diff --git a/404.html b/404.html index 60f948ee6..2addca617 100644 --- a/404.html +++ b/404.html @@ -8,13 +8,13 @@ - +

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- + diff --git a/api/index.html b/api/index.html index 219547c8d..19c8d87d4 100644 --- a/api/index.html +++ b/api/index.html @@ -8,7 +8,7 @@ - + @@ -145,6 +145,6 @@
Contact (opens new window)
- + diff --git a/assets/js/51.7739e561.js b/assets/js/51.79fc567a.js similarity index 99% rename from assets/js/51.7739e561.js rename to assets/js/51.79fc567a.js index bdacb3703..833ef471c 100644 --- a/assets/js/51.7739e561.js +++ b/assets/js/51.79fc567a.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[51],{316:function(e,t,s){e.exports=s.p+"assets/img/federation.a955d770.png"},488:function(e,t,s){"use strict";s.r(t);var a=s(4),n=Object(a.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"federation-aspects-and-known-issues"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#federation-aspects-and-known-issues"}},[e._v("#")]),e._v(" Federation Aspects and Known Issues")]),e._v(" "),t("p",[e._v("openEO Platform is a federated platform. This means that multiple independent 'back-ends',\nwhich all support the openEO interface, are combined into a single instance. From the outside,\nit appears to be a single platform, but you get access to data collections and processing resources from\nmultiple instances.")]),e._v(" "),t("p",[e._v("This image gives you a look at platforms that make up the federation:")]),e._v(" "),t("p",[t("img",{attrs:{src:s(316),alt:"openeo.cloud federation"}})]),e._v(" "),t("p",[e._v("At this time, the federation can not yet entirely hide the fact that it is built out of\nseparate components:")]),e._v(" "),t("ul",[t("li",[e._v("Within the same processing request, you can only use collections from the same back-end")]),e._v(" "),t("li",[e._v("Some processes are not (fully) supported by all back-ends.")]),e._v(" "),t("li",[e._v("If a back-end requires data from an external source, bandwidth limitations may result in slower processing.")])]),e._v(" "),t("h2",{attrs:{id:"data-collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#data-collections"}},[e._v("#")]),e._v(" Data Collections")]),e._v(" "),t("p",[e._v("The federation exposes the "),t("em",[e._v("union")]),e._v(" of the data collections of each of the underlying back-ends.\nWhen a processing request is submitted to the federated platform, the input collections are used to determine to which back-end the actual processing work should be delegated to.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Note")]),e._v(" "),t("p",[e._v("For the technical discussion on collection federation, see "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-aggregator/issues/5",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open-EO/openeo-aggregator#5"),t("OutboundLink")],1)])]),e._v(" "),t("h3",{attrs:{id:"terrascope"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#terrascope"}},[e._v("#")]),e._v(" Terrascope")]),e._v(" "),t("p",[e._v("Terrascope hosts a number of collections itself.\nFor coarse resolution data (e.g. 100 m resolution) this is often the full archive,\nwhile for medium resolution (Sentinel 1, 2) data is only offered for selected areas.")]),e._v(" "),t("p",[e._v("Additional data can be processed upon request, if it is not available from another provider.\nThis may result in an additional cost for processing and storage.")]),e._v(" "),t("h3",{attrs:{id:"sentinel-hub"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#sentinel-hub"}},[e._v("#")]),e._v(" Sentinel Hub")]),e._v(" "),t("p",[e._v("The Terrascope back-end also integrates with Sentinel Hub (part of Euro Data Cube) to give you\naccess to additional collections.\nThis practically means that data needs to be transferred from Sentinel Hub to the Terrascope data center before it can be processed.\nThis works very well for small areas, or a 100x100km MGRS tile in batch mode,\nbut is not yet recommended for processing medium size to large countries or continents.")]),e._v(" "),t("p",[e._v("The collection metadata of the Terrascope back-end tries to clearly identify which collections are served by Sentinel Hub.")]),e._v(" "),t("h4",{attrs:{id:"commercial-data"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#commercial-data"}},[e._v("#")]),e._v(" Commercial Data")]),e._v(" "),t("p",[e._v("openEO Platform provides direct access to commercial data. Currently, data must be purchased directly through Sentinel Hub (see Sentinel Hub documentation on purchasing commercial data "),t("a",{attrs:{href:"https://docs.sentinel-hub.com/api/latest/api/data-import/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v("), but we are working to support ordering commercial data directly from the platform in the future.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Experimental usage")]),e._v(" "),t("p",[e._v("The below described way of how to connect to commercial data is currently only supported by the pyhton client and experimental. As such the behavior might still change in the future.")])]),e._v(" "),t("p",[e._v("Data is accessed as part of the load_collection process and via a "),t("code",[e._v("featureflags")]),e._v(" argument. To access the data, you must:")]),e._v(" "),t("ul",[t("li",[e._v("select the commercial data provider in "),t("code",[e._v("collection_id")]),e._v(" (e.g. "),t("code",[e._v('collection_id="PLANETSCOPE"')]),e._v(")")]),e._v(" "),t("li",[e._v("set the Sentinel Hub BYOC collection ID ("),t("code",[e._v("byoc-{id}")]),e._v(") as "),t("code",[e._v("featureflags")]),e._v(" argument\n(e.g. with openEO Python client version 0.10.1 or higher:\n"),t("code",[e._v('datacube.result_node().update_arguments(featureflags={"byoc_collection_id": byoc_collection_id})')]),e._v(")")])]),e._v(" "),t("p",[e._v("Full example of loading a commercial data collection:")]),e._v(" "),t("div",{staticClass:"language-python extra-class"},[t("pre",{pre:!0,attrs:{class:"language-python"}},[t("code",[e._v("toc "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" connection"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("load_collection"),t("span",{pre:!0,attrs:{class:"token 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On top, pre-processed Level-2 data is\navailable on request (this may result in additional costs). In detail Gamma0 data processed with SNAP and optical ARD\nproducts processed with FORCE are provided.")]),e._v(" "),t("p",[e._v("If the available pre-processed collections are not sufficient, there is on option to perform ARD processing on demand\nwith SNAP respectively FORCE. This may again result in additional costs for processing and storage.")]),e._v(" "),t("p",[e._v("Currently most processes are only available for Level-2 data. Only the ARD processes can be executed on Level-1 data. Also\nonly either ARD processes "),t("strong",[e._v("or")]),e._v(' "standard" processes can be used in one process graph. Combining both types of processes\nis not yet supported. 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This "),t("code",[e._v("on-demand-preview")]),e._v(" is available in the "),t("a",{attrs:{href:"https://editor.openeo.cloud/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Platform Editor"),t("OutboundLink")],1),e._v(" and the "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/index.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Python Client*"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("In the Editor you can find the functionality in the "),t("code",[e._v("Web Services")]),e._v(" tab. Use the button"),t("code",[e._v("Show on Map")]),e._v(".")]),e._v(" "),t("p",[e._v("In the Python Client, you can use the "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.preview",target:"_blank",rel:"noopener noreferrer"}},[e._v(".preview() method"),t("OutboundLink")],1),e._v(" to create a service with your process graph and display the results in an ipyleaflet Map object. You can find an example implementation in "),t("a",{attrs:{href:"https://github.com/openEOPlatform/sample-notebooks/blob/main/on-demand_preview.ipynb",target:"_blank",rel:"noopener noreferrer"}},[e._v("this Jupyter notebook"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Note")]),e._v(" "),t("p",[e._v("This functionallity currently only works with collections that are also on the Sentinel Hub backend (Provider > Backend > sentinelhub). In order for the map to display meaningful results, your process graph should:")]),e._v(" "),t("ul",[t("li",[e._v("return one or three bands,")]),e._v(" "),t("li",[e._v("a single temporal extent,")]),e._v(" "),t("li",[e._v("and scale the data to a range so that the output format can adequately save it.")])])]),e._v(" "),t("p",[e._v("*You need to have at least version 0.19.0 installed to use this functionality.")]),e._v(" "),t("h2",{attrs:{id:"batch-jobs"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs"}},[e._v("#")]),e._v(" Batch jobs")]),e._v(" "),t("p",[e._v("As discussed above, when the federated platform receives a processing request, such as a batch job,\nit will automatically determine to which back-end this request should be delegated for actual processing.")]),e._v(" "),t("h3",{attrs:{id:"managed-job-splitting"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#managed-job-splitting"}},[e._v("#")]),e._v(" Managed job splitting")]),e._v(" "),t("p",[e._v("In addition to this basic delegation feature, the federation also provides more advanced (pre)processing capabilities.\nFor instance, the federation platform can be instructed to split up a single batch job in multiple sub-jobs\nand distribute these across one or more processing back-ends.\nThe federation platform will automatically create, start and keep track of these sub-jobs\nwhile the user just have to interact with a single job: e.g. check the overall processing status,\ndownload the combined result assets, ...")]),e._v(" "),t("p",[e._v("Currently, spatial tile-based splitting is supported as splitting strategy\nand it can be enabled by providing a specific job option when submitting the batch job.\nFor example, using the openEO Python Client, instruct the federation platform to\nsplit up datacube processing in UTM based tiles of 20km by 20km:")]),e._v(" "),t("div",{staticClass:"language-python extra-class"},[t("pre",{pre:!0,attrs:{class:"language-python"}},[t("code",[e._v("job "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" datacube"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("create_job"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("\n job_options"),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("{")]),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"tile_grid"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"utm-20km"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("}")]),e._v("\n"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),e._v("\n")])])]),t("p",[e._v('This creates a virtual master job on the level of the federation platform and real batch jobs\non the appropriate processing back-ends.\nSubsequent interaction (starting the jobs, polling their status, requesting the result assets, ...)