Names")]),e._v(" "),t("p",[e._v("The naming of data sets (collections) is different across providers. We thought about recommending to follow lists like the "),t("a",{attrs:{href:"http://database.eohandbook.com/database/missionindex.aspx",target:"_blank",rel:"noopener noreferrer"}},[e._v("CEOS Mission Index"),t("OutboundLink")],1),e._v(", but then the question is what data does it really refer to? It lists "),t("code",[e._v("Sentinel-2 A")]),e._v(" and "),t("code",[e._v("Sentinel-2 B")]),e._v(", but would that be Level 1C or 2A or something completely different? Therefore, openEO decided to not enforce any naming scheme as data sets throughout the affiliated providers were mostly differently pre-processed anyway. To improve the situation, openEO allows to define process parameters. This allows to define algorithms independently of data set names, which can later be assigned when executing the process. Also, openEO recommends to split processing instructions into two parts: data loading/pre-processing and the actual algorithm.")]),e._v(" "),t("p",[e._v("Some more background information can be found in the corresponding "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/issues/52",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub issue"),t("OutboundLink")],1),e._v(". In general, many limitations (including the following) concern the data holdings of the providers and may hopefully be solved by initiatives that work on Analysis Ready Data (ARD).")]),e._v(" "),t("h2",{attrs:{id:"_2-other-names"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_2-other-names"}},[e._v("#")]),e._v(" 2. Other Names")]),e._v(" "),t("p",[e._v("Similarly, the naming of bands (and potentially other dimension labels, too) are inconsistent across providers, we faced names such as "),t("code",[e._v("B2")]),e._v(", "),t("code",[e._v("B02")]),e._v(", "),t("code",[e._v("2")]),e._v(" or "),t("code",[e._v("blue")]),e._v(" for Sentinel-2. openEO doesn't enforce any naming scheme, but has found ways to improve the situation for users. First, process parameters can be used (see above). Second, bands can also be specified using the "),t("a",{attrs:{href:"https://github.com/radiantearth/stac-spec/tree/v0.9.0/extensions/eo#common-band-names",target:"_blank",rel:"noopener noreferrer"}},[e._v("common names as specified by STAC"),t("OutboundLink")],1),e._v(" or wavelength ranges. openEO recommends to use common names whenever available.")]),e._v(" "),t("p",[e._v("For example, to avoid specific naming you could filter for Sentinel-2 (A and B) band 2 as follows:")]),e._v(" "),t("ul",[t("li",[e._v("by common name: "),t("code",[e._v("filter_bands(data = cube, bands = ['blue'])")]),e._v(" (blue maps to the wavelength range 0.45 - 0.50 according to STAC)")]),e._v(" "),t("li",[e._v("by wavelength, specified in μm: "),t("code",[e._v("filter_bands(data = cube, wavelength = [[0.49, 0.50]])")]),e._v(" (wavelength is 0.4966 for Sentinel-2A and 0.4921 for Sentinel-2B)")])]),e._v(" "),t("p",[e._v("In general, metadata is aligned for usage in openEO processes by using the "),t("a",{attrs:{href:"https://www.stacspec.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC specification"),t("OutboundLink")],1),e._v(", which improves interoperability.")]),e._v(" "),t("h2",{attrs:{id:"_3-chunked-collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_3-chunked-collections"}},[e._v("#")]),e._v(" 3. Chunked Collections")]),e._v(" "),t("p",[e._v("Some providers (for example "),t("a",{attrs:{href:"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR",target:"_blank",rel:"noopener noreferrer"}},[e._v("Google Earth Engine"),t("OutboundLink")],1),e._v(") expose data sets such as Sentinel-2 L2A as one large collection. Other providers expose the data sets split into groups, e.g. chunked by CRS and resolution. EURAC has collections such as "),t("code",[e._v("S2_32632_60m_L2A")]),e._v(" or "),t("code",[e._v("S2_32635_10m_L2A")]),e._v(" for example. There are valid "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/issues/180",target:"_blank",rel:"noopener noreferrer"}},[e._v("reasons"),t("OutboundLink")],1),e._v(" for any of the implementations, but to explain them is too much for this article. Unfortunately, those differences decrease the interoperability and in the end the re-usability of the processes. openEO aims to make algorithms re-usable and thus recommends to split processing instructions into two parts: data loading/pre-processing and the actual algorithm. This allows greater re-usability of the algorithms.")]),e._v(" "),t("h2",{attrs:{id:"_4-what-to-specify"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_4-what-to-specify"}},[e._v("#")]),e._v(" 4. What to specify?")]),e._v(" "),t("p",[e._v("The API has a focus on Service and Data Discovery, Data Processing and Result Retrieval/Publishing. Some more customer-oriented tasks such as user registration and payments are not handled through the openEO API. These aspects are too different across providers and can be handled more efficiently through external interfaces. Most providers already offer such services independently of the openEO API implementation anyway to offer their pre-openEO services.")]),e._v(" "),t("p",[e._v("Still, the openEO API uses OpenID Connect as main Authentication and Authorization mechanism (see also point 5). "),t("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenID Connect"),t("OutboundLink")],1),e._v(" has a "),t("a",{attrs:{href:"https://openid.net/specs/openid-connect-prompt-create-1_0.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("User Registration extension draft"),t("OutboundLink")],1),e._v(" that can be used by back-ends and may be adopted in the future. We closely follow upcoming specifications and standards and are happy to adopt anything useful.")]),e._v(" "),t("h3",{attrs:{id:"file-formats"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#file-formats"}},[e._v("#")]),e._v(" File Formats")]),e._v(" "),t("p",[e._v("Similarly, file "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/issues/63",target:"_blank",rel:"noopener noreferrer"}},[e._v("formats"),t("OutboundLink")],1),e._v(" and their "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/issues/32",target:"_blank",rel:"noopener noreferrer"}},[e._v("options"),t("OutboundLink")],1),e._v(' are often very different between processing software. That is why GDAL exists. To improve interoperability, the openEO API just recommends to align them with GDAL, which is used in most relevant software anyway. Unfortunately, this comes with some small inconveniences. For example, most people would probably expect to just specify "GeoTiff" as file format name, but GDAL has named it "GTiff". Another idea was to use '),t("a",{attrs:{href:"https://www.iana.org/assignments/media-types/media-types.xhtml",target:"_blank",rel:"noopener noreferrer"}},[e._v("media types"),t("OutboundLink")],1),e._v(" such as "),t("code",[e._v("application/json")]),e._v(", but then we realized that these are often to broad or not even defined. For example, openEO had to "),t("a",{attrs:{href:"https://github.com/opengeospatial/geotiff/issues/34",target:"_blank",rel:"noopener noreferrer"}},[e._v("push the OGC"),t("OutboundLink")],1),e._v(" to specify an official media type for GeoTiff. But the same issue exists for other file formats, too.")]),e._v(" "),t("h2",{attrs:{id:"_5-authentication"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_5-authentication"}},[e._v("#")]),e._v(" 5. Authentication")]),e._v(" "),t("p",[e._v("One of the most complex issues we faced during the API development was actually not EO or data processing related. It was the Authentication and Authorization mechanism. We quickly decided to use one of the well-established standards available for security reasons. Unfortunately, most of them don't seem to cater very well for our use case where you have a number of different clients and back-ends communicating with each other. Usually in the Web you have multiple clients communicating with a single back-end ("),t("em",[e._v("1:n")]),e._v("), but in openEO we have a "),t("em",[e._v("n:m")]),e._v(" relation. There are CLI tools, web clients and more that need to authenticate against a growing set of independent providers. Therefore, you can't easily ship clients with security measures like Client IDs (and Client Secrets) as required by "),t("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenID Connect"),t("OutboundLink")],1),e._v(" (based on "),t("a",{attrs:{href:"https://oauth.net/2/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OAuth 2"),t("OutboundLink")],1),e._v("). Users need to get those information by themselves and provide it to the clients, which is inconvenient and probably confusing to many non-developers. This issue is probably the thing I dislike most in the current API specification and thus would love to hear from you whether there are good solutions out there that we missed.")]),e._v(" "),t("h2",{attrs:{id:"_6-debugging-experience"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_6-debugging-experience"}},[e._v("#")]),e._v(" 6. Debugging Experience")]),e._v(" "),t("p",[e._v('The logging and debugging experience varies a lot between cloud processing and the "traditional workflow" (you download data and compute locally with your preferred tools). While it\'s "free" on a local machine to just do "trial and error" to check whether something is running, cloud processing usually comes with a cost and you likely don\'t want to work with "trial and error". Also you don\'t directly have access to the hardware and software and thus it is harder to investigate problems. openEO took several measures to mitigate these issues, but it is still a different experience for users. I guess that is a long learning process until it is adopted by users and may need additional effort to implement tools to improve the situation. While many users are getting used to it in situations where a streamlined experience is somewhat possible as only few clients communicate with a single provider (e.g. Google Earth Engine), the issue increases in openEO due to the different infrastructure and software used on back-end side.')]),e._v(" "),t("h2",{attrs:{id:"_7-defining-interoperable-processes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_7-defining-interoperable-processes"}},[e._v("#")]),e._v(" 7. Defining interoperable Processes")]),e._v(" "),t("p",[e._v("Defining a common set of pre-defined processes was a very long and challenging task.")]),e._v(" "),t("ol",[t("li",[e._v("First, we had to come up with a useful schema that allowed us to describe processes in JSON. We got inspired by different other standards such as "),t("a",{attrs:{href:"https://www.ogc.org/standards/wps",target:"_blank",rel:"noopener noreferrer"}},[e._v("WPS"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"http://spec.openapis.org/oas/v3.0.3",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenAPI"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("li",[e._v("Second, we had to find what processes are required and which granularity they should have. That quickly led to over "),t("a",{attrs:{href:"https://processes.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("a hundred processes"),t("OutboundLink")],1),e._v(", mainly for data cube and math operations. And that was only for mostly raster operations. We still have a big hole in the processes regarding vector related operations, but we aim to close that in the next year(s).")]),e._v(" "),t("li",[e._v("Third, we had to figure out what common processing softwares offered and how to come up with process definitions than can be implemented with those softwares.")]),e._v(" "),t("li",[e._v("Last, we had to specify it in a way that is unambiguous and easy to understand by users.")])]),e._v(" "),t("p",[e._v("All of these tasks took much more time than initially expected. For example, one of the partners reported that the granularity of the processes is not really suited for the underlying processing software. While we couldn't really change the granularity, it was at least not an issue to make openEO work for them. Fortunately, the process part of the openEO API is very flexible and allows providers to change the processes according to their needs or define their own processes. Due to the fact that the actual parameters and return values are described in JSON Schema, clients and back-ends can easily re-use existing JSON Schema implementations and adopt to changes in the process specifications. Thus, if a back-end doesn't support parts of a process, it can just be changed/removed and a client can understand that and cater for it.")]),e._v(" "),t("h2",{attrs:{id:"conclusion"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#conclusion"}},[e._v("#")]),e._v(" Conclusion")]),e._v(" "),t("p",[e._v("This was a deep dive into "),t("em",[e._v("some")]),e._v(" of my challenges during the last years. I hope it is useful for others and I'm hoping to get into discussions with developers facing similar issues. Maybe with joint forces we can solve some of these for a better cloud processing experience in the future. If you have anything to say, please contact me via "),t("a",{attrs:{href:"mailto:m.mohr@uni-muenster.de"}},[e._v("e-mail")]),e._v(", GitHub issues ("),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api",target:"_blank",rel:"noopener noreferrer"}},[e._v("API"),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes/issues",target:"_blank",rel:"noopener noreferrer"}},[e._v("Processes"),t("OutboundLink")],1),e._v(") or "),t("a",{attrs:{href:"https://twitter.com/matthmohr",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),t("OutboundLink")],1),e._v(". You can also "),t("RouterLink",{attrs:{to:"/contact.html"}},[e._v("contact openEO")]),e._v(".")],1)])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[97],{505:function(e,t,o){"use strict";o.r(t);var n=o(4),a=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("With openEO, we are digging into new grounds regarding the interoperability of big Earth observation clouds. While we could address a lot of problems that came up, some issues remained that we couldn't solve. This article explains "),t("em",[e._v("some")]),e._v(" of the lessons learned and thus also gives an honest overview about current limitations of the openEO API.")]),e._v(" "),t("h2",{attrs:{id:"_1-collection-names"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_1-collection-names"}},[e._v("#")]),e._v(" 1. Collection Names")]),e._v(" "),t("p",[e._v("The naming of data sets (collections) is different across providers. We thought about recommending to follow lists like the "),t("a",{attrs:{href:"http://database.eohandbook.com/database/missionindex.aspx",target:"_blank",rel:"noopener noreferrer"}},[e._v("CEOS Mission Index"),t("OutboundLink")],1),e._v(", but then the question is what data does it really refer to? It lists "),t("code",[e._v("Sentinel-2 A")]),e._v(" and "),t("code",[e._v("Sentinel-2 B")]),e._v(", but would that be Level 1C or 2A or something completely different? Therefore, openEO decided to not enforce any naming scheme as data sets throughout the affiliated providers were mostly differently pre-processed anyway. To improve the situation, openEO allows to define process parameters. This allows to define algorithms independently of data set names, which can later be assigned when executing the process. Also, openEO recommends to split processing instructions into two parts: data loading/pre-processing and the actual algorithm.")]),e._v(" "),t("p",[e._v("Some more background information can be found in the corresponding "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/issues/52",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub issue"),t("OutboundLink")],1),e._v(". In general, many limitations (including the following) concern the data holdings of the providers and may hopefully be solved by initiatives that work on Analysis Ready Data (ARD).")]),e._v(" "),t("h2",{attrs:{id:"_2-other-names"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_2-other-names"}},[e._v("#")]),e._v(" 2. Other Names")]),e._v(" "),t("p",[e._v("Similarly, the naming of bands (and potentially other dimension labels, too) are inconsistent across providers, we faced names such as "),t("code",[e._v("B2")]),e._v(", "),t("code",[e._v("B02")]),e._v(", "),t("code",[e._v("2")]),e._v(" or "),t("code",[e._v("blue")]),e._v(" for Sentinel-2. openEO doesn't enforce any naming scheme, but has found ways to improve the situation for users. First, process parameters can be used (see above). Second, bands can also be specified using the "),t("a",{attrs:{href:"https://github.com/radiantearth/stac-spec/tree/v0.9.0/extensions/eo#common-band-names",target:"_blank",rel:"noopener noreferrer"}},[e._v("common names as specified by STAC"),t("OutboundLink")],1),e._v(" or wavelength ranges. openEO recommends to use common names whenever available.")]),e._v(" "),t("p",[e._v("For example, to avoid specific naming you could filter for Sentinel-2 (A and B) band 2 as follows:")]),e._v(" "),t("ul",[t("li",[e._v("by common name: "),t("code",[e._v("filter_bands(data = cube, bands = ['blue'])")]),e._v(" (blue maps to the wavelength range 0.45 - 0.50 according to STAC)")]),e._v(" "),t("li",[e._v("by wavelength, specified in μm: "),t("code",[e._v("filter_bands(data = cube, wavelength = [[0.49, 0.50]])")]),e._v(" (wavelength is 0.4966 for Sentinel-2A and 0.4921 for Sentinel-2B)")])]),e._v(" "),t("p",[e._v("In general, metadata is aligned for usage in openEO processes by using the "),t("a",{attrs:{href:"https://www.stacspec.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC specification"),t("OutboundLink")],1),e._v(", which improves interoperability.")]),e._v(" "),t("h2",{attrs:{id:"_3-chunked-collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_3-chunked-collections"}},[e._v("#")]),e._v(" 3. Chunked Collections")]),e._v(" "),t("p",[e._v("Some providers (for example "),t("a",{attrs:{href:"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR",target:"_blank",rel:"noopener noreferrer"}},[e._v("Google Earth Engine"),t("OutboundLink")],1),e._v(") expose data sets such as Sentinel-2 L2A as one large collection. Other providers expose the data sets split into groups, e.g. chunked by CRS and resolution. EURAC has collections such as "),t("code",[e._v("S2_32632_60m_L2A")]),e._v(" or "),t("code",[e._v("S2_32635_10m_L2A")]),e._v(" for example. There are valid "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/issues/180",target:"_blank",rel:"noopener noreferrer"}},[e._v("reasons"),t("OutboundLink")],1),e._v(" for any of the implementations, but to explain them is too much for this article. Unfortunately, those differences decrease the interoperability and in the end the re-usability of the processes. openEO aims to make algorithms re-usable and thus recommends to split processing instructions into two parts: data loading/pre-processing and the actual algorithm. This allows greater re-usability of the algorithms.")]),e._v(" "),t("h2",{attrs:{id:"_4-what-to-specify"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_4-what-to-specify"}},[e._v("#")]),e._v(" 4. What to specify?")]),e._v(" "),t("p",[e._v("The API has a focus on Service and Data Discovery, Data Processing and Result Retrieval/Publishing. Some more customer-oriented tasks such as user registration and payments are not handled through the openEO API. These aspects are too different across providers and can be handled more efficiently through external interfaces. Most providers already offer such services independently of the openEO API implementation anyway to offer their pre-openEO services.")]),e._v(" "),t("p",[e._v("Still, the openEO API uses OpenID Connect as main Authentication and Authorization mechanism (see also point 5). "),t("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenID Connect"),t("OutboundLink")],1),e._v(" has a "),t("a",{attrs:{href:"https://openid.net/specs/openid-connect-prompt-create-1_0.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("User Registration extension draft"),t("OutboundLink")],1),e._v(" that can be used by back-ends and may be adopted in the future. We closely follow upcoming specifications and standards and are happy to adopt anything useful.")]),e._v(" "),t("h3",{attrs:{id:"file-formats"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#file-formats"}},[e._v("#")]),e._v(" File Formats")]),e._v(" "),t("p",[e._v("Similarly, file "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/issues/63",target:"_blank",rel:"noopener noreferrer"}},[e._v("formats"),t("OutboundLink")],1),e._v(" and their "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/issues/32",target:"_blank",rel:"noopener noreferrer"}},[e._v("options"),t("OutboundLink")],1),e._v(' are often very different between processing software. That is why GDAL exists. To improve interoperability, the openEO API just recommends to align them with GDAL, which is used in most relevant software anyway. Unfortunately, this comes with some small inconveniences. For example, most people would probably expect to just specify "GeoTiff" as file format name, but GDAL has named it "GTiff". Another idea was to use '),t("a",{attrs:{href:"https://www.iana.org/assignments/media-types/media-types.xhtml",target:"_blank",rel:"noopener noreferrer"}},[e._v("media types"),t("OutboundLink")],1),e._v(" such as "),t("code",[e._v("application/json")]),e._v(", but then we realized that these are often to broad or not even defined. For example, openEO had to "),t("a",{attrs:{href:"https://github.com/opengeospatial/geotiff/issues/34",target:"_blank",rel:"noopener noreferrer"}},[e._v("push the OGC"),t("OutboundLink")],1),e._v(" to specify an official media type for GeoTiff. But the same issue exists for other file formats, too.")]),e._v(" "),t("h2",{attrs:{id:"_5-authentication"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_5-authentication"}},[e._v("#")]),e._v(" 5. Authentication")]),e._v(" "),t("p",[e._v("One of the most complex issues we faced during the API development was actually not EO or data processing related. It was the Authentication and Authorization mechanism. We quickly decided to use one of the well-established standards available for security reasons. Unfortunately, most of them don't seem to cater very well for our use case where you have a number of different clients and back-ends communicating with each other. Usually in the Web you have multiple clients communicating with a single back-end ("),t("em",[e._v("1:n")]),e._v("), but in openEO we have a "),t("em",[e._v("n:m")]),e._v(" relation. There are CLI tools, web clients and more that need to authenticate against a growing set of independent providers. Therefore, you can't easily ship clients with security measures like Client IDs (and Client Secrets) as required by "),t("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenID Connect"),t("OutboundLink")],1),e._v(" (based on "),t("a",{attrs:{href:"https://oauth.net/2/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OAuth 2"),t("OutboundLink")],1),e._v("). Users need to get those information by themselves and provide it to the clients, which is inconvenient and probably confusing to many non-developers. This issue is probably the thing I dislike most in the current API specification and thus would love to hear from you whether there are good solutions out there that we missed.")]),e._v(" "),t("h2",{attrs:{id:"_6-debugging-experience"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_6-debugging-experience"}},[e._v("#")]),e._v(" 6. Debugging Experience")]),e._v(" "),t("p",[e._v('The logging and debugging experience varies a lot between cloud processing and the "traditional workflow" (you download data and compute locally with your preferred tools). While it\'s "free" on a local machine to just do "trial and error" to check whether something is running, cloud processing usually comes with a cost and you likely don\'t want to work with "trial and error". Also you don\'t directly have access to the hardware and software and thus it is harder to investigate problems. openEO took several measures to mitigate these issues, but it is still a different experience for users. I guess that is a long learning process until it is adopted by users and may need additional effort to implement tools to improve the situation. While many users are getting used to it in situations where a streamlined experience is somewhat possible as only few clients communicate with a single provider (e.g. Google Earth Engine), the issue increases in openEO due to the different infrastructure and software used on back-end side.')]),e._v(" "),t("h2",{attrs:{id:"_7-defining-interoperable-processes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#_7-defining-interoperable-processes"}},[e._v("#")]),e._v(" 7. Defining interoperable Processes")]),e._v(" "),t("p",[e._v("Defining a common set of pre-defined processes was a very long and challenging task.")]),e._v(" "),t("ol",[t("li",[e._v("First, we had to come up with a useful schema that allowed us to describe processes in JSON. We got inspired by different other standards such as "),t("a",{attrs:{href:"https://www.ogc.org/standards/wps",target:"_blank",rel:"noopener noreferrer"}},[e._v("WPS"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"http://spec.openapis.org/oas/v3.0.3",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenAPI"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("li",[e._v("Second, we had to find what processes are required and which granularity they should have. That quickly led to over "),t("a",{attrs:{href:"https://processes.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("a hundred processes"),t("OutboundLink")],1),e._v(", mainly for data cube and math operations. And that was only for mostly raster operations. We still have a big hole in the processes regarding vector related operations, but we aim to close that in the next year(s).")]),e._v(" "),t("li",[e._v("Third, we had to figure out what common processing softwares offered and how to come up with process definitions than can be implemented with those softwares.")]),e._v(" "),t("li",[e._v("Last, we had to specify it in a way that is unambiguous and easy to understand by users.")])]),e._v(" "),t("p",[e._v("All of these tasks took much more time than initially expected. For example, one of the partners reported that the granularity of the processes is not really suited for the underlying processing software. While we couldn't really change the granularity, it was at least not an issue to make openEO work for them. Fortunately, the process part of the openEO API is very flexible and allows providers to change the processes according to their needs or define their own processes. Due to the fact that the actual parameters and return values are described in JSON Schema, clients and back-ends can easily re-use existing JSON Schema implementations and adopt to changes in the process specifications. Thus, if a back-end doesn't support parts of a process, it can just be changed/removed and a client can understand that and cater for it.")]),e._v(" "),t("h2",{attrs:{id:"conclusion"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#conclusion"}},[e._v("#")]),e._v(" Conclusion")]),e._v(" "),t("p",[e._v("This was a deep dive into "),t("em",[e._v("some")]),e._v(" of my challenges during the last years. I hope it is useful for others and I'm hoping to get into discussions with developers facing similar issues. Maybe with joint forces we can solve some of these for a better cloud processing experience in the future. If you have anything to say, please contact me via "),t("a",{attrs:{href:"mailto:m.mohr@uni-muenster.de"}},[e._v("e-mail")]),e._v(", GitHub issues ("),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api",target:"_blank",rel:"noopener noreferrer"}},[e._v("API"),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes/issues",target:"_blank",rel:"noopener noreferrer"}},[e._v("Processes"),t("OutboundLink")],1),e._v(") or "),t("a",{attrs:{href:"https://twitter.com/matthmohr",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),t("OutboundLink")],1),e._v(". You can also "),t("RouterLink",{attrs:{to:"/contact.html"}},[e._v("contact openEO")]),e._v(".")],1)])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/98.87619bdd.js b/assets/js/98.5a862fc2.js similarity index 99% rename from assets/js/98.87619bdd.js rename to assets/js/98.5a862fc2.js index 12e0bae71..bb742f2a0 100644 --- a/assets/js/98.87619bdd.js +++ b/assets/js/98.5a862fc2.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[98],{505:function(e,t,s){"use strict";s.r(t);var o=s(4),n=Object(o.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("After two release candidates in early 2020, the openEO Consortium released the first stable version of its "),t("a",{attrs:{href:"https://api.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO API"),t("OutboundLink")],1),e._v(" and their set of "),t("a",{attrs:{href:"https://processes.openeo.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("common processes"),t("OutboundLink")],1),e._v(". This blog post gives an overview of the most notable changes since "),t("RouterLink",{attrs:{to:"/news/2019-03-07-openeo-api-040.html"}},[e._v("version 0.4")]),e._v(" and provides an outlook on the next months' project activity.")],1),e._v(" "),t("p",[t("em",[e._v("For those who are new to openEO, we have developed an open API to connect R, Python, JavaScript and other clients to big Earth observation cloud back-ends in a simple and unified way. The idea is that this will make cloud-based processing of large satellite image collections, or other data cubes such as weather model data easier for data scientists. The entire development process has been open, and all "),t("RouterLink",{attrs:{to:"/software.html"}},[e._v("software")]),e._v(" developed in this project is open source and available on "),t("a",{attrs:{href:"https://github.com/open-eo",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),t("OutboundLink")],1),e._v(". For more information about openEO, please read "),t("RouterLink",{attrs:{to:"/about.html"}},[e._v("page about the project")]),e._v(" first.")],1)]),e._v(" "),t("h2",{attrs:{id:"processes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[e._v("#")]),e._v(" Processes")]),e._v(" "),t("p",[e._v("The most important change was introduced just recently in openEO API version 1.0.0 RC2. Until then, openEO process graphs allowed users to express processing algorithms and execute them on a back-end using "),t("em",[e._v("pre-defined processes")]),e._v(" exposed by a back-end provider. While this still exists, a user can now also promote a process graph to a full openEO process description, following the same schema as the pre-defined processes: it must include the processing instructions and metadata like an identifier, parameters, return values, examples etc. This allows "),t("em",[e._v("user-defined processes")]),e._v(" to be stored and used like pre-defined processes, enabling users to extend the processing capabilities of a back-end. Users can now encapsulate algorithms in separate user-defined processes and re-use these in other user-defined processes. This also allows the exchange of processes (algorithms) between users and back-ends, and creates the possibility for a repository of user-contributed processes that other users can benefit from. A place for finding and sharing such processes is (planned to be) the "),t("a",{attrs:{href:"https://hub.openeo.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Hub"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Through user-defined processes (and user defined functions) users may also be able to substitute processes that back-ends have not implemented. In fact, several pre-defined processes are defined with alternative processing instructions that use other openEO pre-defined processes to solve the task. For example, the pre-defined process "),t("code",[e._v("normalized_ difference")]),e._v(" can be emulated with a combination of the processes "),t("code",[e._v("add")]),e._v(", "),t("code",[e._v("subtract")]),e._v(" and "),t("code",[e._v("divide")]),e._v(". So if a back-end is missing the "),t("code",[e._v("normalized_difference")]),e._v(" process, a user can just "),t("a",{attrs:{href:"https://processes.openeo.org/#normalized_difference",target:"_blank",rel:"noopener noreferrer"}},[e._v("download it"),t("OutboundLink")],1),e._v(" and use an openEO client to push it to the back-end where it then acts as drop-in replacement for the missing pre-defined process. In the future we plan to allow just using the URL of the process, so that no download/upload would be required.")]),e._v(" "),t("p",[e._v("As mentioned above, openEO offers a set of "),t("a",{attrs:{href:"https://processes.openeo.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("common processes"),t("OutboundLink")],1),e._v(" to improve interoperability and portability between back-ends. This is not a complete list and is expected to be extended by the openEO Consortium. More than that, we would like to see that processes arise from the community and that we can take over and standardize whatever comes up and is useful for the different domains working with openEO. We can then simply use the user-defined processes as they share the same schema as the pre-defined processes.")]),e._v(" "),t("p",[e._v("The pre-defined openEO processes are released separately from the API to ensure a faster release cycle than we plan for the API. We want to be able to quickly adapt to community needs with additional processes. Future version of the processes are valid with any future API version through the specification of the process metadata standard in openEO.")]),e._v(" "),t("p",[e._v("The pre-defined processes itself also got an update just after the API release, but "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes/blob/1.0.0/CHANGELOG.md#100---2020-07-31",target:"_blank",rel:"noopener noreferrer"}},[e._v("the changes"),t("OutboundLink")],1),e._v(" are relatively small compared to the API. The focus was to clarify unspecified and ambiguous parts of the specification. Some processes such as "),t("code",[e._v("apply_neighborhood")]),e._v(" and "),t("code",[e._v("constant")]),e._v(" were added for the use cases. In collaboration with "),t("a",{attrs:{href:"https://jwagemann.github.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Julia Wagemann"),t("OutboundLink")],1),e._v(" from the "),t("a",{attrs:{href:"https://www.ecmwf.int/",target:"_blank",rel:"noopener noreferrer"}},[e._v("ECMWF"),t("OutboundLink")],1),e._v(" we started defining processes such as "),t("code",[e._v("aggregate_temporal_frequency")]),e._v(", "),t("code",[e._v("anomaly")]),e._v(" and "),t("code",[e._v("climatological_normal")]),e._v(" for climatology and meteorology use-cases. Unfortunately, this effort could not be finished yet due to the COVID-19 situation. We want to continue to broaden the processes to be useful for more domains apart from core Earth Observation.")]),e._v(" "),t("h2",{attrs:{id:"stac-and-ogc-apis"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#stac-and-ogc-apis"}},[e._v("#")]),e._v(" STAC and OGC APIs")]),e._v(" "),t("p",[e._v("The openEO API now supports STAC (API) versions 0.9 and 1.x, which implements OGC API - Features. Thus, a potential integration of the openEO API with upcoming OGC API standards should be easy to establish.")]),e._v(" "),t("h2",{attrs:{id:"user-defined-functions-udfs"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-functions-udfs"}},[e._v("#")]),e._v(" User-defined Functions (UDFs)")]),e._v(" "),t("p",[t("em",[e._v("Note: Don't get confused, user-defined processes and user-defined functions describe different things.")])]),e._v(" "),t("p",[e._v("Although already foreseen in API version 0.4.0, the openEO UDF API has evolved and is better integrated in the openEO API. It now allows to run Docker containers or custom scripts in potentially any programming language. The UDF API itself is not part of the openEO API, but is a separate specification to describe the data exchange between a back-end implementing the openEO API and a UDF API instance. The UDF API is in release candidate phase and we'll likely post a separate blog post focussing solely on them. Currently, implementations in R and Python are available. It is foreseen that users could also host their own UDF API instances and call them from remote back-ends.")]),e._v(" "),t("h2",{attrs:{id:"other-improvements"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#other-improvements"}},[e._v("#")]),e._v(" Other improvements")]),e._v(" "),t("p",[e._v("Other notable changes and additions:")]),e._v(" "),t("ul",[t("li",[e._v("Support for importing user-provided data sets has been added.")]),e._v(" "),t("li",[e._v("Providers can now expose their terms of service and privacy policies.")]),e._v(" "),t("li",[e._v("The WebSocket-based Subscription API for notifications and monitoring introduced in version 0.4 has been replaced by a logging mechanism in the HTTP API. This makes implementing the API easier for clients and back-ends, but doesn't work in real-time any longer.")])]),e._v(" "),t("p",[e._v("The full set of changes can be found in the "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api/blob/1.0.0/CHANGELOG.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("API's change log"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"limitations"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#limitations"}},[e._v("#")]),e._v(" Limitations")]),e._v(" "),t("p",[e._v("With openEO, we are digging into new grounds regarding the interoperability of big Earth observation clouds. While we could solve a lot of issues that came up, there were some issues that we couldn't solve as part of the API and processes. We want to be honest and give some insights into the lessons learned, so I have posted these as a "),t("RouterLink",{attrs:{to:"/news/2020-07-31-lessons-learned.html"}},[e._v("separate blog post")]),e._v(".")],1),e._v(" "),t("h2",{attrs:{id:"next-steps"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[e._v("#")]),e._v(" Next steps")]),e._v(" "),t("p",[e._v("The API's development went through several iterations and is working well for our broad range of use cases, which we'll report soon. "),t("RouterLink",{attrs:{to:"/software.html"}},[e._v("Implementations")]),e._v(" by several organizations for several API versions provide evidence that the API and the processes are useful for a wide range of use cases. Client and back-end developers will now finish their implementations and we hope to have the first operational services this year. We will follow up with new blog posts once other releases are available. If you are interested in trying out openEO, please "),t("RouterLink",{attrs:{to:"/contact.html"}},[e._v("contact us")]),e._v(" to get more information.")],1)])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[98],{508:function(e,t,s){"use strict";s.r(t);var o=s(4),n=Object(o.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("After two release candidates in early 2020, the openEO Consortium released the first stable version of its "),t("a",{attrs:{href:"https://api.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO API"),t("OutboundLink")],1),e._v(" and their set of "),t("a",{attrs:{href:"https://processes.openeo.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("common processes"),t("OutboundLink")],1),e._v(". This blog post gives an overview of the most notable changes since "),t("RouterLink",{attrs:{to:"/news/2019-03-07-openeo-api-040.html"}},[e._v("version 0.4")]),e._v(" and provides an outlook on the next months' project activity.")],1),e._v(" "),t("p",[t("em",[e._v("For those who are new to openEO, we have developed an open API to connect R, Python, JavaScript and other clients to big Earth observation cloud back-ends in a simple and unified way. The idea is that this will make cloud-based processing of large satellite image collections, or other data cubes such as weather model data easier for data scientists. The entire development process has been open, and all "),t("RouterLink",{attrs:{to:"/software.html"}},[e._v("software")]),e._v(" developed in this project is open source and available on "),t("a",{attrs:{href:"https://github.com/open-eo",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),t("OutboundLink")],1),e._v(". 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A place for finding and sharing such processes is (planned to be) the "),t("a",{attrs:{href:"https://hub.openeo.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Hub"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Through user-defined processes (and user defined functions) users may also be able to substitute processes that back-ends have not implemented. In fact, several pre-defined processes are defined with alternative processing instructions that use other openEO pre-defined processes to solve the task. For example, the pre-defined process "),t("code",[e._v("normalized_ difference")]),e._v(" can be emulated with a combination of the processes "),t("code",[e._v("add")]),e._v(", "),t("code",[e._v("subtract")]),e._v(" and "),t("code",[e._v("divide")]),e._v(". So if a back-end is missing the "),t("code",[e._v("normalized_difference")]),e._v(" process, a user can just "),t("a",{attrs:{href:"https://processes.openeo.org/#normalized_difference",target:"_blank",rel:"noopener noreferrer"}},[e._v("download it"),t("OutboundLink")],1),e._v(" and use an openEO client to push it to the back-end where it then acts as drop-in replacement for the missing pre-defined process. In the future we plan to allow just using the URL of the process, so that no download/upload would be required.")]),e._v(" "),t("p",[e._v("As mentioned above, openEO offers a set of "),t("a",{attrs:{href:"https://processes.openeo.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("common processes"),t("OutboundLink")],1),e._v(" to improve interoperability and portability between back-ends. This is not a complete list and is expected to be extended by the openEO Consortium. More than that, we would like to see that processes arise from the community and that we can take over and standardize whatever comes up and is useful for the different domains working with openEO. We can then simply use the user-defined processes as they share the same schema as the pre-defined processes.")]),e._v(" "),t("p",[e._v("The pre-defined openEO processes are released separately from the API to ensure a faster release cycle than we plan for the API. We want to be able to quickly adapt to community needs with additional processes. Future version of the processes are valid with any future API version through the specification of the process metadata standard in openEO.")]),e._v(" "),t("p",[e._v("The pre-defined processes itself also got an update just after the API release, but "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes/blob/1.0.0/CHANGELOG.md#100---2020-07-31",target:"_blank",rel:"noopener noreferrer"}},[e._v("the changes"),t("OutboundLink")],1),e._v(" are relatively small compared to the API. The focus was to clarify unspecified and ambiguous parts of the specification. Some processes such as "),t("code",[e._v("apply_neighborhood")]),e._v(" and "),t("code",[e._v("constant")]),e._v(" were added for the use cases. In collaboration with "),t("a",{attrs:{href:"https://jwagemann.github.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Julia Wagemann"),t("OutboundLink")],1),e._v(" from the "),t("a",{attrs:{href:"https://www.ecmwf.int/",target:"_blank",rel:"noopener noreferrer"}},[e._v("ECMWF"),t("OutboundLink")],1),e._v(" we started defining processes such as "),t("code",[e._v("aggregate_temporal_frequency")]),e._v(", "),t("code",[e._v("anomaly")]),e._v(" and "),t("code",[e._v("climatological_normal")]),e._v(" for climatology and meteorology use-cases. Unfortunately, this effort could not be finished yet due to the COVID-19 situation. We want to continue to broaden the processes to be useful for more domains apart from core Earth Observation.")]),e._v(" "),t("h2",{attrs:{id:"stac-and-ogc-apis"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#stac-and-ogc-apis"}},[e._v("#")]),e._v(" STAC and OGC APIs")]),e._v(" "),t("p",[e._v("The openEO API now supports STAC (API) versions 0.9 and 1.x, which implements OGC API - Features. Thus, a potential integration of the openEO API with upcoming OGC API standards should be easy to establish.")]),e._v(" "),t("h2",{attrs:{id:"user-defined-functions-udfs"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-functions-udfs"}},[e._v("#")]),e._v(" User-defined Functions (UDFs)")]),e._v(" "),t("p",[t("em",[e._v("Note: Don't get confused, user-defined processes and user-defined functions describe different things.")])]),e._v(" "),t("p",[e._v("Although already foreseen in API version 0.4.0, the openEO UDF API has evolved and is better integrated in the openEO API. It now allows to run Docker containers or custom scripts in potentially any programming language. The UDF API itself is not part of the openEO API, but is a separate specification to describe the data exchange between a back-end implementing the openEO API and a UDF API instance. The UDF API is in release candidate phase and we'll likely post a separate blog post focussing solely on them. Currently, implementations in R and Python are available. It is foreseen that users could also host their own UDF API instances and call them from remote back-ends.")]),e._v(" "),t("h2",{attrs:{id:"other-improvements"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#other-improvements"}},[e._v("#")]),e._v(" Other improvements")]),e._v(" "),t("p",[e._v("Other notable changes and additions:")]),e._v(" "),t("ul",[t("li",[e._v("Support for importing user-provided data sets has been added.")]),e._v(" "),t("li",[e._v("Providers can now expose their terms of service and privacy policies.")]),e._v(" "),t("li",[e._v("The WebSocket-based Subscription API for notifications and monitoring introduced in version 0.4 has been replaced by a logging mechanism in the HTTP API. 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downloaded "),e("a",{attrs:{href:"https://owncloud.tuwien.ac.at/index.php/s/c469i4hvgaR15Ip",target:"_blank",rel:"noopener noreferrer"}},[t._v("here"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("hr"),t._v(" "),e("p",[t._v("openEO hosts a (virtual) user workshop on "),e("strong",[t._v("19 October 2020")]),t._v(" (10:30 - 16:30 CEST in two parts) and encourages interested parties to register for this event at "),e("a",{attrs:{href:"https://ec.europa.eu/eusurvey/runner/openEOUserWorkshop",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://ec.europa.eu/eusurvey/runner/openEOUserWorkshop"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("p",[e("strong",[t._v("Needed preparation:")]),t._v(" Manuals for installing the various client APIs, recommended for testing openEO in guided sessions, can be found in the "),e("em",[t._v("User Documentation")]),t._v(" under "),e("em",[e("RouterLink",{attrs:{to:"/documentation/1.0/"}},[t._v("Getting Started")])],1),t._v(".")]),t._v(" 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index 401517b87..95332c2d6 100644 --- a/contact.html +++ b/contact.html @@ -8,7 +8,7 @@ - + @@ -205,6 +205,6 @@ The email address below is NOT meant for technical support regarding specific service providers or client implementations.