\ncan be done through the "master" '),t("code",[e._v("job")]),e._v(" object created above, in the same way as with normal batch jobs.")]),e._v(" "),t("h3",{attrs:{id:"validity-of-signed-urls-in-batch-job-results"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#validity-of-signed-urls-in-batch-job-results"}},[e._v("#")]),e._v(" Validity of signed URLs in batch job results")]),e._v(" "),t("p",[e._v("Batch job results are accessible to the user via signed URLs stored in the result assets. Within the platform,\nthese URLs have a validity (expiry time) of 7 days. Within these 7 days, the results of a batch job can be accessed\nby any person with the URL. Each time a user requests the results from the job endpoint ("),t("code",[e._v("GET /jobs/{job_id}/results")]),e._v("),\na freshly signed URL (valid for 7 days) is created for the result assets.")]),e._v(" "),t("h3",{attrs:{id:"customizing-batch-job-resources-on-terrascope"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#customizing-batch-job-resources-on-terrascope"}},[e._v("#")]),e._v(" Customizing batch job resources on Terrascope")]),e._v(" "),t("p",[e._v("Jobs running on the (Terrascope) cluster get assigned a default amount of CPU and memory resources. This\nmay not always be enough for your job, for instance when using UDF's. Also for very large jobs, you may want\nto tune your resource settings to optimize for cost.")]),e._v(" "),t("p",[e._v("The example below shows how to start a job with all options set to their default values. It is important to highlight\nthat default settings are subject to change by the backend whenever needed.")]),e._v(" "),t("div",{staticClass:"language-python extra-class"},[t("pre",{pre:!0,attrs:{class:"language-python"}},[t("code",[e._v("job_options "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("{")]),e._v(" \n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"executor-memory"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"2G"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"executor-memoryOverhead"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"3G"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"executor-cores"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token number"}},[e._v("2")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"task-cpus"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token number"}},[e._v("1")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"executor-request-cores"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"400m"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"max-executors"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"100"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"driver-memory"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"8G"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"driver-memoryOverhead"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"2G"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"driver-cores"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token number"}},[e._v("5")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"udf-dependency-archives"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("[")]),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 string"}},[e._v('"logging-threshold"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"info"')]),e._v("\n "),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("}")]),e._v("\ncube"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("execute_batch"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("job_options"),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v("job_options"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),e._v("\n")])])]),t("p",[e._v("This is a short overview of the various options:")]),e._v(" "),t("ul",[t("li",[e._v("executor-memory: memory assigned to your workers, for the JVM that executes most predefined processes")]),e._v(" "),t("li",[e._v("executor-memoryOverhead: memory assigned on top of the JVM, for instance to run UDF's")]),e._v(" "),t("li",[e._v("executor-cores: number of CPUs per worker (executor). The number of parallel tasks is executor-cores/task-cpus")]),e._v(" "),t("li",[e._v("task-cpus: CPUs assigned to a single task. UDF's using libraries like Tensorflow can benefit from further parallellization on the level of individual tasks.")]),e._v(" "),t("li",[e._v("executor-request-cores: this settings is only relevant for Kubernetes based backends, allows to overcommit CPU")]),e._v(" "),t("li",[e._v("max-executors: the maximum number of workers assigned to your job. Maximum number of parallel tasks is "),t("code",[e._v("max-executors*executor-cores/task-cpus")]),e._v(". Increasing this can inflate your costs, while not necessarily improving performance!")]),e._v(" "),t("li",[e._v("driver-memory: memory assigned to the spark 'driver' JVM that controls execution of your batch job")]),e._v(" "),t("li",[e._v("driver-memoryOverhead: memory assigned to the spark 'driver' on top of JVM memory, for Python processes.")]),e._v(" "),t("li",[e._v("logging-threshold: the threshold for logging, set to 'info' by default, can be set to 'debug' to generate much more logging")]),e._v(" "),t("li",[e._v("udf-dependency-archives: an array of urls pointing to zip files with extra dependencies, see below")])]),e._v(" "),t("h4",{attrs:{id:"custom-udf-dependencies"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#custom-udf-dependencies"}},[e._v("#")]),e._v(" Custom UDF dependencies")]),e._v(" "),t("p",[e._v("User defined functions often depend on (specific versions of) libraries or require small auxiliary data files. The UDF specifications do not yet\ndefine a standardized manner to provide this other than having the ability of selecting from a predefined set of 'runtimes' that than again have a predefined configuration.")]),e._v(" "),t("p",[e._v("The Terrascope/Geotrellis backends solve this via the udf-dependency-archives job option, that allows to specify a list of zip files that should be included in the working directory of the UDF.")]),e._v(" "),t("p",[e._v("This enables the following example workflow for Python UDF's:")]),e._v(" "),t("ol",[t("li",[e._v("Create a Python 'virtualenv' with your dependencies")]),e._v(" "),t("li",[e._v("Based on the 'site-packages' directory of the virtualenv, create a zip file with all dependencies")]),e._v(" "),t("li",[e._v("Upload the zip to a url that can be reached by the backend.")]),e._v(" "),t("li",[e._v("In job options, add "),t("code",[e._v("\"udf-dependency-archives\": ['https://yourhost.com/myEnv.zip#tmp/mydir']")]),e._v(" The "),t("code",[e._v("#tmp/mydir")]),e._v(" suffix indicates where you want to unzip your files, relative to the working directory.")]),e._v(" "),t("li",[e._v("In your UDF, before trying to import libraries, add your directory to the Python path: "),t("code",[e._v("sys.path.insert(0, 'tmp/mydir')")])]),e._v(" "),t("li",[e._v("Now your libraries should be loaded before anything else!")])]),e._v(" "),t("p",[e._v("Known limitations:")]),e._v(" "),t("ul",[t("li",[e._v("Your dependencies need to be compatible with the Python version of the backend, currently 3.8.")]),e._v(" "),t("li",[e._v("Your dependencies need to be compatible with the OS of the backend, currently AlmaLinux 8.")]),e._v(" "),t("li",[e._v("The backend has a limited set of Python dependences that are preloaded, and cannot be changed, such as numpy.")])]),e._v(" "),t("h4",{attrs:{id:"learning-more"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#learning-more"}},[e._v("#")]),e._v(" Learning more")]),e._v(" "),t("p",[e._v("The topic of resource optimization is a complex one, and here we just give a short summary. The goal of openEO is to hide most of these\ndetails from the user, but we realize that advanced users sometimes want to have a bit more insight, so in the spirit of being open, we give some hints.")]),e._v(" "),t("p",[e._v("To learn more about these options, we point to the piece of code that handles this:")]),e._v(" "),t("p",[e._v("https://github.com/Open-EO/openeo-geopyspark-driver/blob/faf5d5364a82e870e42efd2a8aee9742f305da9f/openeogeotrellis/backend.py#L1213")]),e._v(" "),t("p",[e._v("Most memory related options are translated to Apache Spark configuration settings, which are documented here:")]),e._v(" "),t("p",[e._v("https://spark.apache.org/docs/3.3.1/configuration.html#application-properties")]),e._v(" "),t("h3",{attrs:{id:"batch-job-results-on-sentinel-hub"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#batch-job-results-on-sentinel-hub"}},[e._v("#")]),e._v(" Batch job results on Sentinel Hub")]),e._v(" "),t("p",[e._v("If you are processing data and the underlying back-end is Sentinel Hub, the output extent of your batch job results is currently larger than your input extent because Sentinel Hub processes whole tiles (this may change in the future and the data will be cropped to your input extent).")])],1)}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[51],{316:function(e,t,s){e.exports=s.p+"assets/img/federation.a955d770.png"},490:function(e,t,s){"use strict";s.r(t);var a=s(4),n=Object(a.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"federation-aspects-and-known-issues"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#federation-aspects-and-known-issues"}},[e._v("#")]),e._v(" Federation Aspects and Known Issues")]),e._v(" "),t("p",[e._v("openEO Platform is a federated platform. This means that multiple independent 'back-ends',\nwhich all support the openEO interface, are combined into a single instance. From the outside,\nit appears to be a single platform, but you get access to data collections and processing resources from\nmultiple instances.")]),e._v(" "),t("p",[e._v("This image gives you a look at platforms that make up the federation:")]),e._v(" "),t("p",[t("img",{attrs:{src:s(316),alt:"openeo.cloud federation"}})]),e._v(" "),t("p",[e._v("At this time, the federation can not yet entirely hide the fact that it is built out of\nseparate components:")]),e._v(" "),t("ul",[t("li",[e._v("Within the same processing request, you can only use collections from the same back-end")]),e._v(" "),t("li",[e._v("Some processes are not (fully) supported by all back-ends.")]),e._v(" "),t("li",[e._v("If a back-end requires data from an external source, bandwidth limitations may result in slower processing.")])]),e._v(" "),t("h2",{attrs:{id:"data-collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#data-collections"}},[e._v("#")]),e._v(" Data Collections")]),e._v(" "),t("p",[e._v("The federation exposes the "),t("em",[e._v("union")]),e._v(" of the data collections of each of the underlying back-ends.\nWhen a processing request is submitted to the federated platform, the input collections are used to determine to which back-end the actual processing work should be delegated to.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Note")]),e._v(" "),t("p",[e._v("For the technical discussion on collection federation, see "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-aggregator/issues/5",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open-EO/openeo-aggregator#5"),t("OutboundLink")],1)])]),e._v(" "),t("h3",{attrs:{id:"terrascope"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#terrascope"}},[e._v("#")]),e._v(" Terrascope")]),e._v(" "),t("p",[e._v("Terrascope hosts a number of collections itself.\nFor coarse resolution data (e.g. 100 m resolution) this is often the full archive,\nwhile for medium resolution (Sentinel 1, 2) data is only offered for selected areas.")]),e._v(" "),t("p",[e._v("Additional data can be processed upon request, if it is not available from another provider.\nThis may result in an additional cost for processing and storage.")]),e._v(" "),t("h3",{attrs:{id:"sentinel-hub"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#sentinel-hub"}},[e._v("#")]),e._v(" Sentinel Hub")]),e._v(" "),t("p",[e._v("The Terrascope back-end also integrates with Sentinel Hub (part of Euro Data Cube) to give you\naccess to additional collections.\nThis practically means that data needs to be transferred from Sentinel Hub to the Terrascope data center before it can be processed.\nThis works very well for small areas, or a 100x100km MGRS tile in batch mode,\nbut is not yet recommended for processing medium size to large countries or continents.")]),e._v(" "),t("p",[e._v("The collection metadata of the Terrascope back-end tries to clearly identify which collections are served by Sentinel Hub.")]),e._v(" "),t("h4",{attrs:{id:"commercial-data"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#commercial-data"}},[e._v("#")]),e._v(" Commercial Data")]),e._v(" "),t("p",[e._v("openEO Platform provides direct access to commercial data. Currently, data must be purchased directly through Sentinel Hub (see Sentinel Hub documentation on purchasing commercial data "),t("a",{attrs:{href:"https://docs.sentinel-hub.com/api/latest/api/data-import/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v("), but we are working to support ordering commercial data directly from the platform in the future.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Experimental usage")]),e._v(" "),t("p",[e._v("The below described way of how to connect to commercial data is currently only supported by the pyhton client and experimental. As such the behavior might still change in the future.")])]),e._v(" "),t("p",[e._v("Data is accessed as part of the load_collection process and via a "),t("code",[e._v("featureflags")]),e._v(" argument. To access the data, you must:")]),e._v(" "),t("ul",[t("li",[e._v("select the commercial data provider in "),t("code",[e._v("collection_id")]),e._v(" (e.g. "),t("code",[e._v('collection_id="PLANETSCOPE"')]),e._v(")")]),e._v(" "),t("li",[e._v("set the Sentinel Hub BYOC collection ID ("),t("code",[e._v("byoc-{id}")]),e._v(") as "),t("code",[e._v("featureflags")]),e._v(" argument\n(e.g. with openEO Python client version 0.10.1 or higher:\n"),t("code",[e._v('datacube.result_node().update_arguments(featureflags={"byoc_collection_id": byoc_collection_id})')]),e._v(")")])]),e._v(" "),t("p",[e._v("Full example of loading a commercial data collection:")]),e._v(" "),t("div",{staticClass:"language-python extra-class"},[t("pre",{pre:!0,attrs:{class:"language-python"}},[t("code",[e._v("toc "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" connection"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("load_collection"),t("span",{pre:!0,attrs:{class:"token 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On top, pre-processed Level-2 data is\navailable on request (this may result in additional costs). In detail Gamma0 data processed with SNAP and optical ARD\nproducts processed with FORCE are provided.")]),e._v(" "),t("p",[e._v("If the available pre-processed collections are not sufficient, there is on option to perform ARD processing on demand\nwith SNAP respectively FORCE. This may again result in additional costs for processing and storage.")]),e._v(" "),t("p",[e._v("Currently most processes are only available for Level-2 data. Only the ARD processes can be executed on Level-1 data. Also\nonly either ARD processes "),t("strong",[e._v("or")]),e._v(' "standard" processes can be used in one process graph. Combining both types of processes\nis not yet supported. 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This "),t("code",[e._v("on-demand-preview")]),e._v(" is available in the "),t("a",{attrs:{href:"https://editor.openeo.cloud/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Platform Editor"),t("OutboundLink")],1),e._v(" and the "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/index.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Python Client*"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("In the Editor you can find the functionality in the "),t("code",[e._v("Web Services")]),e._v(" tab. Use the button"),t("code",[e._v("Show on Map")]),e._v(".")]),e._v(" "),t("p",[e._v("In the Python Client, you can use the "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.preview",target:"_blank",rel:"noopener noreferrer"}},[e._v(".preview() method"),t("OutboundLink")],1),e._v(" to create a service with your process graph and display the results in an ipyleaflet Map object. 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In order for the map to display meaningful results, your process graph should:")]),e._v(" "),t("ul",[t("li",[e._v("return one or three bands,")]),e._v(" "),t("li",[e._v("a single temporal extent,")]),e._v(" "),t("li",[e._v("and scale the data to a range so that the output format can adequately save it.")])])]),e._v(" "),t("p",[e._v("*You need to have at least version 0.19.0 installed to use this functionality.")]),e._v(" "),t("h2",{attrs:{id:"batch-jobs"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#batch-jobs"}},[e._v("#")]),e._v(" Batch jobs")]),e._v(" "),t("p",[e._v("As discussed above, when the federated platform receives a processing request, such as a batch job,\nit will automatically determine to which back-end this request should be delegated for actual processing.")]),e._v(" "),t("h3",{attrs:{id:"managed-job-splitting"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#managed-job-splitting"}},[e._v("#")]),e._v(" Managed job splitting")]),e._v(" "),t("p",[e._v("In addition to this basic delegation feature, the federation also provides more advanced (pre)processing capabilities.\nFor instance, the federation platform can be instructed to split up a single batch job in multiple sub-jobs\nand distribute these across one or more processing back-ends.\nThe federation platform will automatically create, start and keep track of these sub-jobs\nwhile the user just have to interact with a single job: e.g. check the overall processing status,\ndownload the combined result assets, ...")]),e._v(" "),t("p",[e._v("Currently, spatial tile-based splitting is supported as splitting strategy\nand it can be enabled by providing a specific job option when submitting the batch job.\nFor example, using the openEO Python Client, instruct the federation platform to\nsplit up datacube processing in UTM based tiles of 20km by 20km:")]),e._v(" "),t("div",{staticClass:"language-python extra-class"},[t("pre",{pre:!0,attrs:{class:"language-python"}},[t("code",[e._v("job "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" datacube"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("create_job"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("\n job_options"),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("{")]),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"tile_grid"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(":")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"utm-20km"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("}")]),e._v("\n"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),e._v("\n")])])]),t("p",[e._v('This creates a virtual master job on the level of the federation platform and real batch jobs\non the appropriate processing back-ends.\nSubsequent interaction (starting the jobs, polling their status, requesting the result assets, ...)\ncan be done through the "master" '),t("code",[e._v("job")]),e._v(" object created above, in the same way as with normal batch jobs.")]),e._v(" "),t("h3",{attrs:{id:"validity-of-signed-urls-in-batch-job-results"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#validity-of-signed-urls-in-batch-job-results"}},[e._v("#")]),e._v(" Validity of signed URLs in batch job results")]),e._v(" "),t("p",[e._v("Batch job results are accessible to the user via signed URLs stored in the result assets. Within the platform,\nthese URLs have a validity (expiry time) of 7 days. Within these 7 days, the results of a batch job can be accessed\nby any person with the URL. Each time a user requests the results from the job endpoint ("),t("code",[e._v("GET /jobs/{job_id}/results")]),e._v("),\na freshly signed URL (valid for 7 days) is created for the result assets.")]),e._v(" "),t("h3",{attrs:{id:"customizing-batch-job-resources-on-terrascope"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#customizing-batch-job-resources-on-terrascope"}},[e._v("#")]),e._v(" Customizing batch job resources on Terrascope")]),e._v(" "),t("p",[e._v("Jobs running on the (Terrascope) cluster get assigned a default amount of CPU and memory resources. This\nmay not always be enough for your job, for instance when using UDF's. Also for very large jobs, you may want\nto tune your resource settings to optimize for cost.")]),e._v(" "),t("p",[e._v("The example below shows how to start a job with all options set to their default values. 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The number of parallel tasks is executor-cores/task-cpus")]),e._v(" "),t("li",[e._v("task-cpus: CPUs assigned to a single task. UDF's using libraries like Tensorflow can benefit from further parallellization on the level of individual tasks.")]),e._v(" "),t("li",[e._v("executor-request-cores: this settings is only relevant for Kubernetes based backends, allows to overcommit CPU")]),e._v(" "),t("li",[e._v("max-executors: the maximum number of workers assigned to your job. Maximum number of parallel tasks is "),t("code",[e._v("max-executors*executor-cores/task-cpus")]),e._v(". Increasing this can inflate your costs, while not necessarily improving performance!")]),e._v(" "),t("li",[e._v("driver-memory: memory assigned to the spark 'driver' JVM that controls execution of your batch job")]),e._v(" "),t("li",[e._v("driver-memoryOverhead: memory assigned to the spark 'driver' on top of JVM memory, for Python processes.")]),e._v(" "),t("li",[e._v("logging-threshold: the threshold for logging, set to 'info' by default, can be set to 'debug' to generate much more logging")]),e._v(" "),t("li",[e._v("udf-dependency-archives: an array of urls pointing to zip files with extra dependencies, see below")])]),e._v(" "),t("h4",{attrs:{id:"custom-udf-dependencies"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#custom-udf-dependencies"}},[e._v("#")]),e._v(" Custom UDF dependencies")]),e._v(" "),t("p",[e._v("User defined functions often depend on (specific versions of) libraries or require small auxiliary data files. The UDF specifications do not yet\ndefine a standardized manner to provide this other than having the ability of selecting from a predefined set of 'runtimes' that than again have a predefined configuration.")]),e._v(" "),t("p",[e._v("The Terrascope/Geotrellis backends solve this via the udf-dependency-archives job option, that allows to specify a list of zip files that should be included in the working directory of the UDF.")]),e._v(" "),t("p",[e._v("This enables the following example workflow for Python UDF's:")]),e._v(" "),t("ol",[t("li",[e._v("Create a Python 'virtualenv' with your dependencies")]),e._v(" "),t("li",[e._v("Based on the 'site-packages' directory of the virtualenv, create a zip file with all dependencies")]),e._v(" "),t("li",[e._v("Upload the zip to a url that can be reached by the backend.")]),e._v(" "),t("li",[e._v("In job options, add "),t("code",[e._v("\"udf-dependency-archives\": ['https://yourhost.com/myEnv.zip#tmp/mydir']")]),e._v(" The "),t("code",[e._v("#tmp/mydir")]),e._v(" suffix indicates where you want to unzip your files, relative to the working directory.")]),e._v(" "),t("li",[e._v("In your UDF, before trying to import libraries, add your directory to the Python path: "),t("code",[e._v("sys.path.insert(0, 'tmp/mydir')")])]),e._v(" "),t("li",[e._v("Now your libraries should be loaded before anything else!")])]),e._v(" "),t("p",[e._v("Known limitations:")]),e._v(" "),t("ul",[t("li",[e._v("Your dependencies need to be compatible with the Python version of the backend, currently 3.8.")]),e._v(" "),t("li",[e._v("Your dependencies need to be compatible with the OS of the backend, currently AlmaLinux 8.")]),e._v(" "),t("li",[e._v("The