Edit this page (opens new window)
Last Updated: 7/11/2024, 10:57:13 PM
- + diff --git a/documentation/0.4/developers/api/errors.html b/documentation/0.4/developers/api/errors.html index e93f52477..0cc0c350c 100644 --- a/documentation/0.4/developers/api/errors.html +++ b/documentation/0.4/developers/api/errors.html @@ -8,7 +8,7 @@ - + @@ -162,6 +162,6 @@ Note: Unsupported endpoints MUST use HTTP status code 501.

  • 500 Internal Server Error: The error has its origin on server side and no other status code in the 500 range is suitable.

  • 501 Not Implemented: An endpoint is specified in the openEO API, but is not supported.

    • If a HTTP status code in the 400 range is returned, the client SHOULD NOT repeat the request without modifications. For HTTP status code in the 500 range, the client MAY repeat the same request later.

    All HTTP status codes defined in RFC 7231 in the 400 and 500 ranges can be used as openEO error code in addition to the most used status codes mentioned here. Responding with openEO error codes 400 and 500 SHOULD be avoided in favor of any more specific standardized or proprietary openEO error code.

    # openEO error codes

    The following table of error codes is incomplete. These error codes will evolve over time. If you are missing any common error, please contribute it by adding an issue (opens new window), creating a pull request or get in contact in our chat room (opens new window).

    The whole table of error codes is available as JSON file, which can be used by implementors to automatically generate error responses.

    Account Management

    openEO Error Code Description Example Message HTTP Status Code
    AuthenticationRequired The client did not provide any authentication details for a resource requiring authentication or the provided authentication details are not correct. Unauthorized. 401
    AuthenticationSchemeInvalid Invalid authentication scheme (e.g. Bearer). Authentication method not supported. 403
    TokenInvalid Authorization token invalid or expired. Session has expired. 403
    CredentialsInvalid Credentials are not correct. 403

    EO Data Discovery

    openEO Error Code Description Example Message HTTP Status Code
    CollectionNotFound The requested collection does not exist. Collection does not exist. 404

    File Management

    openEO Error Code Description Example Message HTTP Status Code
    StorageFailure Server couldn't store file(s) due to server-side reasons. Unable to store file(s). 500
    StorageQuotaExceeded The storage quota has been exceeded by the user. Insufficient Storage. 400
    FileNotFound The requested file does not exist. File does not exist. 404
    FilePathInvalid The specified path is invalid or not accessible. Path could contain invalid characters, an invalid user ID or point to an existing folder or a location outside of the user folder. File path is invalid. 400
    FileOperationUnsupported The specified path is not a file and the operation is only supported for files. Path is likely a directory. Operation is only supported for files. 400
    ContentTypeInvalid The specified media (MIME) type used in the Content-Type header is not allowed. The media type is not supported. Allowed: {types} 400
    FileTypeInvalid File format, file extension or media (MIME) type is not allowed. File type not allowed. Allowed file types: {types} 400
    FileSizeExceeded File exceeds allowed maximum file size. File size it too large. Maximum file size: {size} 400
    FileContentInvalid The content of the file is invalid. File content is invalid. 400
    FileLocked The file is locked by a running job or another process. File '{file}' is locked. 400

    General

    openEO Error Code Description Example Message HTTP Status Code
    Internal An internal server error with a proprietary message. Server error: {message} 500
    NotFound To be used if the requested resource does not exist. Note: Unsupported endpoints MUST send an 'FeatureUnsupported' error. There are also specialized errors for missing jobs (JobNotFound), files (FileNotFound), etc. Resource not found. 404
    FeatureUnsupported The back-end responds with this error whenever an endpoint is specified in the openEO API, but is not supported. Feature not supported. 501
    InfrastructureMaintenance Service is currently not available, but the infrastructure is currently undergoing maintenance work. Service is not available at the moment due to maintenance work. Please try again later. 503
    InfrastructureBusy Service is generally available, but the infrastructure can't handle it at the moment as too many requests are processed. Service is not available at the moment due to overloading. Please try again later. 503
    Timeout The request took too long and timed out. Request timed out. 408
    ContentTypeInvalid The specified media (MIME) type used in the Content-Type header is not allowed. The media type is not supported. Allowed: {types} 400

    Job Management

    openEO Error Code Description Example Message HTTP Status Code
    Timeout The request took too long and timed out. Request timed out. 408
    BudgetInvalid The budget is too low as it is either smaller than or equal to 0 or below the costs. The budget is too low. 400
    NoDataForUpdate For PATCH requests: No valid data specified at all. No valid data specified to be updated. 400
    PropertyNotEditable For PATCH requests: The specified parameter can't be updated. It is read-only. Property '{property}' is read-only. 400
    StorageFailure Server couldn't store file(s) due to server-side reasons. Unable to store file(s). 500
    StorageQuotaExceeded The storage quota has been exceeded by the user. Insufficient Storage. 400
    ProcessGraphMissing No valid process graph specified. 400
    VariableValueMissing No value specified for process graph variable '{variable_id}'. 400
    VariableDefaultValueTypeInvalid The default value for the process graph variable '{variable_id}' is not of type '{type}'. 400
    VariableIdInvalid A specified variable ID is not valid. 400
    VariableTypeInvalid The data type for the process graph variable '{variable_id}' is invalid. Must be one of: string, boolean, number, array or object. 400
    JobNotFound The requested job does not exist. The job does not exist. 404
    FormatUnsupported Output format not supported. 400
    FormatArgumentUnsupported Output format argument '{argument}' is not supported. 400
    FormatArgumentInvalid The output format argument '{argument}' is invalid: {reason} 400
    FormatUnsuitable Data can't be transformed into the requested output format. 400
    JobLocked The job is currently locked due to a running batch computation and can't be modified meanwhile. Job is locked due to a queued or running batch computation. 400
    JobNotFinished Job has not finished computing the results yet. Please try again later. 400
    JobNotStarted Job has not been queued or started yet or was canceled and not restarted by the user. Job hasn't been started yet. 400
    PaymentRequired The budget required to fulfil the request are insufficient. Payment required. 402
    BillingPlanInvalid The billing plan is not on the list of available plans. The billing plan is not valid. 400

    Process Graph Management

    openEO Error Code Description Example Message HTTP Status Code
    NoDataForUpdate For PATCH requests: No valid data specified at all. No valid data specified to be updated. 400
    PropertyNotEditable For PATCH requests: The specified parameter can't be updated. It is read-only. Property '{property}' is read-only. 400
    ProcessGraphNotFound The requested process graph does not exist. Process graph does not exist. 404
    ProcessGraphMissing No valid process graph specified. 400
    VariableValueMissing No value specified for process graph variable '{variable_id}'. 400
    VariableDefaultValueTypeInvalid The default value for the process graph variable '{variable_id}' is not of type '{type}'. 400
    VariableIdInvalid A specified variable ID is not valid. 400
    VariableTypeInvalid The data type for the process graph variable '{variable_id}' is invalid. Must be one of: string, boolean, number, array or object. 400

    Processes

    openEO Error Code Description Example Message HTTP Status Code
    CRSInvalid Invalid or unsupported CRS specified. CRS '{crs}' is invalid. 400
    CoordinateOutOfBounds Coordinate is out of bounds. 400
    CollectionNotFound The requested collection does not exist. Collection does not exist. 404
    FileNotFound The requested file does not exist. File does not exist. 404
    FileContentInvalid The content of the file is invalid. File content is invalid. 400
    ProcessUnsupported Process '{process}' is not supported. 400
    ProcessArgumentUnsupported Process '{process}' does not support argument '{argument}'. 400
    ProcessArgumentInvalid The argument '{argument}' in process '{process}' is invalid: {reason} 400
    ProcessArgumentRequired Process '{process}' requires argument '{argument}'. 400
    ProcessArgumentsMissing Process '{process}' requires at least {min_parameters} parameters. 400
    JobNotFound The requested job does not exist. The job does not exist. 404

    Secondary Services Management

    openEO Error Code Description Example Message HTTP Status Code
    BudgetInvalid The budget is too low as it is either smaller than or equal to 0 or below the costs. The budget is too low. 400
    NoDataForUpdate For PATCH requests: No valid data specified at all. No valid data specified to be updated. 400
    PropertyNotEditable For PATCH requests: The specified parameter can't be updated. It is read-only. Property '{property}' is read-only. 400
    ProcessGraphMissing No valid process graph specified. 400
    VariableValueMissing No value specified for process graph variable '{variable_id}'. 400
    PaymentRequired The budget required to fulfil the request are insufficient. Payment required. 402
    BillingPlanInvalid The billing plan is not on the list of available plans. The billing plan is not valid. 400
    ServiceNotFound The requested secondary service does not exist. Service does not exist. 404
    ServiceUnsupported Secondary service type is not supported. 400
    ServiceArgumentUnsupported Secondary service argument '{argument}' is not supported. 400
    ServiceArgumentInvalid The secondary service argument '{argument}' is invalid: {reason} 400
    ServiceArgumentRequired Required secondary service argument '{argument}' is missing. 400

    Subscriptions

    openEO Error Code Description Example Message HTTP Status Code
    WebSocketUpgradeNotRequested In order to subscribe the connection must be upgradable to WebSockets. Client sent invalid request to establish subscriptions. 400
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    • Architecture

    # Architecture

    The openEO API defines a language how clients communicate to back-ends in order to analyze large Earth observation datasets. The API will be implemented by drivers for specific back-ends. Some first architecture considerations are listed below.