backend has a limited set of Python dependences that are preloaded, and cannot be changed, such as numpy.")])]),e._v(" "),t("h4",{attrs:{id:"learning-more"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#learning-more"}},[e._v("#")]),e._v(" Learning more")]),e._v(" "),t("p",[e._v("The topic of resource optimization is a complex one, and here we just give a short summary. The goal of openEO is to hide most of these\ndetails from the user, but we realize that advanced users sometimes want to have a bit more insight, so in the spirit of being open, we give some hints.")]),e._v(" "),t("p",[e._v("To learn more about these options, we point to the piece of code that handles this:")]),e._v(" "),t("p",[e._v("https://github.com/Open-EO/openeo-geopyspark-driver/blob/faf5d5364a82e870e42efd2a8aee9742f305da9f/openeogeotrellis/backend.py#L1213")]),e._v(" "),t("p",[e._v("Most memory related options are translated to Apache Spark configuration settings, which are documented here:")]),e._v(" "),t("p",[e._v("https://spark.apache.org/docs/3.3.1/configuration.html#application-properties")]),e._v(" "),t("h3",{attrs:{id:"batch-job-results-on-sentinel-hub"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#batch-job-results-on-sentinel-hub"}},[e._v("#")]),e._v(" Batch job results on Sentinel Hub")]),e._v(" "),t("p",[e._v("If you are processing data and the underlying back-end is Sentinel Hub, the output extent of your batch job results is currently larger than your input extent because Sentinel Hub processes whole tiles (this may change in the future and the data will be cropped to your input extent).")])],1)}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/52.4250f428.js b/assets/js/52.f71825e2.js similarity index 99% rename from assets/js/52.4250f428.js rename to assets/js/52.f71825e2.js index e5b50f7f4..efe1ecf9b 100644 --- a/assets/js/52.4250f428.js +++ b/assets/js/52.f71825e2.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[52],{320:function(t,a,s){t.exports=s.p+"assets/img/chart.722d5801.png"},507:function(t,a,s){"use strict";s.r(a);var n=s(4),e=Object(n.a)({},(function(){var t=this,a=t._self._c;return a("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[a("h1",{attrs:{id:"no2-monitoring"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#no2-monitoring"}},[t._v("#")]),t._v(" NO₂ monitoring")]),t._v(" "),a("p",[a("a",{attrs:{href:"https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p/instrumental-payload",target:"_blank",rel:"noopener noreferrer"}},[t._v("TROPOMI ("),a("strong",[t._v("TROPO")]),t._v("spheric "),a("strong",[t._v("M")]),t._v("onitoring "),a("strong",[t._v("I")]),t._v("nstrument)"),a("OutboundLink")],1),t._v(" is the name of a sensor on board of the "),a("a",{attrs:{href:"https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p",target:"_blank",rel:"noopener noreferrer"}},[t._v("Sentinel-5 Precursor (S5P) satellite"),a("OutboundLink")],1),t._v(", developed to monitor atmospheric chemistry.")]),t._v(" "),a("p",[t._v("This use case analyses Sentinel 5P imagery, focusing in particular on NO₂ measurements. 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Various packages are "),t("a",{attrs:{href:"https://openeo.cloud/#plans",target:"_blank",rel:"noopener noreferrer"}},[e._v("available"),t("OutboundLink")],1),e._v(",\nbut they all come with a number of 'platform credits' that you can consume in various ways.")]),e._v(" "),t("p",[e._v("Currently, credits can be deducted based on:")]),e._v(" "),t("ul",[t("li",[e._v("CPU usage (cores/second)")]),e._v(" "),t("li",[e._v("Memory usage (GB/second)")]),e._v(" "),t("li",[e._v("Storage (GB/day)")]),e._v(" "),t("li",[e._v("Data access to specific layers (e.g. Sentinel Hub or commercial)")]),e._v(" "),t("li",[e._v("Usage of services contributed by third parties, through an 'added value' cost (e.g. per hectare)")])]),e._v(" "),t("p",[e._v("For example, let's say we compute a Sentinel-2 based NDWI for an area of 10 hectares to a series of GeoTiffs (one per observation).")]),e._v(" "),t("p",[e._v("When running this example, the batch job reports the usage, which can be seen in the\n"),t("a",{attrs:{href:"https://editor.openeo.cloud",target:"_blank",rel:"noopener noreferrer"}},[e._v("Platform Editor"),t("OutboundLink")],1),e._v(' by clicking the "i" button for an individual batch job:')]),e._v(" "),t("figure",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/5937096/159861044-d758443f-9056-4474-909f-ebd8400de9dd.png",alt:"Usage Metrics shown in the Platform Editor"}}),e._v(" "),t("figcaption",[e._v("Usage Metrics shown in the Platform Editor for an example batch job.")])]),e._v(" "),t("p",[e._v("13,400,773 mb-seconds corresponds to 3.64 GB hours or 3.64 credits\n5099 CPU seconds corresponds to 1.4 CPU hour which translates to 2.12 credits")]),e._v(" "),t("p",[e._v("Summing this up, we arrive at 5.76 credits. In the current free tier, you receive 1000 credits, which amounts to quite a few of jobs like this!\nIt is important to note however that resource consumption (CPU and memory in this case) is not fixed over time because the\nperformance characteristics of a particular cloud tends to fluctuate depending on overall load.\nCloud providers do try to avoid this, but in general only manage to do so within the limits of a given SLA.")]),e._v(" "),t("h2",{attrs:{id:"platform-credit-rates"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#platform-credit-rates"}},[e._v("#")]),e._v(" Platform credit rates")]),e._v(" "),t("p",[e._v("The example above also shows that conversion rates need to be applied to convert resource usage into credits.\nAs openEO platform consists of different software components that are operated on different clouds, these conversion rates are not set to a fixed value\nfor the platform as a whole. For instance, a backend running on more expensive but faster CPU's, might charge more for a CPU hour compared to a provider\nrunning on slower but cheaper CPU's.")]),e._v(" "),t("p",[e._v("While this might seem to make the costs unpredictable, we recommend users to also evaluate their own resource usage.\nIf the cost for a particular job appears relatively high, it might be worthwhile to try running it against a different backend if possible.")]),e._v(" "),t("p",[e._v("The platform will also notify users of changes to the conversion rate, especially if a resource would become more expensive.\nIf you do notice an unexpected increase in credit usage, we recommend contacting the platform through the helpdesk, to ensure that this is expected.")]),e._v(" "),t("h2",{attrs:{id:"estimating-resource-usage"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#estimating-resource-usage"}},[e._v("#")]),e._v(" Estimating resource usage")]),e._v(" "),t("p",[e._v("Often you want to know up front what kind of costs you will incur by using the platform, especially when generating larger results\nor running the same job at fixed times. For the platform however, this is not trivial to do without actually running your job, because the resource\nconsumption heavily depends on the exact combination of processes that you are using.")]),e._v(" "),t("p",[e._v("Hence, the way to estimate job usage is to start small. For instance, for a query on 10m resolution, you may want to start with a 10ha area,\nand simply run that job. As shown above, this will usually incur a cost of only a few cents. In the worst case, you might discover that this already\ncosts a few euros, but then you would also notice that your job is taking multiple hours to run.")]),e._v(" "),t("p",[e._v("In any case, once you've established an initial cost for a small area, you can extrapolate to a larger area. If simple linear extrapolation shows that\na larger job is affordable, then run the job on larger areas, like 50ha or up to 100x100km. This will show you how your job scales,\nand what kind of costs you will be incurring! If at any point the cost appears unreasonable, please contact the platform!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[59],{482:function(e,t,o){"use strict";o.r(t);var a=o(4),r=Object(a.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"platform-credit-usage"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#platform-credit-usage"}},[e._v("#")]),e._v(" Platform credit usage")]),e._v(" "),t("p",[e._v("As a user of openEO platform, you require a subscription to get access. Various packages are "),t("a",{attrs:{href:"https://openeo.cloud/#plans",target:"_blank",rel:"noopener noreferrer"}},[e._v("available"),t("OutboundLink")],1),e._v(",\nbut they all come with a number of 'platform credits' that you can consume in various ways.")]),e._v(" "),t("p",[e._v("Currently, credits can be deducted based on:")]),e._v(" "),t("ul",[t("li",[e._v("CPU usage (cores/second)")]),e._v(" "),t("li",[e._v("Memory usage (GB/second)")]),e._v(" "),t("li",[e._v("Storage (GB/day)")]),e._v(" "),t("li",[e._v("Data access to specific layers (e.g. Sentinel Hub or commercial)")]),e._v(" "),t("li",[e._v("Usage of services contributed by third parties, through an 'added value' cost (e.g. per hectare)")])]),e._v(" "),t("p",[e._v("For example, let's say we compute a Sentinel-2 based NDWI for an area of 10 hectares to a series of GeoTiffs (one per observation).")]),e._v(" "),t("p",[e._v("When running this example, the batch job reports the usage, which can be seen in the\n"),t("a",{attrs:{href:"https://editor.openeo.cloud",target:"_blank",rel:"noopener noreferrer"}},[e._v("Platform Editor"),t("OutboundLink")],1),e._v(' by clicking the "i" button for an individual batch job:')]),e._v(" "),t("figure",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/5937096/159861044-d758443f-9056-4474-909f-ebd8400de9dd.png",alt:"Usage Metrics shown in the Platform Editor"}}),e._v(" "),t("figcaption",[e._v("Usage Metrics shown in the Platform Editor for an example batch job.")])]),e._v(" "),t("p",[e._v("13,400,773 mb-seconds corresponds to 3.64 GB hours or 3.64 credits\n5099 CPU seconds corresponds to 1.4 CPU hour which translates to 2.12 credits")]),e._v(" "),t("p",[e._v("Summing this up, we arrive at 5.76 credits. In the current free tier, you receive 1000 credits, which amounts to quite a few of jobs like this!\nIt is important to note however that resource consumption (CPU and memory in this case) is not fixed over time because the\nperformance characteristics of a particular cloud tends to fluctuate depending on overall load.\nCloud providers do try to avoid this, but in general only manage to do so within the limits of a given SLA.")]),e._v(" "),t("h2",{attrs:{id:"platform-credit-rates"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#platform-credit-rates"}},[e._v("#")]),e._v(" Platform credit rates")]),e._v(" "),t("p",[e._v("The example above also shows that conversion rates need to be applied to convert resource usage into credits.\nAs openEO platform consists of different software components that are operated on different clouds, these conversion rates are not set to a fixed value\nfor the platform as a whole. For instance, a backend running on more expensive but faster CPU's, might charge more for a CPU hour compared to a provider\nrunning on slower but cheaper CPU's.")]),e._v(" "),t("p",[e._v("While this might seem to make the costs unpredictable, we recommend users to also evaluate their own resource usage.\nIf the cost for a particular job appears relatively high, it might be worthwhile to try running it against a different backend if possible.")]),e._v(" "),t("p",[e._v("The platform will also notify users of changes to the conversion rate, especially if a resource would become more expensive.