    1. The openEO API is a contract between clients and back-ends that describes the communication only
    2. Each back-end runs its own API instance including the specific back-end driver. There is no API instance that runs more than one driver.
    3. Clients in R, Python, and JavaScript connect directly to the back-ends and communicate with the back-ends over HTTPS according to the openEO API specification.
    4. API instances can run on back-end servers or additional intermediate layers, which then communicate to back-ends in a back-end specific way.
    5. Back-ends may add functionality and extend the API wherever there is need.
    6. There will be a central back-end registry service (openEO Hub), to allow users to search for back-ends with specific functionality and or data.
    7. The openEO API may define profiles in order to group specific functionality.

    Architecture - openEO API shown in dark blue

    # Microservices

    To simplify and structure the development, the API is divided into a few microservices.

    Microservice Description
    Capabilities This microservice reports on the capabilities of the back-end, i.e. which API endpoints are implemented, which authentication methods are supported, and whether and how UDFs can be executed at the back-end.
    EO Data Discovery Describes which collections are available at the back-end.
    Process Discovery Provides services to find out which processes a back-end provides, i.e., what users can do with the available data.
    UDF Discovery and execution of user-defined functions.
    Batch Job Management Organizes and manages batch jobs that run processes on back-ends.
    File Management Organizes and manages user-uploaded files.
    Process Graph Management Organizes and manages user-defined process graphs.
    Secondary Services Management External web services to access data and job results such as a OGC WMTS service.
    Account Management User management, accounting and authentication.
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    # Getting started for back-end providers

    As a back-end provider who wants to provide its datasets, processes and infrastructure to a broader audience through a standardized interface you may want to implement a driver for openEO.

    First of all, you should go through the list of openEO repositories (opens new window) and check whether there is already a back-end driver that suits your needs. In this case you don't need to develop your own driver, but "only" need to ingest your data, adopt your required processes and set up the infrastructure. Please follow the documentation for the individual driver you want to use.

    If your preferred technology has no back-end driver yet, you may consider writing your own driver. All software written for openEO should follow the software development guidelines.

    You certainly need to understand the glossary, the architecture of openEO and the concepts behind processes and process graphs. This helps you read and understand the API specification. Technical API related documents like CORS and error handing should be read, too.

    If you do not want to start from scratch, you could try to generate a server stub from the OpenAPI 3.0 (opens new window)-based API specification with the OpenAPI Generator (opens new window). If you are using Python to implement your driver you may reuse some common modules of the existing driver implementations:

    You can implement a back-end in iterations. It is recommended to start by implementing the Capabilities microservice. EO Data Discovery, Process Discovery are important for the client libraries to be available, too. Afterwards you should implement Batch Job Management or synchronous data processing. All other microservices can be added later and are not strictly required to run openEO services. Keep in mind that you don't need to implement all endpoints in the first iteration and that you can specify in the Capabilities, which endpoints you are supporting.

    For example, you could start by implementing the following endpoints in the first iteration:

    • Well-Known Document: GET /.well-known/openeo
    • Capabilities: GET / and GET /output_formats
    • Data discovery: GET /collections and GET /collections/{collection_id}
    • Process discovery: GET /processes
    • Data processing: POST /result
    • Authentication (if required): GET /credentials/basic

    Afterwards you can already start experimenting with your first process graphs and process EO data with our client libraries on your back-end.

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    Contact

    • User-defined functions

    # User-defined functions

    The abbreviation UDF stands for user-defined function. With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, allowing custom calculations on server-side data.

    UDFs are currently developed and evaluated outside of the core API. More information regarding the current draft for UDFs can be found in a separate repository (opens new window). There is additional documentation available for the UDF Framework (opens new window) and the UDF API (opens new window).

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    # Getting started for client developers

    For easy access to openEO back-ends it is essential to provide client libraries for users in their well-known programming languages or working environments. This can be either a client library for a specific programming language that hides the technical details of the openEO API or an application with a user interface, e.g. a GIS software plugin or a web-based tool. All software written for openEO should follow the software development guidelines.

    # Client library developers

    If your preferred programming language is not part of the available client libraries you may consider writing your own client library. Our client libraries are basically translating the openEO API into native concepts of the programming languages. Working with openEO should feel like being a first-class citizen (opens new window) of the programming language.

    Get started by reading the guidelines to develop client libraries, which have been written to ensure the client libraries provide a consistent feel and behavior across programming languages. You certainly need to understand the glossary and the concepts behind processes and process graphs. This helps you understand the API specification and related documents.

    If you do not want to start from scratch, you could try to generate a client library stub from the OpenAPI 3.0 (opens new window)-based API specification with the OpenAPI Generator (opens new window). Make sure the generated code complies to the client library guidelines mentioned above.

    # Applications and Software plugins

    Standalone applications and software plugins written in a certain programming language could use the existing client libraries to facilitate access to openEO back-ends. Web applications potentially could use the JavaScript client (opens new window) to access openEO back-ends. Back-Ends may also provide standardized web interfaces such as OGC WMS or OGC WCS to access processed EO data.

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    Contact

    • Examples

    # Examples

    This section contains process graphs that show how the openEO processes work in practice.

    Please feel encouraged to add your examples via Pull Requests!

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  • Download file(s)

    Request

    GET https://cdn.openeo.org/4854b51643548ab8a858e2b8282711d8/result.json HTTP/1.1

    Response

    A JSON file containing the results, content omitted.

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    # Introduction

    This is the openEO Developer Documentation for API versions 0.4.x.

    The openEO API defines a HTTP API that lets cloud back-ends with large Earth observation datasets communicate with front end analysis applications in an interoperable way.

    As an overview, the openEO API specifies how to

    • discover which Earth observation data and processes are available at cloud back-ends,
    • execute (chained) processes on back-ends,
    • run user-defined functions (UDFs) on back-ends where UDFs can be exposed to the data in different ways,
    • download (intermediate) results, and
    • manage user content including billing.

    The API is defined as an OpenAPI 3.0 (opens new window) JSON file.

    # Additional information

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    Contact

    # Getting started for users

    Currently, there are three official client libraries, a web-based interface for openEO and a QGIS plugin.

    If you are unfamiliar with programming, you could start using the web-based editor for openEO (opens new window). It supports visual modelling of your algorithms and a simplified JavaScript based access to the openEO workflows and providers. openEO can also be used in QGIS through a QGIS plugin (opens new window).

    If you are familiar with programming, you could choose a client library for three programming languages:

    Follow the links above to find usage instructions for each of the client libraries.

    # Contribute

    Didn't find your programming language? You can also access the openEO API implementations directly or start implementing your own client library.

    If you are missing any functionality in the API feel free to open an issue (opens new window) or actively start proposing API changes as Pull Requests.

    Feel free to contact us for further assistance.

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    - + diff --git a/documentation/0.4/glossary.html b/documentation/0.4/glossary.html index 08094f78a..fa134856e 100644 --- a/documentation/0.4/glossary.html +++ b/documentation/0.4/glossary.html @@ -8,7 +8,7 @@ - + @@ -168,6 +168,6 @@ the values along that dimension to a single value. For example: eliminate the time dimension by taking the mean along that dimension. Another example is taking the sum or max along the band dimension.

    # aggregate: reducing resolution

    Aggregation computes new values from sets of values that are uniquely assigned to groups. It involves a grouping predicate (e.g. monthly, 100 m x 100 m grid cells, or a set of non-overlapping spatial polygons), and an reducer (e.g., mean) that computes one or more new values from the original ones.

    In effect, aggregate combines the following three steps:

    • split the data cube in groups, based on dimension constraints (time intervals, band groups, spatial polygons)
    • apply a reducer to each group (similar to the reduce process, but reducing a group rather than an entire dimension)
    • combine the result to a new data cube, with some dimensions having reduced resolution (or e.g. raster to vector converted)

    Examples:

    • a weekly time series may be aggregated to monthly values by computing the mean for all values in a month (grouping predicate: months)
    • spatial aggregation involves computing e.g. mean pixel values on a 100 x 100 m grid, from 10 m x 10 m pixels, where each original pixel is assigned uniquely to a larger pixel (grouping predicate: 100 m x 100 m grid cells)

    # resample: changing data cube geometry

    Resampling considers the case where we have data at one resolution and coordinate reference system, and need values at another. In case we have values at a 100 m x 100 m grid and need values at a 10 m x 10 m grid, the original values will be reused many times, and may be simply assigned to the nearest high resolution grid cells (nearest neighbor method), or may be interpolated using various methods (e.g. by bilinear interpolation). This is often called upsampling or upscaling.

    Resampling from finer to coarser grid is a special case of aggregation often called downsampling or downscaling.

    When the target grid or time series has a lower resolution (larger grid cells) or lower frequency (longer time intervals) than the source grid, aggregation might be used for resampling. For example, if the resolutions are similar, (e.g. the source collection provides 10 day intervals and the target needs values for 16 day intervals), then some form of interpolation may be more appropriate than aggregation as defined here.

    # User-defined function (UDF)

    The abbreviation UDF stands for user-defined function. With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, or applied to a particular dimension or set of dimensions, allowing custom server-side calculations. See the section on UDFs for more information.

    # Data Processing modes

    Process graphs can be processed in three different ways:

    1. Results can be pre-computed by creating a batch job using POST /jobs. They are submitted to the back-end's processing system, but will remain inactive until POST /jobs/{job_id}/results has been called. They will run only once and store results after execution. Results can be downloaded. Batch jobs are typically time consuming and user interaction is not possible. This is the only mode that allows to get an estimate about time, volume and costs beforehand.
    2. A more dynamic way of processing and accessing data is to create a secondary web service. They allow web-based access using different protocols such as OGC WMS (opens new window) (Open Geospatial Consortium Web Map Service), OGC WCS (opens new window) (Web Coverage Service) or XYZ tiles (opens new window). These protocols usually allow users to change the viewing extent or level of detail (zoom level). Therefore, computations often run on demand so that the requested data is calculated during the request. Back-ends should make sure to cache processed data to avoid additional/high costs and reduce waiting times for the user.
    3. Process graphs can also be executed on-demand (i.e. synchronously) by sending the process graph to POST /result. Results are delivered with the request itself and no job is created. Only lightweight computations, for example small previews, should be executed using this approach as timeouts are to be expected for long-polling HTTP requests (opens new window).
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    • openEO Documentation

    # openEO Documentation

    This is the openEO Documentation for API versions 0.4. This version is outdated and got replaced by API version 1.0.

    Some useful links to get started:

    Feel encouraged to use the "User Documentation" and "Developers" menu items for further navigation through the documentation.

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    Note: This is the list of all processes specified by the openEO project. Back-ends implement a varying set of processes. Thus, the processes you can use at a specific back-end may derive from the specification, may include non-standardized processes and may not implement all processes listed here. Please check each back-end individually for the processes they support. The client libraries usually have a function called listProcesses or list_processes for that.

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    • User-defined functions

    # User-defined functions

    The abbreviation UDF stands for user-defined function. With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, allowing custom calculations on server-side data.

    UDFs are currently developed and evaluated outside of the core API. More information regarding the current draft for UDFs can be found in a separate repository (opens new window). There is additional documentation available for the UDF Framework (opens new window) and the UDF API (opens new window).

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    # Authentication

    While a couple of openEO operations can be done anonymously, most of the interesting parts of the API require you to identify as a registered user. openEO specifies two ways to authenticate as a user:

    # HTTP Basic

    Let's start with the easiest authentication method, based on the HTTP Basic authentication scheme. It is however not recommended for various reasons, such as its limited security measures. For example, if you are connecting to a back-end with a http:// URL (unencrypted; discouraged in openEO) instead of a https:// one (encrypted), you should certainly not use HTTP Basic authentication.

    With these security related caveats out of the way, you authenticate using your username and password. The clients usually have a authenticate_basic method for this or graphical clients (e.g. QGIS and the Web Editor) will ask for your username and password directly.

    Further Information

    # OpenID Connect

    OpenID Connect (OIDC) is an identity layer on top of the OAuth 2.0 protocol. It is a quite an extensive stack of interacting actors and protocols, and an in-depth discussion of its architecture would lead us too far here. However, in the context of working with openEO, these OpenID Connect concepts are useful to understand:

    • There is decoupling between:

      • the OpenID Connect identity provider (the platform that handles the authentication of the user)
      • the openEO back-end, which manages earth observation collections and executes your algorithms

      openEO back-ends can decide to host their own OpenID Connect infrastructure, but they may also allow to authenticate with an external OpenID Connect provider, which could be an organization like Google or Microsoft. This means that the back-end does not have to take care of all the security and privacy challenges of properly handling user registration, authentication, etc. Also, it allows the user to securely reuse an existing account registered with an established organization, instead of having to register yet another account with some web service.

      Note

      An openEO back-end might support multiple OpenID Connect providers. If there is only one, the openEO client libraries will usually pick it automatically, but otherwise you may need to decide explicitly which provider to authenticate against.

    • Your openEO script or application acts as a so called OpenID Connect client, with an associated client ID. This practically means that, apart from a user account, you need a client ID as well (and often a client secret too) when authenticating.

      The details of how to obtain the client ID and secret largely depend on the back-end and OpenID Connect provider: you might have to register a client yourself, or you might have to use an existing client ID. Consult the openEO back-end (documentation) about how to obtain client ID (and secret).

    • There are several possible "flows" (also called "grants") to complete the whole OpenID Connect authentication dance:

      • Authorization Code Flow
      • Device Flow
      • Client Credentials Flow
      • Resource Owner Password flow
      • Refresh Token Flow

      Picking the right flow highly depends on your use case and context: are you working interactively, are you working in a browser based environment, should your application be able to work without user interaction in the background, what does the OpenID Connect provider support, ...?

    OpenID Connect is clearly more complex than HTTP Basic Authentication. In the sections below we will discuss the practical details of each flow.

    Further Information

    # Authorization Code Flow

    This is the most popular and widely supported OpenID Connect flow in the general web development world. However, it requires an environment that can be hard to get right when using in other environments like a mobile app or a CLI (command line interface). Some prerequisites must be met:

    • You are working interactively (e.g. in a Jupyter notebook, in a Python or R shell or running a script manually)
    • You have access to a web browser (preferably on the same machine as your application), to authenticate with the OpenID Connect provider
    • The web browser has (network) access
    • A URL must be whitelisted in the OpenID client's "redirect URL" configuration at the OpenID Connect provider's side.

    For authentication, the client forwards a user to the log-in page of the OpenID Connect provider. There the user can log in with an existing account (or create a new one) and then generally has to explicitly grant access to basic profile information (e.g. email address) that the back-end will use to identify the user.

    # Device Flow

    The device flow (also called device authorization grant) is a relatively new OpenID Connect flow and it is not as widely supported across different OpenID Connect Providers as the other flows. It provides a nice alternative that is roughly comparable to the authorization code flow but without the previously mentioned issues related to short-living webservers, network access and browser redirects.

    The device flow is only suited for interactive use cases and requires a web browser for the authentication with the OpenID Connect provider. However, it can be any web browser, even one on your mobile phone. There is no networking magic required to be able to access any short-living background webserver like with the authorization code flow.

    The "magic" is that the client will show a message like this:

    To authenticate: visit https://provider.example.com/device
 and enter the user code 'DTNY-KLNX'.

    You should now visit this URL. Usually it is intentionally a short URL to make it feasible to type it instead of copy-pasting it (e.g. on another device). Authenticate with the OpenID Connect provider and enter the user code shown in the message. Meanwhile, the client library is usually actively polling the OpenID Connect provider and when you successfully complete the authentication and entering of the user code, it will receive the necessary tokens for authenticated communication with the back-end.

    # Client Credentials Flow

    The Client Credentials flow directly uses the client ID and secret to authenticate. It does not involve interactive authentication through a web browser, which makes it useful for non-interactive use cases.

    The downside is of the Client Credentials flow is that it can be challenging or even impossible with a given OpenID Connect provider, to set up a client that supports this. Also, your openEO back-end might not allow it, because technically you are authenticating a client, and not a user.

    # Resource Owner Password flow

    With the Resource Owner Password flow you directly pass the user (and client) credentials. Like the Client Credentials flow, it is useful for non-interactive uses cases.

    However, usage of the Resource Owner Password flow is generally discouraged because of its poor security features (e.g. OAuth/OIDC was designed to avoid passing and storing user passwords unnecessarily). It is also not widely supported across OpenID Connect providers, probably due to its weak security measures.

    # Refresh Token Flow

    When OpenID Connect authentication completes successfully, the client receives an access token to be used when doing authenticated calls to the back-end. The access token usually has a short lifetime to reduce the security risk when it would be stolen or intercepted. The client also receives a refresh token that can be used, through the Refresh Token flow, to easily request a new access token, without having to re-authenticate, which makes it useful for non-interactive uses cases.

    However, as it needs an existing refresh token, the Refresh Token Flow requires first to authenticate with one of the other flows (but in practice it might not be required very often because refresh tokens usually have a relatively long lifetime).

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/cookbook/index.html b/documentation/1.0/cookbook/index.html index a8f11a100..48956f50b 100644 --- a/documentation/1.0/cookbook/index.html +++ b/documentation/1.0/cookbook/index.html @@ -8,7 +8,7 @@ - + @@ -740,6 +740,6 @@ // apply to cube var cube_s2_highpass = builder.apply_kernel(cube_s2_b8, highpass)
    A combined edge detection RGB. Sobel vertical and horizontal are displayed as red and green, a 5x5 highpass filter is displayed as blue.
    Above a combined edge detection RGB can be seen. Sobel 3x3 vertical and horizontal edge detections are displayed as red and green, and a 5x5 highpass filter is displayed as blue. For this, all kernels in the code block above were applied and the sucessive cubes were merged afterwards.

    # Endnote

    You have feedback or noticed an error? Feel free to open an issue in the github repository (opens new window) or use the other communication channels (opens new window)

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/datacubes.html b/documentation/1.0/datacubes.html index de6aaabe5..1a3cfa66e 100644 --- a/documentation/1.0/datacubes.html +++ b/documentation/1.0/datacubes.html @@ -8,7 +8,7 @@ - + @@ -198,11 +198,11 @@ Contact

    # Datacubes

    # What are Datacubes?

    Data is represented as datacubes in openEO, which are multi-dimensional arrays with additional information about their dimensionality. Datacubes can provide a nice and tidy interface for spatiotemporal data as well as for the operations you may want to execute on them. As they are arrays, it might be easiest to look at raster data as an example, even though datacubes can hold vector data as well. Our example data however consists of a 6x7 raster with 4 bands [blue, green, red, near-infrared] and 3 timesteps [2020-10-01, 2020-10-13, 2020-10-25], displayed here in an orderly, timeseries-like manner:

    Raster datacube timeseries: 12 imagery tiles are depicted, grouped by 3 dates along a timeline (time dimension). Each date has a blue, green, red and near-infrared band (bands dimension). Each single tile has the dimensions x and y (spatial dimensions).
    An examplary raster datacube with 4 dimensions: x, y, bands and time.

    It is important to understand that datacubes are designed to make things easier for us, and are not literally a cube, meaning that the above plot is just as good a representation as any other. That is why we can switch the dimensions around and display them in whatever way we want, including the view below:

    Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates).
    This is the 'raw' data collection that is our example datacube. The grayscale images are colored for understandability, and dimension labels are displayed.

    A vector datacube on the other hand could look like this:

    An examplary vector datacube with 3 dimensions: 2 geometries are given for the `geometries` dimension, along with 3 timesteps for the temporal dimension `time` and 4 bands in the `bands` dimension.
    An examplary vector datacube with 3 dimensions: 2 geometries are given for the geometries dimension, along with 3 timesteps for the temporal dimension time and 4 bands in the bands dimension.

    Vector datacubes (opens new window) and raster datacubes are common cases of datacubes in the EO domain. A raster datacube has at least two spatial dimensions (usually named x and y) and a vector datacube has at least one geometry dimension (usually named geometry). -The purpose of these distinctions is simply to make it easier to describe "special" cases of datacubes, but you can also define other types such as a temporal datacube that has at least one temporal dimension (usually named t).

    The following additional information are usually available for datacubes:

    • the dimensions (see below)
    • a sampling method (see below)
    • a unit for the values

    All these information are usually provided through the datacube metadata.

    # Dimensions

    A dimension refers to a certain axis of a datacube. This includes all variables (e.g. bands), which are represented as dimensions. Our exemplary raster datacube has the spatial dimensions x and y, and the temporal dimension t. Furthermore, it has a bands dimension, extending into the realm of what kind of information is contained in the cube.

    The following properties are usually available for dimensions:

    • name
    • type (potential types include: spatial (raster or vector data), temporal and other data such as bands)
    • axis (for spatial dimensions) / number
    • labels (usually exposed through textual or numerical representations, in the metadata as nominal values and/or extents)
    • reference system / projection
    • resolution / step size
    • unit for the labels (either explicitly specified or implicitly provided by the reference system)
    • additional information specific to the dimension type (e.g. the geometry types for a dimension containing geometries)

    All these information are usually provided through the datacube metadata.

    Here is an overview of the dimensions contained in our example raster datacube above:

    # name type labels resolution reference system
    1 x spatial 466380, 466580, 466780, 466980, 467180, 467380 200m EPSG:32627 (opens new window)
    2 y spatial 7167130, 7166930, 7166730, 7166530, 7166330, 7166130, 7165930 200m EPSG:32627 (opens new window)
    3 bands bands blue, green, red, nir 4 bands -
    4 t temporal 2020-10-01, 2020-10-13, 2020-10-25 12 days Gregorian calendar / UTC

    Dimension labels are usually either numerical or text (also known as "strings"), which also includes textual representations of timestamps or geometries for example. +The purpose of these distinctions is simply to make it easier to describe "special" cases of datacubes, but you can also define other types such as a temporal datacube that has at least one temporal dimension (usually named t).

    The following additional information are usually available for datacubes:

    • the dimensions (see below)
    • a sampling method (see below)
    • a unit for the values

    All these information are usually provided through the datacube metadata.

    # Dimensions

    A dimension refers to a certain axis of a datacube. This includes all variables (e.g. bands), which are represented as dimensions. Our exemplary raster datacube has the spatial dimensions x and y, and the temporal dimension t. Furthermore, it has a bands dimension, extending into the realm of what kind of information is contained in the cube.

    The following properties are usually available for dimensions:

    • name
    • type (potential types include: spatial (raster or vector data), temporal and other data such as bands)
    • axis (for spatial dimensions) / number
    • labels (usually exposed through textual or numerical representations, in the metadata as nominal values and/or extents)
    • reference system / projection
    • resolution / step size
    • unit for the labels (either explicitly specified or implicitly provided by the reference system)
    • additional information specific to the dimension type (e.g. the geometry types for a dimension containing geometries)

    All these information are usually provided through the datacube metadata.