\nIf you do notice an unexpected increase in credit usage, we recommend contacting the platform through the helpdesk, to ensure that this is expected.")]),e._v(" "),t("h2",{attrs:{id:"estimating-resource-usage"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#estimating-resource-usage"}},[e._v("#")]),e._v(" Estimating resource usage")]),e._v(" "),t("p",[e._v("Often you want to know up front what kind of costs you will incur by using the platform, especially when generating larger results\nor running the same job at fixed times. For the platform however, this is not trivial to do without actually running your job, because the resource\nconsumption heavily depends on the exact combination of processes that you are using.")]),e._v(" "),t("p",[e._v("Hence, the way to estimate job usage is to start small. For instance, for a query on 10m resolution, you may want to start with a 10ha area,\nand simply run that job. As shown above, this will usually incur a cost of only a few cents. In the worst case, you might discover that this already\ncosts a few euros, but then you would also notice that your job is taking multiple hours to run.")]),e._v(" "),t("p",[e._v("In any case, once you've established an initial cost for a small area, you can extrapolate to a larger area. If simple linear extrapolation shows that\na larger job is affordable, then run the job on larger areas, like 50ha or up to 100x100km. This will show you how your job scales,\nand what kind of costs you will be incurring! If at any point the cost appears unreasonable, please contact the platform!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/61.3747a915.js b/assets/js/61.7cf6cd94.js similarity index 99% rename from assets/js/61.3747a915.js rename to assets/js/61.7cf6cd94.js index 2d7645b3b..bf6d68f16 100644 --- a/assets/js/61.3747a915.js +++ b/assets/js/61.7cf6cd94.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[61],{482:function(e,t,o){"use strict";o.r(t);var a=o(4),i=Object(a.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[e._v("#")]),e._v(" Collections")]),e._v(" "),t("p",[e._v("This page describes requirements on STAC collection metadata for backend providers in the openEO platform federation. These requirements should be considered an addition to what is already required by the "),t("a",{attrs:{href:"https://api.openeo.org/#tag/EO-Data-Discovery/operation/describe-collection",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO API"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"collection-availability"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#collection-availability"}},[e._v("#")]),e._v(" Collection availability")]),e._v(" "),t("p",[e._v("The collections are a key asset of the platform, and users need to know what they can expect from a certain collection.\nThe federation relies on openEO collection metadata following the STAC specification to communicate this towards the user. Backend providers\nneed to comply with these requirements for their collection:")]),e._v(" "),t("p",[e._v("The 'experimental' flag from https://stac-extensions.github.io/version/v1.1.0/schema.json needs to be set on experimental collections.\nA collection can be experimental either because there are issues with the actual data or catalog, or because of backend specific issues that make the use of the collection unstable.\nA collection marked as experimental does not need to comply with further requirements.")]),e._v(" "),t("h3",{attrs:{id:"requirements-for-non-experimental-collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#requirements-for-non-experimental-collections"}},[e._v("#")]),e._v(" Requirements for non-experimental collections")]),e._v(" "),t("p",[e._v("The main goal for non-experimental features is to achieve high user satisfaction by providing stability & usability "),t("em",[e._v("and")]),e._v(" to comply with federation agreements that make it possible to offer a unified service.\nThe list below makes this more concrete, but is not expected to cover all aspects. It is therefore important that providers, who know their implementations and datasets best, properly judge if a specific collection can be\nconsidered non-experimental.")]),e._v(" "),t("ol",[t("li",[t("p",[e._v("Collections need to indicate the key 'providers' that are responsible for ensuring access to the data and continuity in the case of active missions. The user may depend on the guarantees offered by these\nproviders with respect to the properties (timeliness, completeness,...) of a specific collection.\nThe providers with role 'host' and 'producer' are mandatory.")])]),e._v(" "),t("li",[t("p",[e._v("The collection description and extents needs to specify known limitations with respect to the original collection. For instance, if only a subset of the full archive is available, this should be indicated. Extents can be rough approximations to avoid requiring very detailed geometry in the metadata.")])]),e._v(" "),t("li",[t("p",[e._v("Collections without an end time are assumed to be active missions. By default, 99% of items in these collections should be available within 48 hours after being published by the producer. This gives users a basic guarantee with respect to timeliness of products.")])]),e._v(" "),t("li",[t("p",[e._v("Collection metadata should be valid STAC metadata and must include all extensions in "),t("code",[e._v("stac_extensions")]),e._v(". Tools such as "),t("a",{attrs:{href:"https://github.com/stac-utils/stac-validator",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC-validator"),t("OutboundLink")],1),e._v(" can indicate obvious issues.")])]),e._v(" "),t("li",[t("p",[e._v("FAIR principle R1: "),t("a",{attrs:{href:"https://www.go-fair.org/fair-principles/r1-metadata-richly-described-plurality-accurate-relevant-attributes/",target:"_blank",rel:"noopener noreferrer"}},[e._v("(Meta)data are richly described with a plurality of accurate and relevant attributes"),t("OutboundLink")],1)])]),e._v(" "),t("li",[t("p",[e._v("Collections have to follow harmonization guidelines specified below, if applicable.")])]),e._v(" "),t("li",[t("p",[e._v("Collections naming (id, dimensions, bands) should remain constant.")])]),e._v(" "),t("li",[t("p",[e._v("Backwards incompatible changes or removal need to be announced with a lead time of 6 months, together with a migration path.")])]),e._v(" "),t("li",[t("p",[e._v("Minimum availability of non-experimental collections is 98% on a monthly basis. The backend availability is used here if availability is not measured per collection.")])]),e._v(" "),t("li",[t("p",[e._v("Availability of a collection means that a simple process graph (e.g. using load_collection) returns a correct result. Collections may have special conditions to work, for instance in the case of commercial data.")])])]),e._v(" "),t("h2",{attrs:{id:"harmonization"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#harmonization"}},[e._v("#")]),e._v(" Harmonization")]),e._v(" "),t("p",[e._v("When back-ends offer/mirror the same datasets, it is required to align names and metadata. For the following collections and metadata an agreement has been achieved. These are all Copernicus Missions, and the standard names refer to the archives prepared and distributed by ESA. If it is not possible/desirable to use this name as collection id, a 'common_name' can be added next to the 'id' property to identify the collection as a standard archive.")]),e._v(" "),t("ul",[t("li",[e._v("SENTINEL1_GRD")]),e._v(" "),t("li",[e._v("SENTINEL2_L1C")]),e._v(" "),t("li",[t("a",{attrs:{href:"#sentinel2-l2a"}},[e._v("SENTINEL2_L2A")])]),e._v(" "),t("li",[e._v("SENTINEL3_OLCI_L1B")])]),e._v(" "),t("h3",{attrs:{id:"common-naming-convention"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#common-naming-convention"}},[e._v("#")]),e._v(" Common naming convention")]),e._v(" "),t("p",[e._v("In order to achieve a uniform structure for all collections on the platform and thus make it easier for users to navigate between collections, it is recommended to follow the common naming convention*:")]),e._v(" "),t("ul",[t("li",[e._v('Names should be written in capital letters ("all caps")')]),e._v(" "),t("li",[e._v("Names should consist of a combination of different optional attributes (see table)")]),e._v(" "),t("li",[e._v("The different attributes should be separated by an underscore")])]),e._v(" "),t("p",[e._v("Very roughly speaking, collections can be divided into two groups (in reality it is more of a spectrum with all gradations in between):")]),e._v(" "),t("ul",[t("li",[e._v("collections containing raw data (or processing levels of that data) measured directly by a satellite (or an other measurement platform) and often distributed by the platform operator (e.g., ESA)")]),e._v(" "),t("li",[e._v("derived collections, which are based on (pre-processed) raw data that has been processed to create a collection with a specific purpose (e.g., a land cover map) and are often distributed by the institution (or a service of an institution) that created the collection")])]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("Attribute")]),e._v(" "),t("th",[e._v("Type")]),e._v(" "),t("th",[e._v("Description")]),e._v(" "),t("th",[e._v("Examples")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[e._v("Provider")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Often used for derived collections produced or order by the listed provider.")]),e._v(" "),t("td",[t("code",[e._v("ESA")]),e._v(", "),t("code",[e._v("CNSE")]),e._v(", "),t("code",[e._v("EMODNET")]),e._v(", "),t("code",[e._v("TERRASCOPE")]),e._v(". "),t("code",[e._v("CAMS")]),e._v(", "),t("code",[e._v("CGLS")])])]),e._v(" "),t("tr",[t("td",[e._v("Satellite/Platform")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Name of the satellite/platform that acquired the data in the collection.")]),e._v(" "),t("td",[t("code",[e._v("SENTINEL2")]),e._v(", "),t("code",[e._v("LANDSAT8")]),e._v(", "),t("code",[e._v("PALSAR2")])])]),e._v(" "),t("tr",[t("td",[e._v("Processing level")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Name of the level to which the data was processed (often processed raw data).")]),e._v(" "),t("td",[t("code",[e._v("L2A")]),e._v(", "),t("code",[e._v("L3")]),e._v(", "),t("code",[e._v("L2_1")])])]),e._v(" "),t("tr",[t("td",[e._v("Version")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Often used for derived collections that are produced in several versions.")]),e._v(" "),t("td",[t("code",[e._v("V1")]),e._v(", "),t("code",[e._v("V2")])])]),e._v(" "),t("tr",[t("td",[e._v("Resolution")]),e._v(" "),t("td",[e._v("string ("),t("code",[e._v("number + unit")]),e._v(" or "),t("code",[e._v("string")]),e._v(")")]),e._v(" "),t("td",[e._v("Usually added, if the resolution is of particular importance for the collection (e.g., novel product with this resolution) for the collection.")]),e._v(" "),t("td",[t("code",[e._v("10M")]),e._v(", "),t("code",[e._v("120M")]),e._v(", "),t("code",[e._v("EUROPE")]),e._v(", "),t("code",[e._v("GLOBAL")])])]),e._v(" "),t("tr",[t("td",[e._v("Product Description")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Human readable description of the data within the collection. Can also be an abbreviation or acronym.")]),e._v(" "),t("td",[t("code",[e._v("LAND_COVER_MAP")]),e._v(", "),t("code",[e._v("WORLDCOVER")]),e._v(", "),t("code",[e._v("NDVI")]),e._v(", "),t("code",[e._v("LAI")])])]),e._v(" "),t("tr",[t("td",[e._v("Year")]),e._v(" "),t("td",[e._v("number")]),e._v(" "),t("td",[e._v("Often used for derived products that where updated in the specified year or created based on data of the specified year.")]),e._v(" "),t("td",[t("code",[e._v("2022")])])])])]),e._v(" "),t("p",[e._v("Collections containing raw data or processing levels of