    Here is an overview of the dimensions contained in our example raster datacube above:

    # name type labels resolution reference system
    1 x spatial 466380, 466580, 466780, 466980, 467180, 467380 200m EPSG:32627 (opens new window)
    2 y spatial 7167130, 7166930, 7166730, 7166530, 7166330, 7166130, 7165930 200m EPSG:32627 (opens new window)
    3 bands bands blue, green, red, nir 4 bands -
    4 t temporal 2020-10-01, 2020-10-13, 2020-10-25 12 days Gregorian calendar / UTC

    Dimension labels are usually either numerical or text (also known as "strings"), which also includes textual representations of timestamps or geometries for example. For example, temporal labels are usually encoded as ISO 8601 (opens new window) compatible dates and/or times and similarly geometries can be encoded as Well-known Text (WKT) (opens new window) or be represented by their IDs.

    Dimensions with a natural/inherent order (usually all temporal and spatial raster dimensions) are always sorted. Dimensions without inherent order (usually bands), retain the order in which they have been defined in metadata or processes (e.g. through filter_bands (opens new window)), with new labels simply being appended to the existing labels.

    A geometry dimension is not included in the example raster datacube above and it is not used in the following examples, but to show how a vector dimension with two polygons could look like:

    name type labels reference system
    geometry vector POLYGON((-122.4 37.6,-122.35 37.6,-122.35 37.64,-122.4 37.64,-122.4 37.6)), POLYGON((-122.51 37.5,-122.48 37.5,-122.48 37.52,-122.51 37.52,-122.51 37.5)) EPSG:4326 (opens new window)

    A dimension with geometries can consist of points, linestrings, polygons, multi points, multi linestrings, or multi polygons. It is not possible to mix geometry types, but the single geometry type with their corresponding multi type can be combined in a dimension (e.g. points and multi points). Empty geometries (such as GeoJSON features with a null geometry or GeoJSON geometries with an empty coordinates array) are allowed and can sometimes also be the result of certain vector operations such as a negative buffer.

    # Applying Processes on Dimensions

    Some processes are typically applied "along a dimension". You can imagine said dimension as an arrow and whatever is happening as a parallel process to that arrow. It simply means: "we focus on this dimension right now".

    # Resolution

    The resolution of a dimension gives information about what interval lies between observations. This is most obvious with the temporal resolution, where the intervals depict how often observations were made. Spatial resolution gives information about the pixel spacing, meaning how many 'real world meters' are contained in a pixel. The number of bands and their wavelength intervals give information about the spectral resolution.

    # Coordinate Reference System as a Dimension

    In the example above, x and y dimension values have a unique relationship to world coordinates through their coordinate reference system (CRS). This implies that a single coordinate reference system is associated with these x and y dimensions. If we want to create a data cube from multiple tiles spanning different coordinate reference systems (e.g. Sentinel-2: different UTM zones), we would have to resample/warp those to a single coordinate reference system. In many cases, this is wanted because we want to be able to look at the result, meaning it is available in a single coordinate reference system.

    Resampling is however costly, involves (some) data loss, and is in general not reversible. Suppose that we want to work only on the spectral and temporal dimensions of a data cube, and do not want to do any resampling. In that case, one could create one data cube for each coordinate reference system. An alternative would be to create one single data cube containing all tiles that has an additional dimension with the coordinate reference system. In that data cube, x and y no longer point to a unique world coordinate, because identical x and y coordinate pairs occur in each UTM zone. Now, only the combination (x, y, crs) has a unique relationship to the world coordinates.

    On such a crs-dimensioned data cube, several operations make perfect sense, such as apply or reduce_dimension on spectral and/or temporal dimensions. A simple reduction over the crs dimension, using sum or mean would typically not make sense. The "reduction" (removal) of the crs dimension that is meaningful involves the resampling/warping of all sub-cubes for the crs dimension to a single, common target coordinate reference system.

    # Values in a datacube

    openEO datacubes contain scalar values (e.g. strings, numbers or boolean values), with all other associated attributes stored in dimensions (e.g. coordinates or timestamps). Attributes such as the CRS or the sensor can also be turned into dimensions. Be advised that in such a case, the uniqueness of pixel coordinates may be affected. When usually, (x, y) refers to a unique location, that changes to (x, y, CRS) when (x, y) values are reused in other coordinate reference systems (e.g. two neighboring UTM zones).

    Be Careful with Data Types

    As stated above, datacubes only contain scalar values. However, implementations may differ in their ability to handle or convert them. Implementations may also not allow mixing data types in a datacube. For example, returning a boolean value for a reducer on a numerical datacube may result in an error on some back-ends. The recommendation is to not change the data type of values in a datacube unless the back-end supports it explicitly.

    Data cube values can be sampled in two different ways. The values are either area or point samples.

    • Area sampling aggregates measurements over defined regions, i.e. the grid cells for raster data or polygons/lines for vector data.
    • Point sampling collects data at specific locations, providing detailed information for specific points.

    # Processes on Datacubes

    In the following part, the basic processes for manipulating datacubes are introduced.

    # Filter

    When filtering data (e.g. filter_spatial (opens new window), filter_temporal (opens new window), filter_bands (opens new window)), only the data that satisfies a condition is returned. For example, this condition could be a timestamp or interval, (a set of) coordinates, or specific bands. By applying filtering the datacube becomes smaller, according to the selected data.

    Simplified

    filter([🌽, 🥔, 🐷], isVegetarian) => [🌽, 🥔]

    In the image, the example datacube can be seen at the top with labeled dimensions. The filtering techniques are displayed separately below. On the left, the datacube is filtered temporally with the interval ["2020-10-15", "2020-10-27"]. The result is a cube with only the rasters for the timestep that lies within that interval ("2020-10-25") and unchanged bands and spatial dimensions. Likewise, the original cube is filtered for a specific band ["nir"] in the middle and a specific spatial region [Polygon(...)] on the right.

    Datacube filtering: From the datacube 4 by 3 grid, arrows depict what happens if the grid is filtered. Temporal filtering results in data for one timestep with all four bands, filtering bands results in data with one band with all three timesteps, and spatial filtering results in all timesteps and bands being preserved, but all with a smaller area.
    Filtering the sample datacube. It is displayed at the top with dimensions labels. Filtered results are shown at the bottom.

    # Apply

    The apply* functions (e.g. apply (opens new window), apply_neighborhood (opens new window), apply_dimension (opens new window)) employ a process on the datacube that calculates new pixel values for each pixel, based on n other pixels. Please note that several programming languages use the name map instead of apply, but they describe the same type of function.

    Simplified

    apply([🌽, 🥔, 🐷], cook) => [🍿, 🍟, 🍖]

    For the case n = 1 this is called a unary function and means that only the pixel itself is considered when calculating the new pixel value. A prominent example is the absolute() function, calculating the absolute value of the input pixel value.

    Datacube apply unary: 3 example tiles hold values below and above 0. after applying the process 'absolute', all values in the three example tiles have changed to their absolute values above 0.
    Applying an unary process. Only the pixel itself is considered for calculating the new pixel value.

    If n is larger than 1, the function is called n-ary. In practice, this means that the pixel neighbourhood is taken into account to calculate the new pixel value. Such neighbourhoods can be of spatial and/or temporal nature. A spatial function works on a kernel that weights the surrounding pixels (e.g. smoothing values with nearby observations), a temporal function works on a time series at a certain pixel location (e.g. smoothing values over time). Combinations of types to n-dimensional neighbourhoods are also possible.

    In the example below, an example weighted kernel (shown in the middle) is applied to the cube (via apply_kernel (opens new window)). To avoid edge effects (affecting pixels on the edge of the image with less neighbours), a padding has been added in the background.

    Datacube apply spatial kernel: Three example tiles hold some values with a lot of variance. A spatial kernel (a cell plus it's 4 direct neighbours) is applied to all pixels, and the result appears to be spatially smoothed, with less variance.
    Applying a spatial kernel. For calculating each new pixel value, the defined weighted neighbourhood is used.

    Of course this also works for temporal neighbourhoods (timeseries), considering neighbours before and after a pixel. To be able to show the effect, two timesteps were added in this example figure. A moving average of window size 3 is then applied, meaning that for each pixel the average is calculated out of the previous, the next, and the timestep in question (tn-1, tn and tn+1). No padding was added which is why we observe edge effects (NA values are returned for t1 and t5, because their temporal neighbourhood is missing input timesteps).

    Datacube apply temporal moving average: Smoothing is applied to 5 example tiles by calculating the mean of 3 timesteps of every single pixel. The resulting tiles for the timestamps look much more alike.
    Applying a moving average (temporal smoothing) by averaging the direct temporal neighbourhoods of pixels. No padding is used, which leads to edge effects.

    Alternatively, a process can also be applied along a dimension of the datacube, meaning the input is no longer a neighbourhood of some sort but all pixels along that dimension (n equals the complete dimension). If a process is applied along the time dimension (e.g. a breakpoint detection), the complete pixel timeseries are the input. If a process is applied along the spatial dimensions (e.g. a mean), all pixels of an image are the input. The process is then applied to all pixels along that dimension and the dimension continues to exist. This is in contrast to reduce. In the image below, a mean is applied to the time dimension. An example pixel timeseries is highlighted by a green line and processed step-by-step.

    Datacube apply dimension time: The mean of all 5 timesteps is calculated for every single pixel. The resulting 5 tiles look exaclty the same, as they have been averaged.
    Applying a mean to the temporal dimension.

    # Resample

    In a resampling processes (e.g. resample_cube_spatial (opens new window), resample_cube_temporal (opens new window)), the layout of a certain dimension is changed into another layout, most likely also changing the resolution of that dimension. This is done by mapping values of the source (old) datacube to the new layout of the target (new) datacube. During that process, resolutions can be upscaled or downscaled (also called upsampling and downsampling), depending on whether they have a finer or a coarser spacing afterwards. A function is then needed to translate the existing data into the new resolution. A prominent example is to reproject a datacube into the coordinate reference system of another datacube, for example in order to merge the two cubes.

    Simplified

    resample(🖼️, downscale) => 🟦

    resample(🌍, reproject) => 🗺️

    The first figure gives an overview of temporal resampling. How exactly the input timesteps are rescaled to the output timesteps depends on the resampling function.

    Datacube temporal resampling (up and down): Downsampling: To a timeline-representation of the example tiles, another timeline with only 2 steps at different dates is applied. The result has tiles only at those new timesteps. In Upsampling, the existing 3 timesteps are sampled into 5 result timesteps.
    Temporal downsampling is seen on the left, upsampling on the right. The temporal layout that the cubes are resampled to is displayed in the middle.

    The second figure displays spatial resampling. Observe how in the upsampling process, the output datacube has not gained in information value. The resulting grid still carries the same pixel information, but in higher spatial resolution. Other upsampling methods may yield smoother results, e.g. by using interpolation.

    Datacube spatial resampling (up and down): Downsampling: The resulting tiles have a lower spatial resolution than the input tiles. Upsampling: The resulting tiles have a higher spatial resolution than the input tiles, but contain the same image than before (no information added).
    Spatial resampling. The geometry which the input cube is resampled to is displayed in the middle. The output cube then contains the same information, but in the resampled spatial layout.

    # Reduce

    The reduce_dimension (opens new window) process collapses a whole dimension of the datacube. It does so by using some sort of reducer, which is a function that calculates a single result from an amount of values, as e.g. mean(), min() and max() are. For example we can reduce the time dimension (t) of a timeseries by calculating the mean value of all timesteps for each pixel. We are left with a cube that has no time dimension, because all values of that dimension are compressed into a single mean value. The same goes for e.g. the spatial dimensions: If we calculate the mean along the x and y dimensions, we are left without any spatial dimensions, but a mean value for each instance that previously was a raster is returned. In the image below, the dimensions that are reduced are crossed out in the result.

    Simplified

    reduce([🥬, 🥒, 🍅, 🧅], prepare) => 🥗

    Think of it as a waste press that does math instead of using brute force. Given a representation of our example datacube, let's see how it is affected.

    Datacube reduce: Three arrows depict what happens to the 12 example tiles, if they are reduced: Reducing timesteps leads to four tiles (one for each band), and the time dimension is deleted. Reducing bands lead to one tile per timestep, and the bands dimension is deleted. Reducing spatially leads to the original 4 by 3 bands by time layout, but the result has no spatial dimension and thus, the tiles have been turned into single values, per tile.
    Overview of reducing the sample datacube. Dimensions that are reduced are crossed out in the result images. Their information value has been compressed by a reducer function.

    # Aggregate

    An aggregation of a datacube can be thought of as a grouped reduce. That means it consists of two steps:

    1. Grouping via a grouping variable, i.e. spatial geometries or temporal intervals
    2. Reducing these groups along the grouped dimension with a certain reducer function, e.g. calculating the mean pixel value per polygon or the maximum pixel values per month

    While the layout of the reduced dimension is changed, other dimensions keep their resolution and geometry. But in contrast to pure reduce, the dimensions along which the reducer function is applied still exist after the operation.

    Simplified

    aggregate(👪 👩‍👦 👨‍👩‍👦‍👦, countFamilyMembers) => [3️⃣, 2️⃣, 4️⃣]

    A temporal aggregation (e.g. aggregate_temporal (opens new window)) is similar to the downsampling process, as it can be seen in the according image above. Intervals for grouping can either be set manually, or periods can be chosen: monthly, yearly, etc. All timesteps in an interval are then collapsed via a reducer function (mean, max, etc.) and assigned to the given new labels.

    A spatial aggregation (e.g. aggregate_spatial (opens new window)) works in a similar manner. Polygons, lines and points can be selected for grouping. Their spatial dimension is then reduced by a given process and thus, a vector cube is returned. The vector cube then has dimensions containing features, attributes and time. In the graphic below, the grouping is only shown for the first timestep.

    Datacube spatial aggregation: A line and a polygon are selected from the original example tiles. The pixels covered by these geometries are aggregated and the result consists no longer of imagery tiles but of an array with values for 2 geometries by 4 bands by 3 timesteps.
    When aggregating spatially, pixels are grouped (cut out) based on geometries, and then collapsed with a reducer function. A vector datacube is returned (as shown with dimensions names and labels). Please note that this visualization shows aggregation on a data cube with four dimensions, but aggregate_spatial specifically can only handle data cubes with three dimensions as of now.
    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/datacubes/index.html b/documentation/1.0/datacubes/index.html index 58620f660..535658c08 100644 --- a/documentation/1.0/datacubes/index.html +++ b/documentation/1.0/datacubes/index.html @@ -8,7 +8,7 @@ - + @@ -197,6 +197,6 @@
    Contact

    • Datacube Documentation

    # Datacube Documentation

    This documentation on datacubes is part of the openEO documentation.

    The folder .scripts contains script*s that output the figures contained in this directory.

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/developers/api/errors.html b/documentation/1.0/developers/api/errors.html index fa8e556c5..a883565ac 100644 --- a/documentation/1.0/developers/api/errors.html +++ b/documentation/1.0/developers/api/errors.html @@ -8,7 +8,7 @@ - + @@ -201,6 +201,6 @@

    • openEO error codes

    # openEO error codes

    The general error handling in the openEO API is documented in the API documentation.

    The following table of error codes is incomplete. These error codes will evolve over time. If you are missing any common error, please contribute it by adding an issue (opens new window), creating a pull request (opens new window) or get in contact.

    The whole table of error codes is available as JSON file, which can be used by implementors to automatically generate error responses.

    Account Management

    openEO Error Code Description Example Message HTTP Status Code
    AuthenticationRequired The client did not provide any authentication details for a resource requiring authentication or the provided authentication details are not correct. Unauthorized. 401
    AuthenticationSchemeInvalid Invalid authentication scheme (e.g. Bearer). Authentication method not supported. 403
    TokenInvalid Authorization token has expired or is invalid. Please authenticate again. 403
    CredentialsInvalid Credentials are not correct. 403
    PermissionsInsufficient Forbidden. The client did provided correct authentication details, but the privileges/permissions of the provided credentials do not allow to request the resource. Forbidden. The permissions of the authenticated account do not allow to request the requested resource. 403

    Batch Jobs

    openEO Error Code Description Example Message HTTP Status Code
    BudgetInvalid The budget is too low as it is either smaller than or equal to 0 or below the costs. The specified budget is too low. 400
    EstimateComplexity The process is too complex to calculate an estimate, e.g. due to a UDF or other processes that are complex to estimate costs reliably. The process is too complex to calculate an estimate. 500
    NoDataForUpdate For PATCH requests: No valid data specified at all. No data specified to be updated. 400
    PropertyNotEditable For PATCH requests: The specified parameter can't be updated. It is read-only. The specified property '{property}' is read-only. 400
    StorageFailure Server couldn't store file(s) due to server-side reasons. Unable to store files due to a server error. Please try again later or contact our support. 500
    StorageQuotaExceeded The storage quota has been exceeded by the user. Your storage quota has been exceeded. 400
    ProcessInvalid The process given is invalid, which ususlly means that the process metadata is invalid. Invalid process specified. 400
    ProcessGraphMissing The process doesn't contain a process graph. For jobs, services, and sync. processing the parameter `process` must contain a `process_graph`. Invalid process specified. It doesn't contain a process graph. 400
    ProcessGraphInvalid The process doesn't contain a valid process graph, which means it doesn't comply to the general structure / schema. Invalid process graph specified. 400
    JobNotFound The requested job does not exist. The batch job '{identifier}' does not exist. 404
    JobLocked The job is currently locked due to a running batch computation and can't be modified meanwhile. Batch job is locked due to a queued or running batch computation. 400
    JobNotFinished Batch job has not finished computing the results yet. Please try again later or contact our support. 400
    JobNotStarted Job has not been queued or started yet or was canceled and not restarted by the user. Batch job must be started first. 400
    ResultLinkExpired The signed URLs for batch job results have expired. Please send a request to `GET /jobs/{job_id}/results` to refresh the links. The link to the batch job result has expired. Please request the results again. 410
    PaymentRequired The budget required to fulfil the request is insufficient. The budget required to fulfil the request is not sufficient. A payment is required first. 402
    BillingPlanInvalid The billing plan is not on the list of available plans. The billing plan is invalid. 400
    BillingPlanMissing No billing plan has been specified by the user and the billing plan can't be determined unambiguously. A billing plan must be specified. 400

    Data Processing

    openEO Error Code Description Example Message HTTP Status Code
    RequestTimeout The request took too long and timed out. Request timed out. 408
    BudgetInvalid The budget is too low as it is either smaller than or equal to 0 or below the costs. The specified budget is too low. 400
    ProcessInvalid The process given is invalid, which ususlly means that the process metadata is invalid. Invalid process specified. 400
    ProcessGraphMissing The process doesn't contain a process graph. For jobs, services, and sync. processing the parameter `process` must contain a `process_graph`. Invalid process specified. It doesn't contain a process graph. 400
    ProcessGraphInvalid The process doesn't contain a valid process graph, which means it doesn't comply to the general structure / schema. Invalid process graph specified. 400
    ProcessGraphComplexity The process graph is too complex for synchronous processing and will likely time out. Please use a batch job instead. The process is too complex for for synchronous processing. Please use a batch job instead. 400
    ProcessUnsupported A process (predefined or user-defined) with the specified identifier is not available. To be used when validating or executing process graphs. Process with identifier '{process}' is not available in namespace '{namespace}'. 400
    ProcessParameterUnsupported Process '{process}' does not support parameter '{parameter}'. 400
    ProcessParameterInvalid The value passed for parameter '{parameter}' in process '{process}' is invalid: {reason} 400
    ProcessParameterRequired Process '{process}' parameter '{parameter}' is required. 400
    BillingPlanInvalid The billing plan is not on the list of available plans. The billing plan is invalid. 400
    BillingPlanMissing No billing plan has been specified by the user and the billing plan can't be determined unambiguously. A billing plan must be specified. 400

    EO Data Discovery

    openEO Error Code Description Example Message HTTP Status Code
    CollectionNotFound The requested collection does not exist. Collection '{identifier}' does not exist. 404

    File Management

    openEO Error Code Description Example Message HTTP Status Code
    StorageFailure Server couldn't store file(s) due to server-side reasons. Unable to store files due to a server error. Please try again later or contact our support. 500
    StorageQuotaExceeded The storage quota has been exceeded by the user. Your storage quota has been exceeded. 400
    FileNotFound The requested file does not exist. File '{file}' does not exist. 404
    FilePathInvalid The specified path is invalid or not accessible. Path could contain invalid characters, point to an existing folder or a location outside of the user folder. File path is invalid: {reason} 400
    FileOperationUnsupported The file operation is not supported for the specified path. 400
    FolderOperationUnsupported The specified path is a folder and the operation is only supported for files. Operation is only supported for files, not folders. 400
    ContentTypeInvalid The specified media (MIME) type used in the Content-Type header is not allowed. The media type is not supported. Allowed: {types} 400
    FileTypeInvalid File format or file extension is not allowed. File format {type} not allowed. Allowed file formats: {types} 400
    FileSizeExceeded File exceeds allowed maximum file size. File size it too large. Maximum file size: {size} 400
    FileContentInvalid The content of the file is invalid. File content is invalid. 400
    FileLocked The file is locked by a running job or another process. File '{file}' is locked by another process. 400

    General

    openEO Error Code Description Example Message HTTP Status Code
    Internal An internal server error with a proprietary message. Server error: {message} 500
    NotFound To be used if the requested resource does not exist. Note: There are specialized errors for missing jobs (JobNotFound), files (FileNotFound), etc. Unsupported endpoints MAY send an 'FeatureUnsupported' (501) error. Resource not found. 404
    FeatureUnsupported The back-end responds with this error whenever an endpoint is specified in the openEO API, but is not supported. Feature not supported. 501
    InfrastructureMaintenance Service is currently not available as the infrastructure is currently undergoing maintenance work. Service is not available at the moment due to maintenance work. Please try again later or contact our support. 503
    InfrastructureBusy Service is generally available, but the infrastructure can't handle it at the moment as too many requests are processed. Service is not available at the moment due to overloading. Please try again later or contact our support. 503
    UnsupportedApiVersion The service doesn't support the openEO API version specified in the request URL. Clients should check well-known document for supported versions. The requested API version '{version}' is not supported. 404
    RequestTimeout The request took too long and timed out. Request timed out. 408
    ContentTypeInvalid The specified media (MIME) type used in the Content-Type header is not allowed. The media type is not supported. Allowed: {types} 400

    Secondary Services

    openEO Error Code Description Example Message HTTP Status Code
    BudgetInvalid The budget is too low as it is either smaller than or equal to 0 or below the costs. The specified budget is too low. 400
    NoDataForUpdate For PATCH requests: No valid data specified at all. No data specified to be updated. 400
    PropertyNotEditable For PATCH requests: The specified parameter can't be updated. It is read-only. The specified property '{property}' is read-only. 400
    ProcessInvalid The process given is invalid, which ususlly means that the process metadata is invalid. Invalid process specified. 400
    ProcessGraphMissing The process doesn't contain a process graph. For jobs, services, and sync. processing the parameter `process` must contain a `process_graph`. Invalid process specified. It doesn't contain a process graph. 400
    ProcessGraphInvalid The process doesn't contain a valid process graph, which means it doesn't comply to the general structure / schema. Invalid process graph specified. 400
    PaymentRequired The budget required to fulfil the request is insufficient. The budget required to fulfil the request is not sufficient. A payment is required first. 402
    BillingPlanInvalid The billing plan is not on the list of available plans. The billing plan is invalid. 400
    BillingPlanMissing No billing plan has been specified by the user and the billing plan can't be determined unambiguously. A billing plan must be specified. 400
    ServiceNotFound The requested secondary service does not exist. Service '{identifier}' does not exist. 404
    ServiceUnsupported Service type '{type}' is not supported. 400
    ServiceConfigUnsupported Refers to the secondary service `configuration` object. Service parameter '{parameter}' is not supported. 400
    ServiceConfigInvalid Refers to the secondary service `configuration` object. The value passed for the service parameter '{parameter}' is invalid: {reason} 400
    ServiceConfigRequired Refers to the secondary service `configuration` object. Service parameter '{parameter}' is required. 400

    User-Defined Processes

    openEO Error Code Description Example Message HTTP Status Code
    ProcessGraphNotFound The requested user-defined process does not exist. To be used for all endpoints starting with `/process_graphs`. User-defined process '{identifier}' does not exist. 404
    ProcessInvalid The process given is invalid, which ususlly means that the process metadata is invalid. Invalid process specified. 400
    ProcessGraphMissing The process doesn't contain a process graph. For jobs, services, and sync. processing the parameter `process` must contain a `process_graph`. Invalid process specified. It doesn't contain a process graph. 400
    ProcessGraphInvalid The process doesn't contain a valid process graph, which means it doesn't comply to the general structure / schema. Invalid process graph specified. 400
    PredefinedProcessExists If a user wants to store a user-defined process with the id of a predefined process. A predefined process with the given identifier exists. 400
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    - + diff --git a/documentation/1.0/developers/arch.html b/documentation/1.0/developers/arch.html index 9b9fc253a..a8d9d5140 100644 --- a/documentation/1.0/developers/arch.html +++ b/documentation/1.0/developers/arch.html @@ -8,7 +8,7 @@ - + @@ -201,6 +201,6 @@

    • Architecture

    # Architecture

    The openEO API defines a language for how clients communicate to back-ends in order to analyze large Earth observation datasets. The API will be implemented by drivers for specific back-ends. Some first architecture considerations are listed below.

    1. The openEO API is a contract between clients and back-ends that describes the communication only
    2. Each back-end runs its own API instance including the specific back-end driver. There is no API instance that runs more than one driver.
    3. Clients in R, Python, and JavaScript connect directly to the back-ends and communicate with the back-ends over HTTPS according to the openEO API specification.
    4. API instances can run on back-end servers or additional intermediate layers, which then communicate to back-ends in a back-end specific way.
    5. Back-ends may add functionality and extend the API wherever there is need.
    6. There will be a central back-end registry service (openEO Hub), to allow users to search for back-ends with specific functionality and or data.
    7. The openEO API may define profiles in order to group specific functionality.

    Architecture - openEO API shown in dark blue

    # Microservices

    To simplify and structure the development, the API is divided into a few microservices.

    Microservice Description
    Capabilities This microservice reports on the capabilities of the back-end, i.e. which API endpoints are implemented, which authentication methods are supported, and whether and how UDFs can be executed at the back-end.
    EO Data Discovery Describes which collections are available at the back-end.
    Process Discovery Provides services to find out which processes a back-end provides, i.e., what users can do with the available data.
    Data Processing Organizes and manages data processing on the back-end, either as synchronous on-demand computation or batch jobs.
    File Storage Organizes and manages user-uploaded files.
    User-Defined Processes Organizes and manages user-defined processes (process graphs).
    Secondary Services External web services to access data and job results such as a OGC WMTS service.
    Account Management User management, accounting and authentication.
    UDF Runtime Execution of user-defined functions, not part of the Core API. See the UDF documentation for more information.
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    # Getting started for service (back-end) providers

    As a back-end provider who wants to provide its datasets, processes and infrastructure to a broader audience through a standardized interface you may want to implement a driver for openEO.

    First of all, you should go through the list of openEO repositories (opens new window) and check whether there is already a back-end driver that suits your needs. In this case you don't need to develop your own driver, but "only" need to ingest your data, adopt your required processes and set up the infrastructure. Please follow the documentation for the individual driver you want to use.

    If your preferred technology has no back-end driver yet, you may consider writing your own driver. All software written for openEO should follow the software development guidelines.

    You certainly need to understand the glossary, the architecture of openEO and the concepts behind processes and process graphs. This helps you read and understand the API specification. Technical API related documents like CORS and error handing should be read, too.

    If you do not want to start from scratch, you could try to generate a server stub from the OpenAPI 3.0 (opens new window)-based API specification with the OpenAPI Generator (opens new window). If you are using Python to implement your driver you may reuse some common modules of the existing driver implementations:

    You can implement a back-end in iterations. It is recommended to start by implementing the Capabilities microservice. EO Data Discovery, Process Discovery are important for the client libraries to be available, too. Afterwards you should implement Batch Job Management or synchronous data processing. All other microservices can be added later and are not strictly required to run openEO services. Keep in mind that you don't need to implement all endpoints in the first iteration and that you can specify in the Capabilities, which endpoints you are supporting.

    For example, you could start by implementing the following endpoints in the first iteration:

    • Well-Known Document: GET /.well-known/openeo
    • Capabilities: GET / and GET /file_formats
    • Data discovery: GET /collections and GET /collections/{collection_id}
    • Process discovery: GET /processes
    • Data processing: POST /result
    • Authentication (if required): GET /credentials/basic

    A more detailed recommendation about the endpoints and processes to implement can be found in the API and Processes profiles.

    Afterwards you can already start experimenting with your first process graphs and process EO data with our client libraries on your back-end.

    More information will follow soon, for example about back-end compliance testing.

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    Also consider that next to the openEO API, there's also a community of open source backend implementations. So if you have the skills to optimize processing pipelines to perfection, why don't you consider contributing to a backend that matches your preferred technology stack?

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    If you have any questions, please contact us.

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    # Getting started for client developers

    For easy access to openEO back-ends it is essential to provide client libraries for users in their well-known programming languages or working environments. This can be either a client library for a specific programming language that hides the technical details of the openEO API or an application with a user interface, e.g. a GIS software plugin or a web-based tool. All software written for openEO should follow the software development guidelines.

    # Client library developers

    If your preferred programming language is not part of the available client libraries you may consider writing your own client library. Our client libraries are basically translating the openEO API into native concepts of the programming languages. Working with openEO should feel like being a first-class citizen (opens new window) of the programming language.

    Get started by reading the guidelines to develop client libraries, which have been written to ensure the client libraries provide a consistent feel and behavior across programming languages. You certainly need to understand the glossary and the concepts behind processes and process graphs. This helps you understand the API specification and related documents.

    If you do not want to start from scratch, you could try to generate a client library stub from the OpenAPI 3.0 (opens new window)-based API specification with the OpenAPI Generator (opens new window). Make sure the generated code complies to the client library guidelines mentioned above.

    More information will follow soon, for example about client testing.

    # Applications and Software plugins

    Standalone applications and software plugins written in a certain programming language could use the existing client libraries to facilitate access to openEO back-ends. Web applications potentially could use the JavaScript client (opens new window) to access openEO back-ends. Back-Ends may also provide standardized web interfaces such as OGC WMS or OGC WCS to access processed EO data.

    More information will follow soon...

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    # Introduction

    The openEO API defines a HTTP API that lets cloud back-ends with large Earth observation datasets communicate with front end analysis applications in an interoperable way.

    As an overview, the openEO API specifies how to

    • discover which Earth observation data and processes are available at cloud back-ends,
    • execute (chained) processes on back-ends,
    • run user-defined functions (UDFs) on back-ends where UDFs can be exposed to the data in different ways,
    • download (intermediate) results, and
    • manage user content including billing.

    The API is defined as an OpenAPI 3.0 (opens new window) YAML file.

    The API and processes are grouped into compliance profiles.

    # Additional information

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    # API Profiles

    The following image provides an overview of the openEO API profiles. The minimal profile that allows an openEO API implementation to be called "openEO-compliant" is L1 Minimal with additionally compliance to at least of L1A, L1B, or L1C.

    The hierarchy of openEO API profiles: L1 Minimal -> L1A/B/C -> L2 Recommended -> L3 Advanced (with sub-profiles) -> L4 Above and Beyond
    An overview of the openEO API profiles.
    • L1 Minimal ensures that the openEO implementation has a minimal set of functionality which allow users to execute basic use-cases.
    • L2 Recommended add functionality that we recommend to be available so that users can more effectively make use of the openEO implementation.
    • L3 Advanced allows users to execute more advanced use cases. The sub-profiles can add a specific set of functionalities centered around specific needs:
      • L3-UDF adds support for User-Defined Functions (users can run custom source code)
      • L3-FS adds support for File Storage (users can upload, use, and delete custom files)
      • L3-SWS adds advanced suppoort for Secondary Web Services (e.g. on-demand processing through a tiling service such as OGC WMTS)
    • L4 Above and Beyond adds very specific and complex requirements for very specific and highly advanced use cases. Additionally, there are also various API extensions defined in the API specification, which are not listed as part of the profiles.

    In the following chapter you can find a list of requirements per profile, grouped by categories.

    # Requirements per Profile

    # L1: Minimal

    # API fundamentals

    # Functionality Description
    1 All > CORS OPTIONS method with all headers
    2 All > CORS Access-Control-Allow-Origin for GET, POST, PUT, PATCH, DELETE
    3 All > CORS Access-Control-Expose-Headers for GET, POST, PUT, PATCH, DELETE
    4 All > HTTPS HTTPS supported
    5 All > UTF-8 Charset is UTF-8 for all requests and responses
    6 All > Error Handling Returns HTTP status codes between 400 and 599 for errors
    7 All > Error Handling Returns a JSON object with at least code and message properties
    13 All > Process Graphs Callback support

    # Well-known Discovery

    # Functionality Description
    21 GET /.well-known/openeo Valid response with at least url and api_version per instance (without authentication)

    # Capabilities

    # Functionality Description
    30 GET / Valid response with at least api/backend/stac_version, id, title, description, endpoints and links (w/o auth)
    31 GET / > endpoints All implemented endpoints listed with methods
    32 GET / > endpoints No endpoints listed that are not implemented or faulty
    33 GET / > api_version Implements openEO API version 1.2.0 (or 1.1.0)
    34 GET / > stac_version Implements STAC version 1.0.0 (or 0.9.0)

    # File Formats

    # Functionality Description
    40 GET /file_formats Valid response with input and output properties
    41 GET /file_formats Works without authentication
    42 GET /file_formats Works with authentication
    43 GET /file_formats > output At least one output file format available
    46 GET /file_formats File format names get accepted case Insensitive
    48 GET /file_formats Each format has at least gis_data_types and parameters

    # Authentication

    # Functionality Description
    100 Bearer token follows the specified format (method/identityProviderId/token)
    101 All that require a Bearer token > Error Handling Returns HTTP status codes 401 if no authentication details are provided
    102 All that require a Bearer token > Error Handling Returns HTTP status codes 403 if invalid credentials or credentials with insufficient permissions are provided
    110 GET /credentials/* At least one authentication method is supported (HTTP Basic or OIDC)

    # Pre-defined Processes

    # Functionality Description
    200 GET /processes Valid response with at least processes and links as arrays
    201 GET /processes Works without authentication
    202 GET /processes Works with authentication
    204 GET /processes > limit parameter All processes are returned if no limit parameter is provided
    209 GET /processes > processes Missing properties in the process objects are not set to null if not valid according to the OpenAPI schema

    # Collections

    # Functionality Description
    300 GET /collections Valid response with at least collections and links as arrays
    301 GET /collections Works without authentication
    302 GET /collections Works with authentication
    303 GET /collections Supports a valid STAC version (0.9.0 or later, preferrably 1.0.0).
    306 GET /collections > limit parameter All collections are returned if no limit parameter is provided
    307 GET /collections > collections All collections are valid according to the STAC specification 0.9 or later (stac_version, id, description, license, extent and links are required)
    313 GET /collections > collections Missing properties in the collection objects are not set to null if not valid according to the OpenAPI schema
    320 GET /collections/{id} Is supported
    321 GET /collections/{id} Response is valid according to the STAC specification 0.9 or later (additionally, cube:dimensions and summaries are required)
    322 GET /collections/{id} Works without authentication
    323 GET /collections/{id} Works with authentication
    326 GET /collections/{id} > cube:dimensions All collections provide their actual data cube dimensions

    # Data Processing

    # Functionality Description
    700 status Job follows described status changes (see description of status property in GET /jobs/{id})
    701 At least one of batch jobs, secondary web services or synchronous processing is implemented

    # L1A: Minimal - Synchronous Processing

    # Synchronous Processing

    # Functionality Description
    900 POST /result Is supported (with authentication)
    901 POST /result > process Only accepts valid process submissions
    905 POST /result Returns HTTP status 200 on success with the data in the response body, HTTP status codes 4XX or 5XX on failure with valid response body
    906 POST /result > Content-Type header Returns suitable media type for the requested file format (not always application/octet-stream) in the Content-Type header
    910 POST /result Rejects processing if a payment is required.

    # L1B: Minimal - Batch Jobs

    # Batch Jobs

    # Functionality Description
    710 GET /jobs Is supported (with authentication)
    711 GET /jobs Valid response with at least jobs and links as arrays
    713 GET /jobs > limit parameter All jobs are returned if no limit parameter is provided
    714 GET /jobs > jobs The served job metadata is valid according to the specification (id, status and created are required)
    715 GET /jobs > jobs title is provided for all jobs (if provided by the user)
    730 POST /jobs Supports creating batch jobs (with authentication)
    731 POST /jobs > process Only accepts valid process submissions
    734 POST /jobs Supports storing title and description
    736 POST /jobs Returns HTTP status 201 and OpenEO-Identifier + Location header if successful
    737 POST /jobs > status Job status is "created" after successful creation of the job, the data processing does not start yet
    750 GET /jobs/{id} Is supported (with authentication)
    751 GET /jobs/{id} Returns at least id, process, status and created
    752 GET /jobs/{id} title and description are available if provided by the user
    754 GET /jobs/{id} > status Correctly reports status (and progress, if supported)
    780 DELETE /jobs/{id} Is supported (with authentication)
    781 DELETE /jobs/{id} Deletes the job and returns a HTTP 204 on success, otherwise a 4XX.

    # Batch Jobs > Results

    # Functionality Description
    850 POST /jobs/{id}/results Supports queueing/starting jobs (with authentication)
    851 POST /jobs/{id}/results Queues or starts the processing, which is reflected in the job status. Does nothing if already queued or running. Responds with HTTP status 202.
    852 POST /jobs/{id}/results Rejects queueing a job if a payment is required.
    860 GET /jobs/{id}/results Is supported (with authentication)
    861 GET /jobs/{id}/results Returns HTTP status code 200 if the result is available, HTTP status code 424 with error message if the job status is "error", 4XX/5XX otherwise.
    863 GET /jobs/{id}/results > partial parameter If processing has not finished and the partial parameter is not set to true, requests are rejected with openEO error JobNotFinished.
    865 GET /jobs/{id}/results > STAC Response is a valid STAC Item or Collection
    866 GET /jobs/{id}/results > STAC Supports a valid STAC version (0.9.0 or later, preferrably 1.0.0).
    869 GET /jobs/{id}/results > STAC Assets all have an href and type, ideally also a title
    870 GET /jobs/{id}/results > STAC Additional metadata is provided for the results (e.g. extents, projection, bands, dimensions). Please provide details in the comments.
    871 GET /jobs/{id}/results > public access link Support canonical links (a public access signed URL, exposed as link with relation type canonical)
    872 GET /jobs/{id}/results > public access link Exposes the expires property in the response body
    890 DELETE /jobs/{id}/results Is supported (with authentication)
    891 DELETE /jobs/{id}/results Cancels data processing (if queued or running), optionally deletes the batch job results and returns a HTTP 202 on success.

    # L1C: Minimal - Secondary Web Services

    # Secondary Web Service Discovery

    # Functionality Description
    600 GET /service_types Is supported
    601 GET /service_types Works without authentication
    602 GET /service_types Works with authentication
    603 GET /service_types Valid response with at least configuration and process_parameters per service

    # Secondary Web Services

    # Functionality Description
    610 GET /services Is supported (with authentication)
    611 GET /services Valid response with at least services and links as arrays
    613 GET /services > limit parameter All services are returned if no limit parameter is provided
    614 GET /services > services The served service metadata is valid according to the specification (id, url, type and enabled are required)
    615 GET /services > services title is provided for all services (if provided by the user)
    630 POST /services Supports creating services (with authentication)
    631 POST /services > process Only accepts valid process submissions
    632 POST /services > configuration Supports configuration parameters, rejects invalid configuration parameters
    633 POST /services > type Rejects invalid types, accepts types case insensitive
    636 POST /services Supports storing title, description and enabled
    638 POST /services Returns HTTP status 201 and OpenEO-Identifier + Location header if successful
    650 GET /services/{id} Is supported (with authentication)
    651 GET /services/{id} Returns at least id, url, type, enabled, process, configuration and attributes
    652 GET /services/{id} title and description are available if provided by the user
    680 DELETE /services/{id} Is supported (with authentication)
    681 DELETE /services/{id} Deletes the service and returns a HTTP 204 on success, otherwise a 4XX.

    # API fundamentals

    # Functionality Description
    9 All > Process Graphs Namespace support
    11 All > Process Graphs Use user-defined processes (in process_id)
    12 All > Process Graphs Parameter support (use of from_parameter)

    # Well-known Discovery

    # Functionality Description
    20 GET /.well-known/openeo Not part of the main API tree / not versioned

    # Capabilities

    # Functionality Description
    35 GET / > billing Billing information are provided

    # File Formats

    # Functionality Description
    44 GET /file_formats File format names aligned with GDAL
    47 GET /file_formats Each format has a description describing their structure and relation to data cubes.
    49 GET /file_formats title and description are provided for all file formats

    # Authentication

    # Functionality Description
    112 GET /credentials/oidc OIDC is supported (without authentication for discovery requests)

    # User Account

    # Functionality Description
    120 GET /me Is supported (with authentication)
    121 GET /me > user_id Returns a unique user ID

    # Pre-defined Processes

    # Functionality Description
    206 GET /processes > processes All processes are customized to reflect the actual implementation status (removal of unsupported parameters, updates to parameter schemas, etc.)
    207 GET /processes > processes Summaries and categories are provided for all processes

    # Collections

    # Functionality Description
    304 GET /collections Implements STAC API - Collections (https://api.stacspec.org/v1.0.0/collections)
    308 GET /collections > collections License links are provided for non-SPDX license identifiers
    309 GET /collections > collections All collections provide a title and keywords
    310 GET /collections > collections Extents report the actual availability of data and don't overpromise
    325 GET /collections/{id} > stac_extensions Lists all extensions that are used in the document

    # User-defined processes

    # Functionality Description
    400 GET /process_graphs Is supported (with authentication)
    401 GET /process_graphs Valid response with at least processes and links as arrays
    403 GET /process_graphs > limit parameter All processes are returned if no limit parameter is provided
    404 GET /process_graphs > processes All stored processes are valid according to the specification (id is required)
    405 GET /process_graphs > processes Missing properties in the process objects are not set to null if missing (allowed only for: summary, description, parameters, returns)
    410 PUT /process_graphs/{id} > create Supports creating new processes (with authentication)
    411 PUT /process_graphs/{id} > update Supports updating existing processes (required only if creating new processes is supported)
    412 PUT /process_graphs/{id} Only accepts valid process submissions (id and process_graph required, given process must be valid)
    413 PUT /process_graphs/{id} > id If the id given in the request body is not equal to the given process_graph_id in the path, updates the id given in the request body.
    414 PUT /process_graphs/{id} Fully replaces the process if a user-defined process with the given ID exists.
    420 GET /process_graphs/{id} Is supported (with authentication)
    421 GET /process_graphs/{id} Valid response with at least id and process_graph
    422 GET /process_graphs/{id} Missing properties in the process objects are not set to null if missing (allowed only for: summary, description, parameters, returns)
    430 DELETE /process_graphs/{id} Is supported (with authentication)
    431 DELETE /process_graphs/{id} Deletes the process and returns a HTTP 204 on success, otherwise a 4XX.

    # Data Processing

    # Functionality Description
    702 At least two of batch jobs, secondary web services or synchronous processing are implemented

    # Batch Jobs

    # Functionality Description
    710 GET /jobs Is supported (with authentication)
    711 GET /jobs Valid response with at least jobs and links as arrays
    713 GET /jobs > limit parameter All jobs are returned if no limit parameter is provided
    714 GET /jobs > jobs The served job metadata is valid according to the specification (id, status and created are required)
    715 GET /jobs > jobs title is provided for all jobs (if provided by the user)
    730 POST /jobs Supports creating batch jobs (with authentication)
    731 POST /jobs > process Only accepts valid process submissions
    734 POST /jobs Supports storing title and description
    736 POST /jobs Returns HTTP status 201 and OpenEO-Identifier + Location header if successful
    737 POST /jobs > status Job status is "created" after successful creation of the job, the data processing does not start yet
    750 GET /jobs/{id} Is supported (with authentication)
    751 GET /jobs/{id} Returns at least id, process, status and created
    752 GET /jobs/{id} title and description are available if provided by the user
    754 GET /jobs/{id} > status Correctly reports status (and progress, if supported)
    780 DELETE /jobs/{id} Is supported (with authentication)
    781 DELETE /jobs/{id} Deletes the job and returns a HTTP 204 on success, otherwise a 4XX.

    # Batch Jobs > Logs

    # Functionality Description
    800 GET /jobs/{id}/logs Is supported (with authentication)
    801 GET /jobs/{id}/logs > offset parameter Supports query parameter offset, only returns log entries that were logged after the given offset
    804 GET /jobs/{id}/logs Valid response with at least logs and links as arrays
    805 GET /jobs/{id}/logs > level level property is available if log_level has been set for the job or the level parameter has been provided
    806 GET /jobs/{id}/logs > logs The served log entries are valid according to the specification (id, level and message are required)
    807 GET /jobs/{id}/logs > logs time is supported, core and data is supported if applicable
    808 GET /jobs/{id}/logs > logs The message is short and concise, doesn't contain full stack traces.

    # Batch Jobs > Results

    # Functionality Description
    850 POST /jobs/{id}/results Supports queueing/starting jobs (with authentication)
    851 POST /jobs/{id}/results Queues or starts the processing, which is reflected in the job status. Does nothing if already queued or running. Responds with HTTP status 202.
    852 POST /jobs/{id}/results Rejects queueing a job if a payment is required.
    860 GET /jobs/{id}/results Is supported (with authentication)
    861 GET /jobs/{id}/results Returns HTTP status code 200 if the result is available, HTTP status code 424 with error message if the job status is "error", 4XX/5XX otherwise.
    863 GET /jobs/{id}/results > partial parameter If processing has not finished and the partial parameter is not set to true, requests are rejected with openEO error JobNotFinished.
    865 GET /jobs/{id}/results > STAC Response is a valid STAC Item or Collection
    866 GET /jobs/{id}/results > STAC Supports a valid STAC version (0.9.0 or later, preferrably 1.0.0).
    869 GET /jobs/{id}/results > STAC Assets all have an href and type, ideally also a title
    870 GET /jobs/{id}/results > STAC Additional metadata is provided for the results (e.g. extents, projection, bands, dimensions). Please provide details in the comments.
    871 GET /jobs/{id}/results > public access link Support canonical links (a public access signed URL, exposed as link with relation type canonical)
    872 GET /jobs/{id}/results > public access link Exposes the expires property in the response body
    890 DELETE /jobs/{id}/results Is supported (with authentication)
    891 DELETE /jobs/{id}/results Cancels data processing (if queued or running), optionally deletes the batch job results and returns a HTTP 202 on success.

    # Synchronous Processing

    # Functionality Description
    900 POST /result Is supported (with authentication)
    901 POST /result > process Only accepts valid process submissions
    905 POST /result Returns HTTP status 200 on success with the data in the response body, HTTP status codes 4XX or 5XX on failure with valid response body
    906 POST /result > Content-Type header Returns suitable media type for the requested file format (not always application/octet-stream) in the Content-Type header
    910 POST /result Rejects processing if a payment is required.