that data often use a combination of satellite/platform and processing level (e.g., "),t("code",[e._v("SENTINEL2_L1C")]),e._v(" ). Derived collections often use a combination of provider and product description (e.g., CNES_LAND_COVER_MAP).")]),e._v(" "),t("p",[e._v("*Some existing collections may not strictly follow this naming convention as they were added to the platform prior to this agreement.")]),e._v(" "),t("h3",{attrs:{id:"sentinel2-l2a"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#sentinel2-l2a"}},[e._v("#")]),e._v(" Sentinel2-L2A")]),e._v(" "),t("p",[e._v("The common name for this collection is 'SENTINEL2_L2A'. It refers to the L2A products generated by the Sen2Cor software, which can be configured to be compatible with the ESA generated products. Note that the products in the ESA archive were also processed with different versions of Sen2Cor, so it is not possible to specify a very specific version or configuration of the processing chain.")]),e._v(" "),t("h4",{attrs:{id:"bands"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#bands"}},[e._v("#")]),e._v(" Bands")]),e._v(" "),t("p",[e._v("Band names for spectral bands follow the Bxx naming convention used by ESA. For example: B01, B02, B03, B08, B8A, B12")]),e._v(" "),t("ul",[t("li",[t("code",[e._v("SCL")]),e._v(" = the Sen2Cor scene classification band")]),e._v(" "),t("li",[t("code",[e._v("approximateViewAzimuth")]),e._v(" = collective term for the mean and accurate viewing azimuth angle. Depending on which backend is processing the data, the mean angle (for Sentinel Hub) or the accurate angle (for Terrascope) is used. If the accurate angle ("),t("code",[e._v("viewAzimuthAngles")]),e._v(") or the mean angle ("),t("code",[e._v("viewAzimuthMean")]),e._v(") is explicitly specified, the data is processed on the backend that holds the specified band.")]),e._v(" "),t("li",[t("code",[e._v("viewZenithMean")]),e._v(" = collective term for the mean and accurate viewing zenith angle. Depending on which backend is processing the data, the mean angle (for Sentinel Hub) or the accurate angle (for Terrascope) is used. If the accurate angle ("),t("code",[e._v("viewZenithMean")]),e._v(") or the mean angle ("),t("code",[e._v("viewZenithAngles")]),e._v(") is explicitly specified, the data is processed on the backend that holds those bands.")]),e._v(" "),t("li",[t("code",[e._v("sunAzimuthAngles")]),e._v("/"),t("code",[e._v("sunZenithAngles")]),e._v(" = collective term for the exact sun azimuth and sun zenith angle.")])]),e._v(" "),t("h3",{attrs:{id:"common-properties"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#common-properties"}},[e._v("#")]),e._v(" Common Properties")]),e._v(" "),t("p",[e._v("We list here a set of common properties, that can be relevant for multiple collections. Collections are strongly encouraged to use these properties instead of using a different name for the same property.")]),e._v(" "),t("h4",{attrs:{id:"common"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#common"}},[e._v("#")]),e._v(" Common")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/stac-extensions/sat#satorbit_state",target:"_blank",rel:"noopener noreferrer"}},[e._v("sat:orbit_state"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/stac-extensions/sat#satrelative_orbit",target:"_blank",rel:"noopener noreferrer"}},[e._v("sat:relative_orbit"),t("OutboundLink")],1)])]),e._v(" "),t("h4",{attrs:{id:"optical-instruments"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#optical-instruments"}},[e._v("#")]),e._v(" Optical instruments")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/stac-extensions/eo#eocloud_cover",target:"_blank",rel:"noopener noreferrer"}},[e._v("eo:cloud_cover"),t("OutboundLink")],1)])]),e._v(" "),t("h4",{attrs:{id:"sar-instruments"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#sar-instruments"}},[e._v("#")]),e._v(" SAR instruments")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/stac-extensions/sar#item-properties-or-asset-fields",target:"_blank",rel:"noopener noreferrer"}},[e._v("sar:instrument_mode"),t("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[61],{485:function(e,t,o){"use strict";o.r(t);var a=o(4),i=Object(a.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[e._v("#")]),e._v(" Collections")]),e._v(" "),t("p",[e._v("This page describes requirements on STAC collection metadata for backend providers in the openEO platform federation. These requirements should be considered an addition to what is already required by the "),t("a",{attrs:{href:"https://api.openeo.org/#tag/EO-Data-Discovery/operation/describe-collection",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO API"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"collection-availability"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#collection-availability"}},[e._v("#")]),e._v(" Collection availability")]),e._v(" "),t("p",[e._v("The collections are a key asset of the platform, and users need to know what they can expect from a certain collection.\nThe federation relies on openEO collection metadata following the STAC specification to communicate this towards the user. Backend providers\nneed to comply with these requirements for their collection:")]),e._v(" "),t("p",[e._v("The 'experimental' flag from https://stac-extensions.github.io/version/v1.1.0/schema.json needs to be set on experimental collections.\nA collection can be experimental either because there are issues with the actual data or catalog, or because of backend specific issues that make the use of the collection unstable.\nA collection marked as experimental does not need to comply with further requirements.")]),e._v(" "),t("h3",{attrs:{id:"requirements-for-non-experimental-collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#requirements-for-non-experimental-collections"}},[e._v("#")]),e._v(" Requirements for non-experimental collections")]),e._v(" "),t("p",[e._v("The main goal for non-experimental features is to achieve high user satisfaction by providing stability & usability "),t("em",[e._v("and")]),e._v(" to comply with federation agreements that make it possible to offer a unified service.\nThe list below makes this more concrete, but is not expected to cover all aspects. It is therefore important that providers, who know their implementations and datasets best, properly judge if a specific collection can be\nconsidered non-experimental.")]),e._v(" "),t("ol",[t("li",[t("p",[e._v("Collections need to indicate the key 'providers' that are responsible for ensuring access to the data and continuity in the case of active missions. The user may depend on the guarantees offered by these\nproviders with respect to the properties (timeliness, completeness,...) of a specific collection.\nThe providers with role 'host' and 'producer' are mandatory.")])]),e._v(" "),t("li",[t("p",[e._v("The collection description and extents needs to specify known limitations with respect to the original collection. For instance, if only a subset of the full archive is available, this should be indicated. Extents can be rough approximations to avoid requiring very detailed geometry in the metadata.")])]),e._v(" "),t("li",[t("p",[e._v("Collections without an end time are assumed to be active missions. By default, 99% of items in these collections should be available within 48 hours after being published by the producer. This gives users a basic guarantee with respect to timeliness of products.")])]),e._v(" "),t("li",[t("p",[e._v("Collection metadata should be valid STAC metadata and must include all extensions in "),t("code",[e._v("stac_extensions")]),e._v(". Tools such as "),t("a",{attrs:{href:"https://github.com/stac-utils/stac-validator",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC-validator"),t("OutboundLink")],1),e._v(" can indicate obvious issues.")])]),e._v(" "),t("li",[t("p",[e._v("FAIR principle R1: "),t("a",{attrs:{href:"https://www.go-fair.org/fair-principles/r1-metadata-richly-described-plurality-accurate-relevant-attributes/",target:"_blank",rel:"noopener noreferrer"}},[e._v("(Meta)data are richly described with a plurality of accurate and relevant attributes"),t("OutboundLink")],1)])]),e._v(" "),t("li",[t("p",[e._v("Collections have to follow harmonization guidelines specified below, if applicable.")])]),e._v(" "),t("li",[t("p",[e._v("Collections naming (id, dimensions, bands) should remain constant.")])]),e._v(" "),t("li",[t("p",[e._v("Backwards incompatible changes or removal need to be announced with a lead time of 6 months, together with a migration path.")])]),e._v(" "),t("li",[t("p",[e._v("Minimum availability of non-experimental collections is 98% on a monthly basis. The backend availability is used here if availability is not measured per collection.")])]),e._v(" "),t("li",[t("p",[e._v("Availability of a collection means that a simple process graph (e.g. using load_collection) returns a correct result. Collections may have special conditions to work, for instance in the case of commercial data.")])])]),e._v(" "),t("h2",{attrs:{id:"harmonization"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#harmonization"}},[e._v("#")]),e._v(" Harmonization")]),e._v(" "),t("p",[e._v("When back-ends offer/mirror the same datasets, it is required to align names and metadata. For the following collections and metadata an agreement has been achieved. These are all Copernicus Missions, and the standard names refer to the archives prepared and distributed by ESA. If it is not possible/desirable to use this name as collection id, a 'common_name' can be added next to the 'id' property to identify the collection as a standard archive.")]),e._v(" "),t("ul",[t("li",[e._v("SENTINEL1_GRD")]),e._v(" "),t("li",[e._v("SENTINEL2_L1C")]),e._v(" "),t("li",[t("a",{attrs:{href:"#sentinel2-l2a"}},[e._v("SENTINEL2_L2A")])]),e._v(" "),t("li",[e._v("SENTINEL3_OLCI_L1B")])]),e._v(" "),t("h3",{attrs:{id:"common-naming-convention"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#common-naming-convention"}},[e._v("#")]),e._v(" Common naming convention")]),e._v(" "),t("p",[e._v("In order to achieve a uniform structure for all collections on the platform and thus make it easier for users to navigate between collections, it is recommended to follow the common naming convention*:")]),e._v(" "),t("ul",[t("li",[e._v('Names should be written in capital letters ("all caps")')]),e._v(" "),t("li",[e._v("Names should consist of a combination of different optional attributes (see table)")]),e._v(" "),t("li",[e._v("The different attributes should be separated by an underscore")])]),e._v(" "),t("p",[e._v("Very roughly speaking, collections can be divided into two groups (in reality it is more of a spectrum with all gradations in between):")]),e._v(" "),t("ul",[t("li",[e._v("collections containing raw data (or processing levels of that data) measured directly by a satellite (or an other measurement platform) and often distributed by the platform operator (e.g., ESA)")]),e._v(" "),t("li",[e._v("derived collections, which are based on (pre-processed) raw data that has been processed to create a collection with a specific purpose (e.g., a land cover map) and are often distributed by the institution (or a service of an institution) that created the collection")])]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("Attribute")]),e._v(" "),t("th",[e._v("Type")]),e._v(" "),t("th",[e._v("Description")]),e._v(" "),t("th",[e._v("Examples")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[e._v("Provider")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Often used for derived collections produced or order by the listed provider.")]),e._v(" "),t("td",[t("code",[e._v("ESA")]),e._v(", "),t("code",[e._v("CNSE")]),e._v(", "),t("code",[e._v("EMODNET")]),e._v(", "),t("code",[e._v("TERRASCOPE")]),e._v(". "),t("code",[e._v("CAMS")]),e._v(", "),t("code",[e._v("CGLS")])])]),e._v(" "),t("tr",[t("td",[e._v("Satellite/Platform")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Name of the satellite/platform that acquired the data in the collection.")]),e._v(" "),t("td",[t("code",[e._v("SENTINEL2")]),e._v(", "),t("code",[e._v("LANDSAT8")]),e._v(", "),t("code",[e._v("PALSAR2")])])]),e._v(" "),t("tr",[t("td",[e._v("Processing level")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Name of the level to which the data was processed (often processed raw data).")]),e._v(" "),t("td",[t("code",[e._v("L2A")]),e._v(", "),t("code",[e._v("L3")]),e._v(", "),t("code",[e._v("L2_1")])])]),e._v(" "),t("tr",[t("td",[e._v("Version")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Often used for derived collections that are produced in several versions.")]),e._v(" "),t("td",[t("code",[e._v("V1")]),e._v(", "),t("code",[e._v("V2")])])]),e._v(" "),t("tr",[t("td",[e._v("Resolution")]),e._v(" "),t("td",[e._v("string ("),t("code",[e._v("number + unit")]),e._v(" or "),t("code",[e._v("string")]),e._v(")")]),e._v(" "),t("td",[e._v("Usually added, if the resolution is of particular importance for the collection (e.g., novel product with this resolution) for the collection.")]),e._v(" "),t("td",[t("code",[e._v("10M")]),e._v(", "),t("code",[e._v("120M")]),e._v(", "),t("code",[e._v("EUROPE")]),e._v(", "),t("code",[e._v("GLOBAL")])])]),e._v(" "),t("tr",[t("td",[e._v("Product Description")]),e._v(" "),t("td",[e._v("string")]),e._v(" "),t("td",[e._v("Human readable description of the data within the collection. Can also be an abbreviation or acronym.")]),e._v(" "),t("td",[t("code",[e._v("LAND_COVER_MAP")]),e._v(", "),t("code",[e._v("WORLDCOVER")]),e._v(", "),t("code",[e._v("NDVI")]),e._v(", "),t("code",[e._v("LAI")])])]),e._v(" "),t("tr",[t("td",[e._v("Year")]),e._v(" "),t("td",[e._v("number")]),e._v(" "),t("td",[e._v("Often used for derived products that where updated in the specified year or created based on data of the specified year.")]),e._v(" "),t("td",[t("code",[e._v("2022")])])])])]),e._v(" "),t("p",[e._v("Collections containing raw data or processing levels of that data often use a combination of satellite/platform and processing level (e.g., "),t("code",[e._v("SENTINEL2_L1C")]),e._v(" ). Derived collections often use a combination of provider and product description (e.g., CNES_LAND_COVER_MAP).")]),e._v(" "),t("p",[e._v("*Some existing collections may not strictly follow this naming convention as they were added to the platform prior to this agreement.")]),e._v(" "),t("h3",{attrs:{id:"sentinel2-l2a"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#sentinel2-l2a"}},[e._v("#")]),e._v(" Sentinel2-L2A")]),e._v(" "),t("p",[e._v("The common name for this collection is 'SENTINEL2_L2A'. It refers to the L2A products generated by the Sen2Cor software, which can be configured to be compatible with the ESA generated products. Note that the products in the ESA archive were also processed with different versions of Sen2Cor, so it is not possible to specify a very specific version or configuration of the processing chain.")]),e._v(" "),t("h4",{attrs:{id:"bands"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#bands"}},[e._v("#")]),e._v(" Bands")]),e._v(" "),t("p",[e._v("Band names for spectral bands follow the Bxx naming convention used by ESA. For example: B01, B02, B03, B08, B8A, B12")]),e._v(" "),t("ul",[t("li",[t("code",[e._v("SCL")]),e._v(" = the Sen2Cor scene classification band")]),e._v(" "),t("li",[t("code",[e._v("approximateViewAzimuth")]),e._v(" = collective term for the mean and accurate viewing azimuth angle. Depending on which backend is processing the data, the mean angle (for Sentinel Hub) or the accurate angle (for Terrascope) is used. If the accurate angle ("),t("code",[e._v("viewAzimuthAngles")]),e._v(") or the mean angle ("),t("code",[e._v("viewAzimuthMean")]),e._v(") is explicitly specified, the data is processed on the backend that holds the specified band.")]),e._v(" "),t("li",[t("code",[e._v("viewZenithMean")]),e._v(" = collective term for the mean and accurate viewing zenith angle. Depending on which backend is processing the data, the mean angle (for Sentinel Hub) or the accurate angle (for Terrascope) is used. If the accurate angle ("),t("code",[e._v("viewZenithMean")]),e._v(") or the mean angle ("),t("code",[e._v("viewZenithAngles")]),e._v(") is explicitly specified, the data is processed on the backend that holds those bands.")]),e._v(" "),t("li",[t("code",[e._v("sunAzimuthAngles")]),e._v("/"),t("code",[e._v("sunZenithAngles")]),e._v(" = collective term for the exact sun azimuth and sun zenith angle.")])]),e._v(" "),t("h3",{attrs:{id:"common-properties"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#common-properties"}},[e._v("#")]),e._v(" Common Properties")]),e._v(" "),t("p",[e._v("We list here a set of common properties, that can be relevant for multiple collections. Collections are strongly encouraged to use these properties instead of using a different name for the same property.")]),e._v(" "),t("h4",{attrs:{id:"common"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#common"}},[e._v("#")]),e._v(" Common")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/stac-extensions/sat#satorbit_state",target:"_blank",rel:"noopener noreferrer"}},[e._v("sat:orbit_state"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/stac-extensions/sat#satrelative_orbit",target:"_blank",rel:"noopener noreferrer"}},[e._v("sat:relative_orbit"),t("OutboundLink")],1)])]),e._v(" "),t("h4",{attrs:{id:"optical-instruments"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#optical-instruments"}},[e._v("#")]),e._v(" Optical instruments")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/stac-extensions/eo#eocloud_cover",target:"_blank",rel:"noopener noreferrer"}},[e._v("eo:cloud_cover"),t("OutboundLink")],1)])]),e._v(" "),t("h4",{attrs:{id:"sar-instruments"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#sar-instruments"}},[e._v("#")]),e._v(" SAR instruments")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/stac-extensions/sar#item-properties-or-asset-fields",target:"_blank",rel:"noopener noreferrer"}},[e._v("sar:instrument_mode"),t("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file diff --git a/assets/js/63.4bcb504f.js b/assets/js/63.6e1a0849.js similarity index 98% rename from assets/js/63.4bcb504f.js rename to assets/js/63.6e1a0849.js index b2aa01903..bde0011c3 100644 --- a/assets/js/63.4bcb504f.js +++ b/assets/js/63.6e1a0849.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[63],{486:function(e,t,n){"use strict";n.r(t);var o=n(4),r=Object(o.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"federation-contract"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#federation-contract"}},[e._v("#")]),e._v(" Federation Contract")]),e._v(" "),t("p",[e._v("For a federation to work, all providers need to come up with a federation contract to agree on.\nSee the "),t("RouterLink",{attrs:{to:"/federation/"}},[e._v("overview about the existing federation concepts and issues")]),e._v(".")],1),e._v(" "),t("p",[e._v("This contract has 2 main goals:")]),e._v(" "),t("ol",[t("li",[e._v("Achieve user satisfaction, which can be measured in terms of user growth and number of complaints versus the usage of a specific feature.")]),e._v(" "),t("li",[e._v("Agree on interfaces and harmonization rules to align the different services in the federation.")])]),e._v(" "),t("p",[e._v("The platform aims to be inclusive towards onboarding new features and components,\nto stimulate growth and innovation. 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However, when there is doubt about indicating a feature as experimental:")]),e._v(" "),t("ul",[t("li",[e._v("By default or if unsure, it is probably experimental.")]),e._v(" "),t("li",[e._v("If a feature is new or it hasn't been used, it's experimental.")]),e._v(" "),t("li",[e._v("If still in doubt, consult with partner providers.")])]),e._v(" "),t("p",[e._v("If a non-experimental component exceeds the "),t("a",{attrs:{href:"https://sre.google/workbook/implementing-slos/",target:"_blank",rel:"noopener noreferrer"}},[e._v("error budget"),t("OutboundLink")],1),e._v(", for instance when downtime exceeds the objective,\nthe provider is expected to stop working on new features and improve reliability, or to mark the component\nas experimental. 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"),e("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect (OIDC)"),e("OutboundLink")],1),t._v(".\nIt offers some interesting features (e.g., a user can securely reuse an existing account),\nbut is a fairly complex topic, discussed in more depth on the "),e("RouterLink",{attrs:{to:"/join/free_trial.html"}},[t._v("Free Trial")]),t._v(" page.")],1),t._v(" "),e("p",[t._v("The openEO Python client library tries to make authentication as streamlined as possible.\nIn most cases for example, the following snippet is enough to obtain an authenticated connection:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" openeo\n\nconnection "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" openeo"),e("span",{pre:!0,attrs:{class:"token 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canopy ...', 'stac_version': '0.9.0', ...\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("The openEO Python client library comes with Jupyter (notebook) integration in a couple of places.\nFor example, put "),e("code",[t._v('connection.describe_collection("SENTINEL2_L2A")')]),t._v(" (without "),e("code",[t._v("print()")]),t._v(")\nas last statement in a notebook cell\nand you'll get a nice graphical rendering of the collection metadata.")])]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("Find out more about data discovery, loading and filtering in the\n"),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/data_access.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("official openEO Python client documentation"),e("OutboundLink")],1)])]),t._v(" "),e("h3",{attrs:{id:"processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),e("p",[t._v('Processes in openEO are operations that can be applied on (EO) data\n(e.g. calculate the mean of an array, or mask out observations outside a given polygon).\nThe output of one process can be used as the input of another process,\nand by doing so, multiple processes can be connected that way in a larger "process graph"\nthat implements a certain algorithm.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Check "),e("a",{attrs:{href:"https://openeo.org/documentation/1.0/glossary.html#processes",target:"_blank",rel:"noopener noreferrer"}},[t._v("the openEO glossary"),e("OutboundLink")],1),t._v("\nfor more details on pre-defined, user-defined processes and process graphs.")])]),t._v(" "),e("p",[t._v("Let's list the available pre-defined processes\nwith "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.list_processes",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("list_processes")]),e("OutboundLink")],1),t._v(":")]),t._v(" "),e("div",{staticClass:"language- extra-class"},[e("pre",{pre:!0,attrs:{class:"language-text"}},[e("code",[t._v(">>> print(connection.list_processes())\n[{'id': 'absolute', 'summary': 'Absolute value', 'description': 'Computes the absolute value of ... \n {'id': 'mean', 'summary': 'Arithmetic mean(average)', ...\n ...\n")])])]),e("p",[t._v("Like with collections, instead of programmatic exploration you'll probably prefer a\nmore graphical, interactive interface.