    # L3: Advanced

    # API fundamentals

    # Functionality Description
    8 All > Error Handling Additionally returns id and/or url properties
    10 All > Process Graphs Multiple result nodes (e.g. save result as GeoTiff and netCDF)

    # Well-known Discovery

    # Functionality Description
    22 GET /.well-known/openeo Lists production and development instance (if applicable)

    # Conformance Classes

    # Functionality Description
    60 GET /conformance Is supported (without authentication)
    61 GET /conformance Valid response with at least conformsTo

    # Authentication

    # Functionality Description
    111 GET /credentials/basic HTTP Basic is not supported in production

    # Pre-defined Processes

    # Functionality Description
    203 GET /processes > limit parameter Support for pagination, incl. pagination links

    # Collections

    # Functionality Description
    305 GET /collections > limit parameter Support for pagination, incl. pagination links
    330 GET /collections/{id}/queryables Is supported and valid JSON Schema

    # User-defined processes

    # Functionality Description
    402 GET /process_graphs > limit parameter Support for pagination, incl. Pagination links

    # Validation

    # Functionality Description
    490 POST /validation Is supported (with authentication)
    491 POST /validation For valid input: HTTP 200 returning an empty errors array
    492 POST /validation For invalid input: HTTP 200 with a non-empty errors array. Each objects contains at least code and message.
    493 POST /validation HTTP status codes 4XX and 5XX are not used to indicate errors in the process. They only indicate invalid HTTP requests or server errors
    494 POST /validation > id Checks validity of process id
    495 POST /validation > parameters Checks validity of parameters (e.g. JSON Schema)
    496 POST /validation > returns Checks validity of return value (e.g. JSON Schema)
    497 POST /validation > process_graph Checks validity of provided process graph (e.g. parameters used, processes available, parameters provided valid, …)

    # Batch Jobs

    # Functionality Description
    712 GET /jobs > limit parameter Support for pagination, incl. pagination links
    732 POST /jobs > billing Has billing support (plan, budget)
    733 POST /jobs > log_level Supports setting a min. log level
    735 POST /jobs > additionalProperties Accepts/supports additional properties (if applicable) - please provide a list of them in the comments
    753 GET /jobs/{id} Has billing support (plan, costs, budget, usage)
    755 GET /jobs/{id} > additionalProperties Correctly reflects additional properties set during job creation (or after job update)
    760 PATCH /jobs/{id} Supports updating batch jobs (with authentication)
    764 PATCH /jobs/{id} Supports updating title and description

    # Batch Jobs > Logs

    # Functionality Description
    802 GET /jobs/{id}/logs > level parameter Supports query parameter level
    803 GET /jobs/{id}/logs > limit parameter Support for pagination, incl. pagination links

    # Batch Jobs > Estimates

    # Functionality Description
    820 GET /jobs/{id}/estimate Is supported (with authentication), provides at least one of costs, duration or size
    821 GET /jobs/{id}/estimate > costs Supports cost estimates
    824 GET /jobs/{id}/estimate > downloads_included Specifies how many full downloads of the processed data are included in the estimate.
    825 GET /jobs/{id}/estimate > expires Specifies the time until the estimate is valid.
    826 GET /jobs/{id}/estimate Returns with HTTP 200 on success and returns with HTTP status 500 and error code EstimateComplexity if an estimate can't be computed.

    # Batch Jobs > Results

    # Functionality Description
    864 GET /jobs/{id}/results > OpenEO-Costs header Supports the OpenEO-Costs header
    867 GET /jobs/{id}/results > STAC Supports a full STAC hierachy (i.e. childs/items in a Collection)

    # Synchronous Processing

    # Functionality Description
    902 POST /result > billing Has billing support (plan, budget)
    903 POST /result > log_level Supports setting a min. log level
    904 POST /result > additionalProperties Accepts/supports additional properties (if applicable) - please provide a list of them in the comments
    907 POST /result > OpenEO-Costs header Returns OpenEO-Costs header
    908 POST /result > Link header Returns a link to logs in the Link header (relation type "monitor")
    909 POST /result > multiple files Supports TAR file format (or other archive format) if multiple files get generated

    # L3-UDF: UDF

    # UDF Runtimes

    # Functionality Description
    71 GET /udf_runtimes Is supported
    72 GET /udf_runtimes Works without authentication
    73 GET /udf_runtimes Works with authentication
    74 GET /udf_runtimes Valid response with at least type, default and versions per UDF runtime
    75 GET /udf_runtimes title and description are provided for all fUDF runtimes

    # L3-FS: File Storage

    # File Storage

    # Functionality Description
    500 GET /files Is supported (with authentication)
    501 GET /files Valid response with at least files and links as arrays
    502 GET /files > limit parameter Support for pagination, incl. pagination links
    503 GET /files > limit parameter All processes are returned if no limit parameter is provided
    504 GET /files > files The served file metadata is valid according to the specification (path is required)
    505 GET /files > files File sizes and modification dates are provided
    506 GET /files > files Paths are valid according to the specification (no leading slash, unix-style separator, no URL encoding)
    510 PUT /files/{path} Supports uploading files (with authentication)
    511 PUT /files/{path} Paths are correctly parsed (works around the OpenAPI limitation)
    512 PUT /files/{path} Support for folders
    513 PUT /files/{path} Overrides existing files if applicable
    520 GET /files/{path} Is supported (with authentication)
    521 GET /files/{path} Provides the file content, correctly encoded (upload + download don't change the checksum of the files) and with a suitable media type
    530 DELETE /files/{path} Is supported (with authentication)
    531 DELETE /files/{path} Deletes the file and returns a HTTP 204 on success, otherwise a 4XX.

    # L3-SWS: Secondary Web Services

    # Secondary Web Service Discovery

    # Functionality Description
    600 GET /service_types Is supported
    601 GET /service_types Works without authentication
    602 GET /service_types Works with authentication
    603 GET /service_types Valid response with at least configuration and process_parameters per service
    604 GET /service_types title and description are provided for all services

    # Secondary Web Services

    # Functionality Description
    610 GET /services Is supported (with authentication)
    611 GET /services Valid response with at least services and links as arrays
    612 GET /services > limit parameter Support for pagination, incl. pagination links
    613 GET /services > limit parameter All services are returned if no limit parameter is provided
    614 GET /services > services The served service metadata is valid according to the specification (id, url, type and enabled are required)
    615 GET /services > services title is provided for all services (if provided by the user)
    630 POST /services Supports creating services (with authentication)
    631 POST /services > process Only accepts valid process submissions
    632 POST /services > configuration Supports configuration parameters, rejects invalid configuration parameters
    633 POST /services > type Rejects invalid types, accepts types case insensitive
    634 POST /services > billing Has billing support (plan, budget)
    635 POST /services > log_level Supports setting a min. log level
    636 POST /services Supports storing title, description and enabled
    638 POST /services Returns HTTP status 201 and OpenEO-Identifier + Location header if successful
    650 GET /services/{id} Is supported (with authentication)
    651 GET /services/{id} Returns at least id, url, type, enabled, process, configuration and attributes
    652 GET /services/{id} title and description are available if provided by the user
    653 GET /services/{id} Has billing support (plan, costs, budget, usage)
    660 PATCH /services/{id} Supports updating services (with authentication)
    661 PATCH /services/{id} > process Allows updating processes, but only accepts valid process submissions
    662 PATCH /services/{id} > configuration Supports updating configuration parameters
    663 PATCH /services/{id} > configuration Rejects invalid configuration parameters
    664 PATCH /services/{id} > type Rejects invalid types, accepts types case insensitive
    665 PATCH /services/{id} > billing Has billing support (plan, budget)
    666 PATCH /services/{id} > log_level Supports updating the min. log level
    667 PATCH /services/{id} > enabled Supports enabling/disabling the service
    668 PATCH /services/{id} Supports updating title and description
    680 DELETE /services/{id} Is supported (with authentication)
    681 DELETE /services/{id} Deletes the service and returns a HTTP 204 on success, otherwise a 4XX.

    # L4: Above and Beyond

    # Secondary Web Services

    # Functionality Description
    616 GET /services Supports on-demand web services (processing while using the service)
    617 GET /services Supports viewing of batch job results (processing beforehand as batch job, i.e. load_result -> save process)
    637 POST /services > additionalProperties Accepts/supports additional properties (if applicable) - please provide a list of them in the comments
    654 GET /services/{id} > additionalProperties Correctly reflects additional properties set during service creation (or after service update)
    669 PATCH /services/{id} > additionalProperties Accepts/supports additional properties (if applicable)

    # Secondary Web Services > Logs

    # Functionality Description
    690 GET /services/{id}/logs Is supported (with authentication)
    691 GET /services/{id}/logs > offset parameter Supports query parameter offset, only returns log entries that were logged after the given offset
    692 GET /services/{id}/logs > level parameter Supports query parameter level
    693 GET /services/{id}/logs > limit parameter Support for pagination, incl. pagination links
    694 GET /services/{id}/logs Valid response with at least logs and links as arrays
    695 GET /services/{id}/logs > level level property is available if log_level has been set for the service or the level parameter has been provided
    696 GET /services/{id}/logs > logs The served log entries are valid according to the specification (id, level and message are required)
    697 GET /services/{id}/logs > logs time is supported, core and data is supported if applicable
    698 GET /services/{id}/logs > logs The message is short and concise, doesn't contain full stack traces.

    # Batch Jobs

    # Functionality Description
    761 PATCH /jobs/{id} > process Allows updating processes, but only accepts valid process submissions
    762 PATCH /jobs/{id} > billing Has billing support (plan, budget)
    763 PATCH /jobs/{id} > log_level Supports updating the min. log level
    765 PATCH /jobs/{id} > additionalProperties Accepts/supports additional properties (if applicable)

    # Batch Jobs > Estimates

    # Functionality Description
    822 GET /jobs/{id}/estimate > duration Supports runtime duration estimates
    823 GET /jobs/{id}/estimate > size Supports result file size estimates

    # Batch Jobs > Results

    # Functionality Description
    862 GET /jobs/{id}/results > partial parameter Supports partial query parameter
    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/developers/profiles/index.html b/documentation/1.0/developers/profiles/index.html index cb02fbac4..469966388 100644 --- a/documentation/1.0/developers/profiles/index.html +++ b/documentation/1.0/developers/profiles/index.html @@ -8,7 +8,7 @@ - + @@ -197,6 +197,6 @@
    Contact

    • openEO Profiles

    # openEO Profiles

    We've defined requirements grouped into a set of profiles for the openEO API and the openEO Processes. This helps to classify the implementation status of an openEO instance and gives recommendations to implementors which functionality to prioritize.

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/developers/profiles/processes.html b/documentation/1.0/developers/profiles/processes.html index 4ddf80270..ac31b9b8a 100644 --- a/documentation/1.0/developers/profiles/processes.html +++ b/documentation/1.0/developers/profiles/processes.html @@ -8,7 +8,7 @@ - + @@ -209,6 +209,6 @@
    • Parameter spatial_extent: Supports filtering by base/height
  • load_uploaded_files (experimental)
  • load_url (experimental)
  • reduce_dimension
    • Parameter reducer: At least all processes (up to the level of this requirement) in the categories array, comparison, logic and math can be used (also in a chain of processes).
  • reduce_spatial (experimental)
    • Parameter reducer: At least all processes (up to the level of this requirement) in the categories array, comparison, logic and math can be used (also in a chain of processes).
  • sar_backscatter (experimental)
    • Parameter coefficient: enum reflects implemention (all values are supported)
    • Custom parameters are provided via the options parameter
    • Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/glossary.html b/documentation/1.0/glossary.html index 0adb4065d..f58dcbd64 100644 --- a/documentation/1.0/glossary.html +++ b/documentation/1.0/glossary.html @@ -8,7 +8,7 @@ - + @@ -205,6 +205,6 @@ Special cases are raster and vector datacubes (opens new window). Learn more about datacubes in the datacube documentation (opens new window).

    # Vector data

    In general, vector data represent specific things (also called "features") in a space, e.g. on the surface of the Earth.

    A coordinate represents a specific point in space.

    A feature is a thing that usually has a geometry (e.g. the outline of an agricultural field, a forest or an urban area) and it may have additional properties assigned (e.g. a name, a color or a population). In rare cases features may not have a geometry, which is often referred to as an "empty geometry".

    Geometries consist of one or more coordinates that may be connected and then form a specific type of geometry, e.g. two points can be connected to a straight line and four straight lines can be connected to rectangle.

    Commonly used types of geometries are:

    • Point
    • LineString (connected straight line pieces)
    • Polygon (connected straight line pieces forming a closed ring, possibly with holes - for example a triangle or rectangle)

    Multiple geometries of the same type can be combined into a group of geometries, e.g. a so-called "multi point" or "multi polygon".

    Features and geometries are specified by the OGC in the Simple Feature Access specification (opens new window) (and ISO 19125). See the specification for more details.

    # User-defined function (UDF)

    The abbreviation UDF stands for user-defined function. With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, or applied to a particular dimension or set of dimensions, allowing custom server-side calculations. See the section on UDFs for more information.

    # Data Processing modes

    Processes can run in three different ways:

    1. Results can be pre-computed by creating a batch job. They are submitted to the back-end's processing system, but will remain inactive until explicitly put into the processing queue. They will run only once and store results after execution. Results can be downloaded. Batch jobs are typically time consuming and user interaction is not possible although log files are generated for them. This is the only mode that allows to get an estimate about time, volume and costs beforehand.
    2. A more dynamic way of processing and accessing data is to create a secondary web service. They allow web-based access using different protocols such as OGC WMS (opens new window) (Open Geospatial Consortium Web Map Service), OGC WCS (opens new window) (Web Coverage Service) or XYZ tiles (opens new window). These protocols usually allow users to change the viewing extent or level of detail (zoom level). Therefore, computations often run on demand so that the requested data is calculated during the request. Back-ends should make sure to cache processed data to avoid additional/high costs and reduce waiting times for the user.
    3. Processes can also be executed on-demand (i.e. synchronously). Results are delivered with the request itself and no job is created. Only lightweight computations, for example previews, should be executed using this approach as timeouts are to be expected for long-polling HTTP requests (opens new window).
    Edit this page (opens new window)
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    - + diff --git a/documentation/1.0/index.html b/documentation/1.0/index.html index 725669812..a652ba531 100644 --- a/documentation/1.0/index.html +++ b/documentation/1.0/index.html @@ -8,7 +8,7 @@ - + @@ -202,6 +202,6 @@ 0.4

    # Introduction

    First of all, a good idea is to get familiar with the terminology in openEO by reading the glossary. There's also an in-depth introduction to datacubes and processing them with openEO.

    Currently, there are three official client libraries, a web-based interface for openEO and a QGIS plugin.

    If you are unfamiliar with programming, you could start using the web-based editor for openEO (opens new window). It supports visual modelling of your algorithms and a simplified JavaScript based access to the openEO workflows and providers. openEO can also be used in QGIS through a QGIS plugin.

    If you are familiar with programming, you could choose a client library for three programming languages:

    Follow the links above to find usage instructions for each of the client libraries.

    For details about available processes, see the list of openEO processes. The openEO Cookbook describes common use-cases with code examples in all three languages.

    The more advanced topic to run your own code on back-ends, is explained in the introduction to openEO UDFs (user-defined functions).

    Note

    This is the documentation for the openEO API version 1.0. If you are looking for other versions of openEO, please use the version switch at the top right of this page.

    # Contribute

    Didn't find your programming language? You can also access the openEO API implementations directly or start implementing your own client library.

    If you are missing any functionality in the API feel free to open an issue (opens new window) or actively start proposing API changes as Pull Requests.

    Feel free to contact us for further assistance.

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/javascript/index.html b/documentation/1.0/javascript/index.html index ac1626acd..e9060c4c8 100644 --- a/documentation/1.0/javascript/index.html +++ b/documentation/1.0/javascript/index.html @@ -8,7 +8,7 @@ - + @@ -365,6 +365,6 @@ Unfortunately, you can only create Python and R functions at the moment. Therefore, this guide doesn't get into detail. For more information check out the Python or R tutorials on UDFs.

    # Additional Information

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/processes.html b/documentation/1.0/processes.html index 6b8fe2821..8465a9651 100644 --- a/documentation/1.0/processes.html +++ b/documentation/1.0/processes.html @@ -8,7 +8,7 @@ - + @@ -202,6 +202,6 @@ 0.4

    Note: This is the list of all processes specified by the openEO project. Back-ends implement a varying set of processes. Thus, the processes you can use at a specific back-end may derive from the specification, may include non-standardized processes and may not implement all processes listed here. Please check each back-end individually for the processes they support. The client libraries usually have a function called listProcesses or list_processes for that.

    - + diff --git a/documentation/1.0/python/client-side-processing.html b/documentation/1.0/python/client-side-processing.html index 11f09df54..9a0ab0efc 100644 --- a/documentation/1.0/python/client-side-processing.html +++ b/documentation/1.0/python/client-side-processing.html @@ -8,7 +8,7 @@ - + @@ -297,6 +297,6 @@ ndvi_median = ndvi.reduce_dimension(dimension="time", reducer="median") result_ndvi = ndvi_median.execute()

    # Client-Side Processing Example Notebooks

    # Additional Information

    Additional information and resources about the openEO Python Client Library:

    Edit this page (opens new window)
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    - + diff --git a/documentation/1.0/python/index.html b/documentation/1.0/python/index.html index 878a42be0..06bc4e0f5 100644 --- a/documentation/1.0/python/index.html +++ b/documentation/1.0/python/index.html @@ -8,7 +8,7 @@ - + @@ -350,6 +350,6 @@ job.start_and_wait().download_results()

    Now the resulting GTiff file of the RGB backscatter composite is in your current directory.

    RGB composite

    The source code (opens new window) of this example can be found on GitHub.

    # User Defined Functions

    If your use case can not be accomplished with the default processes of openEO, you can define a user defined function. Therefore, you can create a Python function that will be executed at the back-end and functions as a process in your process graph.

    Detailed information about Python UDFs can be found in the official documentation (opens new window) as well as examples in the Python client repository (opens new window).

    # Additional Information

    Additional information and resources about the openEO Python Client Library:

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/qgis/index.html b/documentation/1.0/qgis/index.html index d56c100e5..8f90c9b88 100644 --- a/documentation/1.0/qgis/index.html +++ b/documentation/1.0/qgis/index.html @@ -8,7 +8,7 @@ - + @@ -326,6 +326,6 @@ the openEO web editor (opens new window). There you can create new services via the editor or from your jobs.
  • Refresh Table - Similar to the jobs table, this button refreshes the list of services manually, in general the list will refresh every few seconds. If you do not want to automatically refresh the list (it might cause some lags if the connection to the backend is low) you can do so by deactivating the checkbox next to the refresh button.
  • Create from web editor - This button opens your browser with this introduction page.

    • # Additional Information

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/r/index.html b/documentation/1.0/r/index.html index 17e80e4ae..ec7f5e381 100644 --- a/documentation/1.0/r/index.html +++ b/documentation/1.0/r/index.html @@ -8,7 +8,7 @@ - + @@ -382,6 +382,6 @@ # then start the processing of the job and turn on logging (messages that are captured on the back-end during the process execution) start_job(job = job, log = TRUE)

    Now the resulting PNG file of the RGB backscatter composite is stored as a PNG file in the current working directory. It looks like this:

    RGB composite

    TIP

    The source code (opens new window) of the example can also be found on the R client repository on Github.

    # User Defined Functions

    If your use case can not be accomplished with the default processes of openEO, you can define a user defined function.

    In general the processing workflow works by uploading the Python or R script into the users file directory on the back-end and reference the script via its URL or by its relational name (e.g. /scripts/script1.R) in the function run_udf. The latter function is a predefined openEO process that the back-end might provide, if UDFs are supported.

    Find out more about UDFs in the respective Python UDF (opens new window) and R UDF (opens new window) repositories with their documentation and examples.

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/1.0/udfs.html b/documentation/1.0/udfs.html index 21b8f16ad..6867e430d 100644 --- a/documentation/1.0/udfs.html +++ b/documentation/1.0/udfs.html @@ -8,7 +8,7 @@ - + @@ -227,6 +227,6 @@ // Further process the data cube or save it using save_result...

    # Python

    The Python client has separate documentation on UDFs (opens new window).

    # QGIS

    QGIS doesn't natively support running UDFs yet, but you can use copy and paste user-defined processes that include UDFs.

    # R

    There's no dedicated UDF documentation for R yet, but you can have a look at the getting started guide and examples in the UDF server repository (opens new window) for now.

    # Web Editor

    Running UDFs in the Web Editor just requires you to drag the run_udf process into the model builder. Afterwards you can edit the process parameters to your needs. You have to set the environment / programming language to run the UDF with and then you can either directly program a UDF in the code editor or select an UDF file you have previously uploaded to the user workspace.

    # Back-ends

    If you'd like to offer user-defined functions for your users, you need to implement either the process run_udf or run_udf_externally (or both). Both usually require you to access UDF servers via the UDF API (opens new window). For the process run_udf you may bypass the API and exchange data directly between the openEO back-end and the openEO UDF servers for performance reasons.

    run_udf allows users to run their code on your infrastructure. This can be done using one of the UDF server implementations in Python (opens new window) or R (opens new window). Please note that both UDF servers are drafts and still need work to improve performance and make them secure! This is not yet an out-of-the-box solution for running UDFs securely and fast.

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/code-of-conduct.html b/documentation/code-of-conduct.html index b306551e5..81e39d69c 100644 --- a/documentation/code-of-conduct.html +++ b/documentation/code-of-conduct.html @@ -8,7 +8,7 @@ - + @@ -208,6 +208,6 @@ from the project team.

    Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by opening an issue or contacting one or more of the project maintainers.

    This Code of Conduct is adapted from the Contributor Covenant (opens new window), version 1.0.0, available at http://contributor-covenant.org/version/1/0/0/ (opens new window).

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/documentation/software-guidelines.html b/documentation/software-guidelines.html index a1de5dd66..3bdffd809 100644 --- a/documentation/software-guidelines.html +++ b/documentation/software-guidelines.html @@ -8,7 +8,7 @@ - + @@ -198,6 +198,6 @@ Contact

    # Software Development Guidelines

    This document describes guidelines for software developers, written for the openEO (opens new window) project. Since the openEO infrastructure encompasses several programming languages and software environments, this document does not prescribe particular tools or platforms but rather focuses on general principles and methods behind them.

    1. License: all software developed in the openEO project and published on the openEO GitHub (opens new window) organisation shall be licensed under the Apache 2.0 license (opens new window). If software repositories deviate from this, or contain code or other artifacts that deviates from this, this shall be described in the README.md file.
    2. Location: Official openEO software is developed under the openEO GitHub organisation (opens new window).
    3. Proof-of-concept versus sustainable: each repository shall indicate its status: either proof-of-concept, or sustainable. Proof-of-concept code is meant to work but comes without quality assurance. Software repositories with proof-of-concept developments shall clearly say so in the first paragraph of the README.md file.
    4. Sustainable code should undergo standard quality checks, and point out its documentation.
    5. Sustainable code shall undergo code review; no direct commits to master; any commit shall come in the form of a PR, commit after review.
    6. Sustainable code shall be written in a Test-driven manner, and repositories shall at the top of their README.md give indication of the degree to which code is covered by tests.
    7. Continuous integration shall be used to indicate code currently passes its test on CI platforms.
    8. A Code of conduct describes the rules and constraints to developers and contributors.
    9. Version numbers of sustainable software releases shall follow Semantic Versioning 2.0.0 (opens new window).

    # Software quality guidelines

    • software shall be written in such a way that another person can understand its intention
    • comment lines shall be used sparsely, but effectively
    • reuse of unstable or esoteric libraries shall be avoided

    # Software documentation guidelines

    Software documentation shall include:

    • installation instructions
    • usage instructions
    • explain in detail the intention of the software
    • pointers to reference documents explaining overarching concepts

    Each repository's README.md shall point to the documentation.

    Reference documentation shall be written using well-defined reference documentation language, such as RFC2119 (opens new window) or arc42 (opens new window), and refer to the definitions used.

    # Software review

    • sustainable software development shall take place by always having two persons involved in a change to the master branch: individuals push to branches, pull request indicate readiness to be taken up in the master branch, a second developer reviews the pull request before merging it into the master branch.
    • software review discussions shall be intelligible for external developers, and serve as implicit documentation of development decisions taken

    # Test-driven development

    Software shall be developed in a test-driven fashion, meaning that while the code is written, tests are developed that verify, to a reasonable extent, the correctness of the code. Tools such as codecov.io (opens new window) to automatically indicate the amount of code covered by tests, and code that is not covered by tests shall be used in combination with a continuous integration framework.

    # Continuous integration

    Repositories containing running software shall use an appropriate continuous integration platform, such as GitHub Actions or similar, to show whether the current build passes all checks. This helps understand contributors that the software passes tests on an independent platform, and may give insights in the way the software is compiled, deployed and tested.

    # Additional guidelines

    There is specific guideline for client library development.

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/events/igarrs2021.html b/events/igarrs2021.html index 377b95468..1e872d80a 100644 --- a/events/igarrs2021.html +++ b/events/igarrs2021.html @@ -8,7 +8,7 @@ - + @@ -200,6 +200,6 @@ IEEE IGARRS 2021 openEO tutorial.

    Details: https://igarss2021.com/tutorials.php#tut9 (opens new window)

    # Agenda

    • 14:00-14:20: Introduction to openEO (Pieter Kempeneers - JRC)
    • 14:20-14:30: Q&A
    • 14:30-15:30: Python client (VITO) (Jeroen Dries - VITO)
    • 15:30-16:15: R client (WUR) (Dainius Masiliunas - WUR)
    • 16:15-16:30: Break
    • 16:30-16:45: openEO UIs (Jeroen Dries - VITO)
    • 16:45-17:15: Use case demo (Jeroen Dries - VITO)
    • 17:15-18:00: Q&A

    # Python client

    # Logging in

    For participants interested in trying thing out, try logging into Terrascope openEO backend:

    https://docs.terrascope.be/#/Developers/WebServices/OpenEO/OpenEO (opens new window)

    Jupyter notebooks with the openEO Python client installed: https://notebooks.terrascope.be (opens new window)

    This jupyter environment also has the notebook samples in this git repository already available:

    https://github.com/VITObelgium/notebook-samples (opens new window)

    Otherwise, git can be used to check out the notebooks, or simply download the 'ipynb' file.

    Use 'edugain and social accounts' login to avoid having to register at Terrascope, you may still be required to fill in a form.

    # openEO in Python intro

    General Python documentation:

    https://open-eo.github.io/openeo-python-client/ (opens new window)

    Basics:

    https://github.com/Open-EO/openeo-python-client/blob/master/examples/notebooks/openeo-terrascope-webinar.ipynb (opens new window)

    More advanced:

    https://github.com/VITObelgium/notebook-samples/blob/master/tools/openEO/openeo-showcase.ipynb (opens new window)

    # R client

    https://openeo.org/documentation/1.0/r/ (opens new window)

    Demonstration notebook:

    https://github.com/VITObelgium/notebook-samples/blob/master/tools/openEO/openeo-r-client.ipynb (opens new window)

    # openEO user interfaces

    https://hub.openeo.org (opens new window)

    https://editor.openeo.org (opens new window)

    # Use cases

    Using predefined 'higher level' services:

    https://github.com/VITObelgium/notebook-samples/blob/master/Terrascope/Beginner/OpenEO_CropCalendarService.ipynb (opens new window)

    Creating 'higher level' services:

    https://open-eo.github.io/openeo-python-client/udp.html#building-and-storing-user-defined-process (opens new window)

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/index.html b/index.html index 72a3842ca..441cbb73d 100644 --- a/index.html +++ b/index.html @@ -8,7 +8,7 @@ - + @@ -213,6 +213,6 @@
  • December 6, 2023
    openEO submitted to OGC for consideration as an OGC community standard
    • Read more...

    Other Channels

    - + diff --git a/news/2017-12-05-welcome.html b/news/2017-12-05-welcome.html index d210047c9..0af13a27f 100644 --- a/news/2017-12-05-welcome.html +++ b/news/2017-12-05-welcome.html @@ -8,7 +8,7 @@ - + @@ -204,6 +204,6 @@ source software solutions. We develop an API to connect R, python and javascript clients to big Earth observation cloud back-ends in a simple and unified way.

    We invite everyone interested to participate. Get in touch today!

    - + diff --git a/news/2017-12-18-VITO_meeting.html b/news/2017-12-18-VITO_meeting.html index bcf26807c..7c5409d91 100644 --- a/news/2017-12-18-VITO_meeting.html +++ b/news/2017-12-18-VITO_meeting.html @@ -8,7 +8,7 @@ - + @@ -205,6 +205,6 @@ gathered to discuss, and work on realising the first three use cases (opens new window), which are planned to be delivered in Month 6 (March 2018):

    We worked on the following back-ends:

    • Sentinel hub
    • EODC file-based
    • Rasdaman
    • GRASS
    • GeoTrellis

    and on the R and python clients, and started working on a glossary.

    Among the many insights we gathered by sitting together and talk, we found that

    • the "core API" as described in the proposal is not so much a software layer on itself, but rather an API in front of every compute back-end; this simplifies the whole architecture pretty much
    • OpenSearch should have the ability to describe collections of granules (or images, tiles) in addition to describing individual granules
    • band can be seen as array dimension as well as attributes of array records, but seeing it as a dimension may make life easier
    • use case 1 can be described as a sequence of filter operations (on image collection, bounding box, date range, and bands) followed by two aggregate operations (compute division over bands, compute mininum over time)

    Intermediate results can be found in a bunch of repositories, mostly proof-of-concept, on the openEO Github organisation (opens new window).

    - + diff --git a/news/2018-01-31-WWU_meeting.html b/news/2018-01-31-WWU_meeting.html index f2011bcce..1f29d3bf8 100644 --- a/news/2018-01-31-WWU_meeting.html +++ b/news/2018-01-31-WWU_meeting.html @@ -8,7 +8,7 @@ - + @@ -200,6 +200,6 @@ on January 31, 2018 by Marius Appel.

    On Jan 22-24, the Institute for Geoinformatics (opens new window) at the University of Muenster hosted the second week of intense collaboration. 16 developers from 8 partners worked on implementing the proof of concept use cases for different clients and backends, improving the core API, and connecting the clients to different backends.

    We continued implementing the GeoTrellis (opens new window), WCPS (opens new window), GRASS GIS (opens new window), and Sentinel Hub (opens new window) backend drivers and a Python client (opens new window) for OpenEO, which we started during our meeting in December at VITO (opens new window).

    Besides many improvements in the proof of concept implementations, this week resulted in two more prototypical OpenEO clients:

    During the three days, we also discussed important ideas of the core API specification and how it can be improved by supporting synchronous processing and representing descriptions of process chains as resources. EODC (opens new window) presented its new OpenShift (opens new window)-based architecture to run OpenEO queries in containers. Further important discussions made clear how our proof of concept in month 6 will look like.

    More details of the week's results can be found in our GitHub (opens new window) repositories.

    - + diff --git a/news/2018-03-15-poc.html b/news/2018-03-15-poc.html index 1ec57ba9b..118acd6bc 100644 --- a/news/2018-03-15-poc.html +++ b/news/2018-03-15-poc.html @@ -8,7 +8,7 @@ - + @@ -225,6 +225,6 @@ made while testing the various clients and back-ends.

    # Proof-of-concept videos

    # R client and WCPS back-end, use case 1

    # Grass GIS back-end, use cases 1, 2 and 3

    # R client and R back-end, use-case 1

    # R client and R back-end, use case 3

    # openEO Web Editor (JS client) with three back-ends (use case 1)

    Demonstrates use of back-ends Sentinel Hub, WCPS EURAC, and OpenShift EODC

    # openEO Web Editor (JS client) with R back-end (use case 3)

    # Python client with GeoPySpark back-end (use case 1)

    # openEO Web Editor (JS client) with Google Earth Engine back-end (use case 1)

    The following four documents (formal project deliverables) describe the proof-of-concept more extensively:

    In addition, a document has been written that describes standards and interfaces used by the various back-ends, and the extent to which they are or will be useful for further developing the openEO API:

    These deliverables have been submitted, but have not been approved, and hence may not be final.

    # Next steps

    During the proof-of-concept, intentionally a number of difficult issues were not addressed, including

    • billing (costs) of processes and account management and
    • authentification while others were defined rather vaguely, including
    • the description of data (end point /data)
    • the description of processes (end point /processes).

    The next steps will include:

    • discussing of the use-cases in the proof-of-concept
    • deciding whether to adopt existing standards and interfaces, e.g. for data descriptions
    • getting users involved outside the openEO consortium to define further requirements and priorities
    • designing a new iteration of the openEO API.

    # Getting involved

    As any API, the openAPI will only become good and useful if it is being used, and it needs testing by a wide audience. We are now at the stage of designing it, but at the point where large chunks are useful already. If you want to contribute to any of this, please do not hesitate and contact us e.g. by

    • expressing interest
    • writing GitHub issues wherever you think it is appropriate
    • sending an email
    - + diff --git a/news/2018-05-23-user-requirements-survey-launched.html b/news/2018-05-23-user-requirements-survey-launched.html index 68de24aa7..b2afbd53c 100644 --- a/news/2018-05-23-user-requirements-survey-launched.html +++ b/news/2018-05-23-user-requirements-survey-launched.html @@ -8,7 +8,7 @@ - + @@ -199,6 +199,6 @@

    • User Requirements Survey for openEO launched

    User Requirements Survey for openEO launched

    Written on May 23, 2018 by Sarah Carter.

    This survey (opens new window) aims to gather a detailed view of external user needs in relation EO data access and analysis. This will enable the openEO project team to better identify current needs of different users and the potential for openEO to be useful to them and to overcome any barriers they have in their work streams. Ultimately, this will steer the openEO API in a way which meets user needs including being compatible with additional EO service providers and their data infrastructures. Additional processes and use cases will also be added where necessary with the aim of benefiting more potential users.

    The survey is open to all, so please fill it in by 18 June 2018, and share within your relevant networks. It should take no longer than 10 minutes to complete.

    https://ec.europa.eu/eusurvey/runner/openEO_UserSurvey (opens new window)

    - + diff --git a/news/2018-10-30-year-one-review-meeting.html b/news/2018-10-30-year-one-review-meeting.html index 881869c88..d0ce38a0b 100644 --- a/news/2018-10-30-year-one-review-meeting.html +++ b/news/2018-10-30-year-one-review-meeting.html @@ -8,7 +8,7 @@ - + @@ -200,6 +200,6 @@ on October 30, 2018 by Matthias Schramm.

    The openEO consortium is currently undergoing its first evaluation process by the European Commission. During the first year the partners exceeded the planned progress. During the starting phase the meaning of the different layers was clarified more precisely. openEO represents a set of contracts between two API layers.

    1. Instances of the core API are implemented at back-end drivers within the respective EO service providers. By guaranteeing the same instances at the various back-ends, their interoperability can be guaranteed.
    2. Client APIs – software libraries specific to given programming languages – are enabling users to interact with the back-end's drivers. The communication between clients and service providers is realised via HTTP requests, which's complexity is not visible to the users, but is dealt with by openEO.

    Since the successful development of the Proof of Concept, client APIs are developed for Python (opens new window), R (opens new window), and JavaScript (opens new window), enabling the execution of a growing set of functionalities. Being language neutral, new client APIs of e.g. additional programming languages, Quantum GIS interfaces, or GRASS GIS can be added easily. Currently, the core API is establishing access to entry points of 7 European EO service providers, which are thus able to provide services to the user, reflecting examples from all stages of EO data processing.

    A process catalogue is under development, describing a set of functionalities to be implemented for openEO, their I/O data and their exact workflow. Interested users are encouraged to discuss with the consortium partners via the various provided channels to enable openEO forming widely accepted and used standards with a consistent syntax. The well-defined process catalogue shall also serve 3rd-party processing platforms with a template to become accessible to openEO. In the same manner, a client library development guideline is being prepared momentarily to ensure a standardised implementation of the client APIs.

    For using arbitrary code to process EO data with the openEO API, first User-Defined Functions (UDF) are currently implemented and added to openEO-compatible workflows. The UDFs are running in specific dockers at the EO data service providers.

    openEO is published in its version 0.3.0 (opens new window), providing a HTTP communication between the users and service providers specified by OpenAPI 3.0 JSON files. The JSON data entails process graphs, sent as a job to the back-ends, which can be executed in three different ways.

    1. A batch job can be submitted, which stays inactive until processing is requested. It will run only once and stores its results after execution.
    2. Secondary web services allow web-based access using different protocols such as OGC WMS, OGC WCS or XYZ tiles. The computation runs on demand to allow users to change e.g. the result's viewing extent or level of detail.
    3. Lightweight process graphs (e.g. small previews) can be executed synchronously. More costly processes have to expect timeouts for long-polling HTTP requests.

    The next steps for the consortium will entail the realisation of the newest openEO version at all back-ends and a definition of the targeted level of UDFs (e.g. simple NDVI calculation vs. machine learning algorithms). The process catalogues will be widened up, entailing at least all needed processes to implement the project's use cases via openEO. As an interested user please contact us, if you have any suggestions.

    Finally, we plan to submit our first preliminary stable openEO version in May 2019.

    - + diff --git a/news/2019-03-07-openeo-api-040.html b/news/2019-03-07-openeo-api-040.html index f5693025b..fb6d7ca92 100644 --- a/news/2019-03-07-openeo-api-040.html +++ b/news/2019-03-07-openeo-api-040.html @@ -8,7 +8,7 @@ - + @@ -227,6 +227,6 @@ .reduce("temporal", (b, data) => data.min()) .saveResult("GTiff");

    Similarly, it would work in the R client. You can also check the process graph documentation (opens new window) if you'd like to know how this algorithm looks as a process graph.

    # Other improvements

    We also updated our Data Discovery to be compatible with the most recent version of STAC, v0.6.2 (opens new window). In the last months, openEO contributed several extensions to the STAC specification such as an extension to describe Synthetic-Aperture Radar (SAR) data (opens new window) or an extension to describe Data Cubes (opens new window).

    Despite the process and data discovery, we generally improved the discovery of openEO back-ends. For example, the API now also allows clients to automatically detect supported API versions at the back-end, so that users don't need to keep track of that and can always connect to the same URL without worrying about compatibility between clients and back-ends. Our approach to user-defined functions (UDFs) (opens new window) was also better integrated withing the API. UDF runtimes can be discovered now and well-defined processes allow executing UDFs. Many more improvements were incorporated into the API specification based on feedback from the review meeting, discussions with users and implementations (see the full changelog (opens new window)).

    # Next steps

    This is the API version, which is targeted towards getting the first more universally usable version of the openEO clients and back-ends released. We will work hard now to implement the back-ends and clients to comply with the new API version and will follow up with a new blog post once this is achieved. Afterwards, all interested parties can use clients and back-ends to get a first solid impression of the project. In the meantime, it is already possible to make experiments with the back-ends and clients working on the API version 0.3.1. Please contact us (opens new window) to get more information about it. Having said that, the new version is not the last one and we will continue to improve the API, client and back-ends. So any feedback is highly appreciated and can be sent our way via GitHub issues in the corresponding GitHub repositories (opens new window) or via any of the other contact options (opens new window).

    - + diff --git a/news/2019-10-07-EO-big-data-hackaton.html b/news/2019-10-07-EO-big-data-hackaton.html index 0787f61d8..c7fab4082 100644 --- a/news/2019-10-07-EO-big-data-hackaton.html +++ b/news/2019-10-07-EO-big-data-hackaton.html @@ -8,7 +8,7 @@ - + @@ -199,6 +199,6 @@

    • H2020 EO Big Data Hackathon Nov 7-8

    H2020 EO Big Data Hackathon Nov 7-8

    Written on October 7, 2019 by Edzer Pebesma.

    openEO will participate, along with the four other H2020 projects funded under the same call, in the H2020 EO Big Data Hackathon (opens new window), which is open for participation.

    The hackaton will take place on Nov 7 and 8, 2019, in Frascati, Italy.

    - + diff --git a/news/2020-07-31-lessons-learned.html b/news/2020-07-31-lessons-learned.html index 1b196e6e1..53602bbec 100644 --- a/news/2020-07-31-lessons-learned.html +++ b/news/2020-07-31-lessons-learned.html @@ -8,7 +8,7 @@ - + @@ -199,6 +199,6 @@

    Lessons learned while developing the openEO API

    Written on July 31, 2020 by Matthias Mohr.

    With openEO, we are digging into new grounds regarding the interoperability of big Earth observation clouds. While we could address a lot of problems that came up, some issues remained that we couldn't solve. This article explains some of the lessons learned and thus also gives an honest overview about current limitations of the openEO API.

    # 1. Collection Names

    The naming of data sets (collections) is different across providers. We thought about recommending to follow lists like the CEOS Mission Index (opens new window), but then the question is what data does it really refer to? It lists Sentinel-2 A and Sentinel-2 B, but would that be Level 1C or 2A or something completely different? Therefore, openEO decided to not enforce any naming scheme as data sets throughout the affiliated providers were mostly differently pre-processed anyway. To improve the situation, openEO allows to define process parameters. This allows to define algorithms independently of data set names, which can later be assigned when executing the process. Also, openEO recommends to split processing instructions into two parts: data loading/pre-processing and the actual algorithm.

    Some more background information can be found in the corresponding GitHub issue (opens new window). In general, many limitations (including the following) concern the data holdings of the providers and may hopefully be solved by initiatives that work on Analysis Ready Data (ARD).

    # 2. Other Names

    Similarly, the naming of bands (and potentially other dimension labels, too) are inconsistent across providers, we faced names such as B2, B02, 2 or blue for Sentinel-2. openEO doesn't enforce any naming scheme, but has found ways to improve the situation for users. First, process parameters can be used (see above). Second, bands can also be specified using the common names as specified by STAC (opens new window) or wavelength ranges. openEO recommends to use common names whenever available.

    For example, to avoid specific naming you could filter for Sentinel-2 (A and B) band 2 as follows:

    • by common name: filter_bands(data = cube, bands = ['blue']) (blue maps to the wavelength range 0.45 - 0.50 according to STAC)
    • by wavelength, specified in μm: filter_bands(data = cube, wavelength = [[0.49, 0.50]]) (wavelength is 0.4966 for Sentinel-2A and 0.4921 for Sentinel-2B)

    In general, metadata is aligned for usage in openEO processes by using the STAC specification (opens new window), which improves interoperability.

    # 3. Chunked Collections

    Some providers (for example Google Earth Engine (opens new window)) expose data sets such as Sentinel-2 L2A as one large collection. Other providers expose the data sets split into groups, e.g. chunked by CRS and resolution. EURAC has collections such as S2_32632_60m_L2A or S2_32635_10m_L2A for example. There are valid reasons (opens new window) for any of the implementations, but to explain them is too much for this article. Unfortunately, those differences decrease the interoperability and in the end the re-usability of the processes. openEO aims to make algorithms re-usable and thus recommends to split processing instructions into two parts: data loading/pre-processing and the actual algorithm. This allows greater re-usability of the algorithms.

    # 4. What to specify?

    The API has a focus on Service and Data Discovery, Data Processing and Result Retrieval/Publishing. Some more customer-oriented tasks such as user registration and payments are not handled through the openEO API. These aspects are too different across providers and can be handled more efficiently through external interfaces. Most providers already offer such services independently of the openEO API implementation anyway to offer their pre-openEO services.

    Still, the openEO API uses OpenID Connect as main Authentication and Authorization mechanism (see also point 5). OpenID Connect (opens new window) has a User Registration extension draft (opens new window) that can be used by back-ends and may be adopted in the future. We closely follow upcoming specifications and standards and are happy to adopt anything useful.

    # File Formats

    Similarly, file formats (opens new window) and their options (opens new window) are often very different between processing software. That is why GDAL exists. To improve interoperability, the openEO API just recommends to align them with GDAL, which is used in most relevant software anyway. Unfortunately, this comes with some small inconveniences. For example, most people would probably expect to just specify "GeoTiff" as file format name, but GDAL has named it "GTiff". Another idea was to use media types (opens new window) such as application/json, but then we realized that these are often to broad or not even defined. For example, openEO had to push the OGC (opens new window) to specify an official media type for GeoTiff. But the same issue exists for other file formats, too.

    # 5. Authentication

    One of the most complex issues we faced during the API development was actually not EO or data processing related. It was the Authentication and Authorization mechanism. We quickly decided to use one of the well-established standards available for security reasons. Unfortunately, most of them don't seem to cater very well for our use case where you have a number of different clients and back-ends communicating with each other. Usually in the Web you have multiple clients communicating with a single back-end (1:n), but in openEO we have a n:m relation. There are CLI tools, web clients and more that need to authenticate against a growing set of independent providers. Therefore, you can't easily ship clients with security measures like Client IDs (and Client Secrets) as required by OpenID Connect (opens new window) (based on OAuth 2 (opens new window)). Users need to get those information by themselves and provide it to the clients, which is inconvenient and probably confusing to many non-developers. This issue is probably the thing I dislike most in the current API specification and thus would love to hear from you whether there are good solutions out there that we missed.

    # 6. Debugging Experience

    The logging and debugging experience varies a lot between cloud processing and the "traditional workflow" (you download data and compute locally with your preferred tools). While it's "free" on a local machine to just do "trial and error" to check whether something is running, cloud processing usually comes with a cost and you likely don't want to work with "trial and error". Also you don't directly have access to the hardware and software and thus it is harder to investigate problems. openEO took several measures to mitigate these issues, but it is still a different experience for users. I guess that is a long learning process until it is adopted by users and may need additional effort to implement tools to improve the situation. While many users are getting used to it in situations where a streamlined experience is somewhat possible as only few clients communicate with a single provider (e.g. Google Earth Engine), the issue increases in openEO due to the different infrastructure and software used on back-end side.

    # 7. Defining interoperable Processes

    Defining a common set of pre-defined processes was a very long and challenging task.

    1. First, we had to come up with a useful schema that allowed us to describe processes in JSON. We got inspired by different other standards such as WPS (opens new window) and OpenAPI (opens new window).
    2. Second, we had to find what processes are required and which granularity they should have. That quickly led to over a hundred processes (opens new window), mainly for data cube and math operations. And that was only for mostly raster operations. We still have a big hole in the processes regarding vector related operations, but we aim to close that in the next year(s).
    3. Third, we had to figure out what common processing softwares offered and how to come up with process definitions than can be implemented with those softwares.
    4. Last, we had to specify it in a way that is unambiguous and easy to understand by users.

    All of these tasks took much more time than initially expected. For example, one of the partners reported that the granularity of the processes is not really suited for the underlying processing software. While we couldn't really change the granularity, it was at least not an issue to make openEO work for them. Fortunately, the process part of the openEO API is very flexible and allows providers to change the processes according to their needs or define their own processes. Due to the fact that the actual parameters and return values are described in JSON Schema, clients and back-ends can easily re-use existing JSON Schema implementations and adopt to changes in the process specifications. Thus, if a back-end doesn't support parts of a process, it can just be changed/removed and a client can understand that and cater for it.

    # Conclusion

    This was a deep dive into some of my challenges during the last years. I hope it is useful for others and I'm hoping to get into discussions with developers facing similar issues. Maybe with joint forces we can solve some of these for a better cloud processing experience in the future. If you have anything to say, please contact me via e-mail, GitHub issues (API (opens new window), Processes (opens new window)) or Twitter (opens new window). You can also contact openEO.

    - + diff --git a/news/2020-07-31-openeo-api-released.html b/news/2020-07-31-openeo-api-released.html index 5ec7ef5e3..e63530283 100644 --- a/news/2020-07-31-openeo-api-released.html +++ b/news/2020-07-31-openeo-api-released.html @@ -8,7 +8,7 @@ - + @@ -199,6 +199,6 @@

    Stable release of the openEO API and Processes (v1.0.0)

    Written on July 31, 2020 by Matthias Mohr.

    After two release candidates in early 2020, the openEO Consortium released the first stable version of its openEO API (opens new window) and their set of common processes (opens new window). This blog post gives an overview of the most notable changes since version 0.4 and provides an outlook on the next months' project activity.

    For those who are new to openEO, we have developed an open API to connect R, Python, JavaScript and other clients to big Earth observation cloud back-ends in a simple and unified way. The idea is that this will make cloud-based processing of large satellite image collections, or other data cubes such as weather model data easier for data scientists. The entire development process has been open, and all software developed in this project is open source and available on GitHub (opens new window). For more information about openEO, please read page about the project first.

    # Processes

    The most important change was introduced just recently in openEO API version 1.0.0 RC2. Until then, openEO process graphs allowed users to express processing algorithms and execute them on a back-end using pre-defined processes exposed by a back-end provider. While this still exists, a user can now also promote a process graph to a full openEO process description, following the same schema as the pre-defined processes: it must include the processing instructions and metadata like an identifier, parameters, return values, examples etc. This allows user-defined processes to be stored and used like pre-defined processes, enabling users to extend the processing capabilities of a back-end. Users can now encapsulate algorithms in separate user-defined processes and re-use these in other user-defined processes. This also allows the exchange of processes (algorithms) between users and back-ends, and creates the possibility for a repository of user-contributed processes that other users can benefit from. A place for finding and sharing such processes is (planned to be) the openEO Hub (opens new window).

    Through user-defined processes (and user defined functions) users may also be able to substitute processes that back-ends have not implemented. In fact, several pre-defined processes are defined with alternative processing instructions that use other openEO pre-defined processes to solve the task. For example, the pre-defined process normalized_ difference can be emulated with a combination of the processes add, subtract and divide. So if a back-end is missing the normalized_difference process, a user can just download it (opens new window) and use an openEO client to push it to the back-end where it then acts as drop-in replacement for the missing pre-defined process. In the future we plan to allow just using the URL of the process, so that no download/upload would be required.

    As mentioned above, openEO offers a set of common processes (opens new window) to improve interoperability and portability between back-ends. This is not a complete list and is expected to be extended by the openEO Consortium. More than that, we would like to see that processes arise from the community and that we can take over and standardize whatever comes up and is useful for the different domains working with openEO. We can then simply use the user-defined processes as they share the same schema as the pre-defined processes.

    The pre-defined openEO processes are released separately from the API to ensure a faster release cycle than we plan for the API. We want to be able to quickly adapt to community needs with additional processes. Future version of the processes are valid with any future API version through the specification of the process metadata standard in openEO.

    The pre-defined processes itself also got an update just after the API release, but the changes (opens new window) are relatively small compared to the API. The focus was to clarify unspecified and ambiguous parts of the specification. Some processes such as apply_neighborhood and constant were added for the use cases. In collaboration with Julia Wagemann (opens new window) from the ECMWF (opens new window) we started defining processes such as aggregate_temporal_frequency, anomaly and climatological_normal for climatology and meteorology use-cases. Unfortunately, this effort could not be finished yet due to the COVID-19 situation. We want to continue to broaden the processes to be useful for more domains apart from core Earth Observation.

    # STAC and OGC APIs

    The openEO API now supports STAC (API) versions 0.9 and 1.x, which implements OGC API - Features. Thus, a potential integration of the openEO API with upcoming OGC API standards should be easy to establish.

    # User-defined Functions (UDFs)

    Note: Don't get confused, user-defined processes and user-defined functions describe different things.

    Although already foreseen in API version 0.4.0, the openEO UDF API has evolved and is better integrated in the openEO API. It now allows to run Docker containers or custom scripts in potentially any programming language. The UDF API itself is not part of the openEO API, but is a separate specification to describe the data exchange between a back-end implementing the openEO API and a UDF API instance. The UDF API is in release candidate phase and we'll likely post a separate blog post focussing solely on them. Currently, implementations in R and Python are available. It is foreseen that users could also host their own UDF API instances and call them from remote back-ends.

    # Other improvements

    Other notable changes and additions:

    • Support for importing user-provided data sets has been added.
    • Providers can now expose their terms of service and privacy policies.
    • The WebSocket-based Subscription API for notifications and monitoring introduced in version 0.4 has been replaced by a logging mechanism in the HTTP API. This makes implementing the API easier for clients and back-ends, but doesn't work in real-time any longer.

    The full set of changes can be found in the API's change log (opens new window).

    # Limitations

    With openEO, we are digging into new grounds regarding the interoperability of big Earth observation clouds. While we could solve a lot of issues that came up, there were some issues that we couldn't solve as part of the API and processes. We want to be honest and give some insights into the lessons learned, so I have posted these as a separate blog post.

    # Next steps

    The API's development went through several iterations and is working well for our broad range of use cases, which we'll report soon. Implementations by several organizations for several API versions provide evidence that the API and the processes are useful for a wide range of use cases. Client and back-end developers will now finish their implementations and we hope to have the first operational services this year. We will follow up with new blog posts once other releases are available. If you are interested in trying out openEO, please contact us to get more information.

    - + diff --git a/news/2020-09-02-openeo-user-workshop.html b/news/2020-09-02-openeo-user-workshop.html index e5eb88ffa..87fa00100 100644 --- a/news/2020-09-02-openeo-user-workshop.html +++ b/news/2020-09-02-openeo-user-workshop.html @@ -8,7 +8,7 @@ - + @@ -199,6 +199,6 @@

    • openEO Virtual User Workshop

    openEO Virtual User Workshop

    Written on September 2, 2020 by Matthias Schramm.

    # UPDATE:

    Presentations and Notebooks of the user workshop can be downloaded here (opens new window).

    openEO hosts a (virtual) user workshop on 19 October 2020 (10:30 - 16:30 CEST in two parts) and encourages interested parties to register for this event at https://ec.europa.eu/eusurvey/runner/openEOUserWorkshop (opens new window).

    Needed preparation: Manuals for installing the various client APIs, recommended for testing openEO in guided sessions, can be found in the User Documentation under Getting Started.

    User questionnaire: Please help us also by spending some minutes for this user questionnaire (opens new window).

    Access details: Please click the session topics below to connect to the corresponding sessions in Microsoft Teams.

    Agenda:

    Part 1
    No. Start Duration Topic Remark
    1.1 10:30h 30min Introduction into openEO + technical overview
    1.2 11:00h 20min Overview backends
    1.3 11:20h 20min Overview processes + documentation
    1.4 11:40h 5min Installation of openEO
    1.5 11:45h 15min Q/A: Backends, Installation, Q/A Chat Questionaire
    Break for users to install openEO, partners stay available in chat for answering questions on that in bilateral talks
    Part 2
    No. Start Duration Topic Remark
    2.1 14:00h 5min Welcome back / Ways for users to approach to openEO
    Live Demontrations, Part 1
    2.2a 14:05h 20min Python Client Parallel session
    2.2b 14:05h 20min JavaScript Client + mobile application
    14:25h 5min Short break / Switching sessions
    Live Demontrations, Part 2
    2.3a 14:30h 20min Web Editor + QGIS Parallel session
    2.3b 14:30h 20min R Client
    14:50h 5min Short break / Switching sessions
    Guided Sessions and Q/A
    2.4a 14:55h 60min Guided room for users to test the R Client Parallel session
    2.4b 14:55h 60min Guided room for users to test the Python Client
    2.4c 14:55h 60min Guided room for users to test other clients
    2.5a 15:55h 15min Q/A and Discussions: R Client Parallel session
    2.5b 15:55h 15min Q/A and Discussions: Python
    2.5c 15:55h 15min Q/A and Discussions: Other clients
    Wrap up
    2.6 16:10h 20min Q/A; Discussion on user needs, future work

    Background: openEO is a user-driven open source API, which grants a uniform communication between the Earth Observation (EO) data user community and diverse EO cloud service providers. Thus, it aims for a cross-platform interoperability to ease a comparison, the cooperation and the switch between them. The project consortium (H2020 Grant No 776242) released a stable version of the openEO API, covering standardised online processing of all aspects of an EO data life cycle on diverse cloud platforms.

    In this User Workshop, the audience is provided with insight into the functionalities of the openEO API. In various live demonstration and training sessions, real demonstration cases will be explained from the user perspective. Possible applications of the openEO API will be presented for different access levels – from the use of programming languages to browser based model builders or mobile applications. Target audience for this event are programmers, decision makers, as well as third-party cloud EO service providers.

    - + diff --git a/news/2020-11-30-openeo-api-101-released.html b/news/2020-11-30-openeo-api-101-released.html index aaa0eb52d..686bb1859 100644 --- a/news/2020-11-30-openeo-api-101-released.html +++ b/news/2020-11-30-openeo-api-101-released.html @@ -8,7 +8,7 @@ - + @@ -205,6 +205,6 @@ issues with credentials in web browsers. Back-ends are advised to check their implementations against the new specification and update the CORS headers accordingly. The web clients have already been updated accordingly.

    More details about the release:

    - + diff --git a/news/2021-05-17-openeo-api-110-released.html b/news/2021-05-17-openeo-api-110-released.html index ef09a4346..ac53d2254 100644 --- a/news/2021-05-17-openeo-api-110-released.html +++ b/news/2021-05-17-openeo-api-110-released.html @@ -8,7 +8,7 @@ - + @@ -208,6 +208,6 @@ For several parts exposed by the API can now be declared to be experimental or deprecated. Finally, the API specification also clarifies several ambiguous parts of the specification.

    Back-ends and clients are advised to check their implementations against the new specification and update accordingly.

    More details about the release:

    - + diff --git a/news/2021-11-08-r-client-on-cran.html b/news/2021-11-08-r-client-on-cran.html index 3785792c4..fd6c9dd61 100644 --- a/news/2021-11-08-r-client-on-cran.html +++ b/news/2021-11-08-r-client-on-cran.html @@ -8,7 +8,7 @@ - + @@ -201,6 +201,6 @@

    Today, we released a new version 1.1.0 of the openEO R Client, which features a couple of improvements and bug fixes (opens new window).

    At the same time, the R client has been published on CRAN, too: openeo (opens new window) This means you can now install the R client more easily and directly through CRAN with the following command:

    install.packages("openeo")

    If you have trouble installing or working with the package, feel free to leave an issue in the GitHub issue tracker (opens new window).

    - + diff --git a/news/2022-04-08-psc-meetings.html b/news/2022-04-08-psc-meetings.html index fd3815e86..519479c2c 100644 --- a/news/2022-04-08-psc-meetings.html +++ b/news/2022-04-08-psc-meetings.html @@ -8,7 +8,7 @@ - + @@ -203,6 +203,6 @@ We will always discuss the open issues in the PSC issue tracker (opens new window) and talk through the future and strategic directions openEO is heading towards. The meetings are open to the public and anyone can join as a guest. If you'd like to join the next meeting, please contact openeo.psc@uni-muenster.de and we'll send you an invite.

    - + diff --git a/news/2022-05-12-r-client-v120.html b/news/2022-05-12-r-client-v120.html index f73c1cd39..f40426a0a 100644 --- a/news/2022-05-12-r-client-v120.html +++ b/news/2022-05-12-r-client-v120.html @@ -8,7 +8,7 @@ - + @@ -218,6 +218,6 @@ ) )

  • The login procedure was greatly simplified for back-ends that support this new behavior. For example, on openEO Platform you can now login simply by calling the login method as such login() instead of passing a client ID and secret tom connect() or login(). It is still possible, but not required any more. It will then interactively guide you through the login procedure. It is now also supported to login in environments that are remotely hosted (e.g. RStudio Cloud, Jupyter).

  • Getting the JSON representation of a process is now simplified. The new recommended way is toJSON(as(result, "Process")) where result is the last node of your process, which usually is save_result.

  • Directly load data into stars (opens new window) if you set as_stars=TRUE in compute_result

    • - + diff --git a/news/2022-07-07-monthly-dev-calls.html b/news/2022-07-07-monthly-dev-calls.html index 9b7b7f1ed..9af5254c5 100644 --- a/news/2022-07-07-monthly-dev-calls.html +++ b/news/2022-07-07-monthly-dev-calls.html @@ -8,7 +8,7 @@ - + @@ -204,6 +204,6 @@ know each other. In the past there have been project-specific developer calls, but we would like to build bridges between projects and coordinate the work better.

    If you'd like to join these calls, please send an e-mail to openeo.psc@uni-muenster.de. Everyone is welcome!

    - + diff --git a/news/2022-11-17-r-client-v130.html b/news/2022-11-17-r-client-v130.html index 3c1fbc27b..e208b0a67 100644 --- a/news/2022-11-17-r-client-v130.html +++ b/news/2022-11-17-r-client-v130.html @@ -8,7 +8,7 @@ - + @@ -218,6 +218,6 @@ We describe the details on how to create process graphs and respectively "User Defined Processes" (UDP). Those processes can be seen as the analysis workflows that are run as jobs at a designated openEO back-end. They might be called later directly or some variables can be added to the UDP to customize the processes at runtime, e.g. set the temporal interval or the area of interest. This vignette focuses on the user point of view.

  • Package Software Architecture (opens new window) This is the first part of a contribution guide which ensures that developers can effectively contribute to the R-Client. Since a server-client based package is different from most other R packages some concepts are used that most R users and developers are not so familiar with. Therefore, this contribution guide eases the process of actively contributing to the packages development or bug fixing by explaining the underlying concepts.

  • Developer Implementation Details (opens new window) In this guide selected core mechanisms of the openEO package are described. It is targeted towards interested developers and it is highly recommended to dive into the source code, while reading through this guide. The explanations here are abstracted from the code and shall guide new developers on the concepts and routines of this package.

    • - + diff --git a/news/2023-05-25-new-openeo-versions-120-200rc1 copy.html b/news/2023-05-25-new-openeo-versions-120-200rc1 copy.html index 929ed537d..3f738536b 100644 --- a/news/2023-05-25-new-openeo-versions-120-200rc1 copy.html +++ b/news/2023-05-25-new-openeo-versions-120-200rc1 copy.html @@ -8,7 +8,7 @@ - + @@ -204,6 +204,6 @@
  • inspect: The parameter message has been moved to be the second argument. #369 (opens new window)
  • New definition for aggregate_spatial:
  • Updated the processes based on the subtypes raster-cube or vector-cube to work with the subtype datacube instead. #68 (opens new window)
  • sort and order: The ordering of ties is not defined anymore. #409 (opens new window)
  • quantiles: Parameter probabilities provided as array must be in ascending order. #297 (opens new window)
  • fit_curve and predict_curve: Heavily modified specifications. fit_curve works on arrays instead of data cubes, predict_curve doesn't support gap filling anymore, clarify no-data handling, ... #425 (opens new window)
  • climatological_normal: The climatology_period parameter accepts an array of integers instead of strings. #331 (opens new window)
  • between: Support for temporal comparison. Use date_between instead. #331 (opens new window)
  • Deprecated GeometryCollections are not supported any longer. #389 (opens new window)
  • Deprecated PROJ definitions for the CRS are not supported any longer.
  • The comparison processes eq, neq, lt, lte, gt, gte and array_contains:
    • Removed support for temporal comparison. Instead explicitly use date_difference.
    • Removed support for the input data types array and object. #208 (opens new window)
  • sort and order: Removed support for time-only values. #331 (opens new window)
    • - + diff --git a/news/2023-12-06-openeo-submitted-to-ogc.html b/news/2023-12-06-openeo-submitted-to-ogc.html index 5857636b9..a9f2aeb86 100644 --- a/news/2023-12-06-openeo-submitted-to-ogc.html +++ b/news/2023-12-06-openeo-submitted-to-ogc.html @@ -8,7 +8,7 @@ - + @@ -202,6 +202,6 @@ More specifically, the submission document (opens new window) justifies why the openEO API specification (version 1.2.0) and the openEO process specification (version 1.2.0) are - from our point of view - a good candidate for an OGC community standard. We hope OGC accepts our documents and establishes the corresponding procedures soon.

    The members of the openEO PSC voted to submit the justification document to OGC by unanimous consent. We are glad that the following OGC members confirmed to support the OCG submmissin of openEO:

    • University of Münster - Institute for Geoinformatics
    • Eurac Research
    • VITO (Flemish Institute for Technological Research)
    • GeoConnections - Natural Resources Canada
    • EUMETSAT
    • European Space Agency (ESA)
    • EOX IT Services GmbH
    • Telespazio VEGA UK Ltd
    • Planet Labs PBC
    • German Aerospace Center – DLR
    • Matthias Mohr - Softwareentwicklung

    We'll keep you updated about the progress. The next step is likely a public request for comments.

    - + diff --git a/news/2024-02-06-openeo-ogc-rfc.html b/news/2024-02-06-openeo-ogc-rfc.html index b44cba8f1..e14b64937 100644 --- a/news/2024-02-06-openeo-ogc-rfc.html +++ b/news/2024-02-06-openeo-ogc-rfc.html @@ -8,7 +8,7 @@ - + @@ -202,6 +202,6 @@ OGC now seeks public comment on the potential adoption of openEO as an OGC Community Standard. Comments are due by February 27, 2024.

    For more details, please read the press release that was published today by OGC: Request for Comments: OGC considering openEO as a Community Standard; comment sought on its adoption (opens new window)

    openEO will also be presented at the next OGC member meeting in Delft on Thu, 28 Mar 2024 during the closing planery.

    We'll keep you updated about the progress.

    - + diff --git a/news/2024-03-22_mooc_cubeandclouds.html b/news/2024-03-22_mooc_cubeandclouds.html index c7c635c85..9fc932ca2 100644 --- a/news/2024-03-22_mooc_cubeandclouds.html +++ b/news/2024-03-22_mooc_cubeandclouds.html @@ -8,7 +8,7 @@ - + @@ -199,6 +199,6 @@

    • MOOC Cubes and Clouds - open for registration

    MOOC Cubes and Clouds - open for registration

    Written on March 22, 2024 by Peter Zellner.

    The latest EO college course “Cubes and Clouds” explores the newest concepts in Earth Observation through engaging lectures, videos, animated content, and hands-on exercises!

    openEO plays a main role in the course due to its standardized API for homogenizing cloud processing. It allows for portablility of the created workflow between different cloud providers, reproducibility and sharing of the workflow which is enriched with valuable metadata. The idea behind and need for openEO are tought in a dedicated lecture. All hands-on exercises use openEO, guiding participants from basic processes to carrying out end-to-end EO workflows and sharing their results!

    You can subscribe to the course for free and learn about cloud native EO at Cubes & Clouds – EO College (eo-college.org) (opens new window).

    - + diff --git a/news/index.html b/news/index.html index a4a9f1d2a..d99a72b32 100644 --- a/news/index.html +++ b/news/index.html @@ -8,7 +8,7 @@ - + @@ -220,10 +220,10 @@ openEO API v1.0.1 released
  • September 2, 2020
    openEO Virtual User Workshop -
  • July 31, 2020
    - Stable release of the openEO API and Processes (v1.0.0)
  • July 31, 2020
    Lessons learned while developing the openEO API +
  • July 31, 2020
    + Stable release of the openEO API and Processes (v1.0.0)
  • October 7, 2019
    H2020 EO Big Data Hackathon Nov 7-8
  • March 7, 2019
    @@ -241,6 +241,6 @@
  • December 5, 2017
    Welcome to openEO!
    • Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
    - + diff --git a/psc.html b/psc.html index 40030c9ee..410a18400 100644 --- a/psc.html +++ b/psc.html @@ -8,7 +8,7 @@ - + @@ -197,6 +197,6 @@
    Contact

    # openEO Project Steering Committee

    This document describes how the openEO Project Steering Committee determines membership, and makes decisions on openEO project issues. The openEO project is concerned with all repositories (software and specifications etc.) found under https://github.com/Open-EO (opens new window).

    In brief the committee votes on proposals on https://github.com/Open-EO/PSC/issues (opens new window). Proposals are available for review for at least five business days, and a single veto is sufficient to delay progress though ultimately a majority of members can pass a proposal.

    # Members

    The list of members of the Project Steering Committee is:

    The current list will be maintained by the openEO chair on this page and will be published to the openEO web pages, too.

    # Detailed Process

    1. Proposals are written up and submitted on https://github.com/Open-EO/PSC/issues/new/choose (opens new window) for discussion and voting, by any interested party, not just committee members.
    2. Proposals need to be available for review for at least five business days before a final decision can be made. Public holidays count as business days.
    3. Respondents may vote +1 to indicate support for the proposal and a willingness to support implementation.
    4. Respondents may vote -1 to veto a proposal, but must provide clear reasoning and alternate approaches to resolving the problem within the five business days.
    5. A vote of -0 indicates mild disagreement, but has no effect. A 0 indicates no opinion. A +0 indicates mild support, but has no effect.
    6. Anyone may comment on proposals on the list, but only members of the Project Steering Committee’s votes will be counted.
    7. A proposal will be accepted if it receives +4 (including the proposer) and no vetoes (-1) or has not received enough votes in 20 business days.
    8. If a proposal is vetoed, and it cannot be revised to satisfy all parties, then it can be resubmitted for an override vote in which a majority of all eligible voters indicating +1 is sufficient to pass it. Note that this is a majority of all committee members, not just those who actively vote.
    9. Upon completion of discussion and voting the proposer should announce whether they are proceeding (proposal accepted) or are withdrawing their proposal (vetoed).
    10. The Chair gets a vote.
    11. The Chair is responsible for keeping track of who is a member of the Project Steering Committee.
    12. Addition and removal of members from the committee, as well as selection of a Chair should be handled as a proposal to the committee.
    13. The Chair adjudicates in cases of disputes about voting.

    # When is Vote Required?

    Generally speaking, technical decisions should be made by consensus within openEO components. However, if a decision cannot be decided by consensus of the committers involved in the component, any committer can request the decision be brought to a vote of the openEO PSC. Technical decision that would be suitable for such a process include:

    • Any change to PSC membership (new members, removing inactive members)
    • Anything that could cause backward compatibility issues in normative specifications (e.g., API or process specifications).
    • Adding or changing substantial amounts of the normative specifications.
    • Issues of procedure (e.g. changes to these guidelines).
    • When stable releases should take place.
    • Strategic decisions regarding the project and anything that might be controversial.

    # Final remarks

    • The Chair is the ultimate adjudicator if things break down.
    • The absolute majority rule can be used to override an obstructionist veto, but it is intended that in normal circumstances vetoers need to be convinced to withdraw their veto. We are trying to reach consensus.
    • Software components that at least two other software repositories under https://github.com/Open-EO (opens new window) depend on, are encouraged to implement a review system for breaking changes (e.g. GitHub Pull Request Approvals) that require a certain number of approvals: 2 dependents require 1 approval, more than 2 dependents require 2 approvals. Exceptions for this must go through the PSC process.

    # Committee Membership

    # Adding Members

    Anyone with a contribution to the openEO organization (opens new window) may nominate someone for committee membership at any time. Only existing PSC committee members may vote on new members. Nominees must receive a majority vote from existing members to be added to the PSC.

    # Stepping Down

    If for any reason a PSC member is not able to fully participate then they certainly are free to step down. If a member is not active (e.g., no voting or email participation) for a period of 12 months then the PSC reserves the right to seek nominations to fill that position. Should that person become active again then they would certainly be welcome, but would require a nomination.

    # History

    This document is based on the GDAL Project Management Committee Guidelines (opens new window) and was accepted by the openEO project board on October, 7 2020.

    The following changes have been made thereafter: https://github.com/Open-EO/PSC/compare/v1...main (opens new window)

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    # Software

    All developed software is organized within the openEO GitHub organization (opens new window).

    # Clients

    Software that allows remote sensers and other users to access the openEO back-ends:

    Client software Package Manager / Hosted Version GitHub
    JavaScript/TypeScript library npm: @openeo/js-client (opens new window) openeo-js-client (opens new window)
    Python library PyPI: openeo (opens new window) / Conda Forge: openeo (opens new window) openeo-python-client (opens new window)
    QGIS plugin QGIS plugin repository: openeo-qgis-plugin-master (opens new window) openeo-qgis-plugin (opens new window)
    R library CRAN: openeo (opens new window) openeo-r-client (opens new window)
    Web Editor Hosted Version (opens new window) openeo-web-editor (opens new window)

    See the getting started guide for users for more information. Users considering to implement a new client library should read the getting started guide for client developers.

    # Back-ends

    Data and infrastructure providers can host their own instance of the openEO API:

    See the getting started guide for back-end providers for more information.

    To start developing a new back-end driver, you may start with any of the common functionalities implemented in several programming languages:

    Our approach to tackle language-agnostic user-defined functions can be found in the following repositories:

    # Specification

    The API specification is available in the openEO API repository (opens new window). The process definitions are available in the openEO processes repository (opens new window).

    # Ecosystem

    Edit this page (opens new window)
    Last Updated: 7/11/2024, 10:57:13 PM
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