\nUse the Jupyter notebook integration (put "),e("code",[t._v("connection.list_processes()")]),t._v(" without "),e("code",[t._v("print()")]),t._v(" as last statement in a notebook cell)\nor visit a "),e("RouterLink",{attrs:{to:"/processes/"}},[t._v("web-based 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"),e("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect (OIDC)"),e("OutboundLink")],1),t._v(".\nIt offers some interesting features (e.g., a user can securely reuse an existing account),\nbut is a fairly complex topic, discussed in more depth on the "),e("RouterLink",{attrs:{to:"/join/free_trial.html"}},[t._v("Free Trial")]),t._v(" page.")],1),t._v(" "),e("p",[t._v("The openEO Python client library tries to make authentication as streamlined as possible.\nIn most cases for example, the following snippet is enough to obtain an authenticated connection:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" openeo\n\nconnection "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" openeo"),e("span",{pre:!0,attrs:{class:"token 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here"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h3",{attrs:{id:"creating-a-datacube"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#creating-a-datacube"}},[t._v("#")]),t._v(" Creating a Datacube")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Please note that the following code only creates a process graph representation of your EO analysis and does not take major processing power from you device.")])]),t._v(" "),e("p",[t._v("The first step is loading the desired slice of a data collection\nwith "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.load_collection",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("Connection.load_collection")]),e("OutboundLink")],1),t._v(":")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[t._v("datacube 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object"),e("OutboundLink")],1),t._v('\ncontaining the "SENTINEL1_GRD" data restricted to the given spatial extent, temporal extent and bands.')]),t._v(" "),e("h3",{attrs:{id:"applying-processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#applying-processes"}},[t._v("#")]),t._v(" Applying processes")]),t._v(" "),e("p",[t._v("By applying an openEO process on a datacube, we create a new datacube object that represents the manipulated data.\nThe openEO Python client allows to do this by calling "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("DataCube")]),t._v(" object methods"),e("OutboundLink")],1),t._v(".\nThe most common or popular openEO processes have a dedicated "),e("code",[t._v("DataCube")]),t._v(" method (e.g. "),e("code",[t._v("mask")]),t._v(", "),e("code",[t._v("aggregate_spatial")]),t._v(", "),e("code",[t._v("filter_bbox")]),t._v(", 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metadata objects are based on lists, so you can use "),e("code",[t._v("str()")]),t._v(" to get the structure of the list and address fields by the "),e("code",[t._v("$")]),t._v(" operator.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("If the package is used with RStudio the metadata can also be nicely rendered as a web page in the viewer panel by running "),e("code",[t._v('collection_viewer(x="SENTINEL1_GRD")')]),t._v(".")])]),t._v(" "),e("h3",{attrs:{id:"processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Please note that the any of this code only creates a process graph representation of your EO analysis and does not take major processing power from you device.")])]),t._v(" "),e("p",[t._v('Processes in openEO are operations that can be applied on (EO) data\n(e.g. calculate the mean of an array, or mask out observations outside a given polygon).\nThe output of one process can be used as the input of another process,\nand by doing so, multiple processes can be connected that way in a larger "process graph":\na new (user-defined) processes that implements a certain algorithm.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Check "),e("a",{attrs:{href:"https://openeo.org/documentation/1.0/glossary.html#processes",target:"_blank",rel:"noopener noreferrer"}},[t._v("the openEO glossary"),e("OutboundLink")],1),t._v("\nfor more details on pre-defined, user-defined processes and process graphs.")])]),t._v(" "),e("p",[t._v("The following code snippet shows how to get the available processes:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# List of available openEO processes with full metadata")]),t._v("\nprocesses "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# List of available openEO processes by identifiers (string)")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("names"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# print metadata of the process with ID "load_collection"')]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("processes"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("code",[t._v("list_processes()")]),t._v(" method returns a list of process metadata objects that the back-end provides.\nEach process list entry is a more complex list object (called 'ProcessInfo') and contains the process identifier and additional metadata about the process, such as expected arguments and return types.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("As for the collection, processes can also be rendered as a web page in the viewer panel, if RStudio is used. In order to open the viewer use "),e("code",[t._v("process_viewer()")]),t._v(" with either a particular process ("),e("code",[t._v('process_viewer("load_collection")')]),t._v(") or you can pass on all processes ("),e("code",[t._v("process_viewer(processes)")]),t._v("). When all processes are chosen, there is also a search bar and a category tree.")])]),t._v(" "),e("h2",{attrs:{id:"authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("p",[t._v("In the code snippets above we did not need to log in\nsince we just queried publicly available back-end information.\nHowever, to run non-trivial processing queries one has to authenticate\nso that permissions, resource usage, etc. can be managed properly.")]),t._v(" "),e("p",[t._v("To handle authentication, openEO leverages "),e("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect (OIDC)"),e("OutboundLink")],1),t._v(".\nIt offers some interesting features (e.g., a user can securely reuse an existing account),\nbut is a fairly complex topic, discussed in more depth on the "),e("RouterLink",{attrs:{to:"/join/free_trial.html"}},[t._v("Free Trial")]),t._v(" page.")],1),t._v(" "),e("p",[t._v("The following code snippet shows how to log in via OIDC authentication:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("login"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Calling this method opens your system web browser, with which you can authenticate yourself on the back-end authentication system.\nAfter that the website will give you the instructions to go back to the R client, where your connection has logged your account in.\nThis means, that every call that comes after that via the connection variable is executed by your user account.")]),t._v(" "),e("h2",{attrs:{id:"creating-a-user-defined-process"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#creating-a-user-defined-process"}},[t._v("#")]),t._v(" Creating a (user-defined) process")]),t._v(" "),e("p",[t._v("Now that we know how to discover the back-end and how to authenticate, lets continue by creating a new batch job to process some data.")]),t._v(" "),e("p",[t._v("First we need to create a process builder object that carries all the available predefined openEO processes of the connected back-end as attached R functions with the parameters stated in the process metadata.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("p "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The functions of the builder return process nodes, which represent a particular result in the workflow. As one of the first steps we need to select the source data collection.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("datacube "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n id "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"SENTINEL1_GRD"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n spatial_extent"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("list"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("west "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" south "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" east "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.65")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" north "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.35")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n temporal_extent"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("c"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"2017-03-01"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"2017-04-01"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n bands"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("c"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"VV"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"VH"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("This results in a process node that represents a "),e("a",{attrs:{href:"https://openeo.org/documentation/1.0/glossary.html#spatial-datacubes",target:"_blank",rel:"noopener noreferrer"}},[t._v("datacube"),e("OutboundLink")],1),t._v(' and contains the "SENTINEL1_GRD" data restricted to the given spatial extent, the given temporal extent and the given bands .')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Sample Data Retrieval")]),t._v(" "),e("p",[t._v("In order to get a better understanding about the processing mechanisms and the data structures used in openEO Platform, it helps to check the actual data from time to time.\nThe function "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/reference/get_sample.html",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("get_sample")]),e("OutboundLink")],1),t._v(" aids the user in downloading data for a very small spatial extent. It is automatically loaded into R so that you can directly inspect it with "),e("code",[t._v("stars")]),t._v('.\nRead the vignette on "'),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/articles/openeo-02-sample_data.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Sample Data Retrieval"),e("OutboundLink")],1),t._v('" for more details.')])]),t._v(" "),e("p",[t._v("Having the input data ready, we want to apply a process on the datacube.\nTherefore, we can call the process directly on the datacube object, which then returns a datacube with the process applied.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("min_reducer "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("function")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("context"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v(" \n return"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("min"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n\nreduced "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("reduce_dimension"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" reducer "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" min_reducer"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" dimension"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"t"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The datacube is now reduced by the time dimension named "),e("code",[t._v("t")]),t._v(', by taking the minimum value of the timeseries values. Now the datacube has no time dimension left. Other so called "reducer" processes exist, e.g. for computing maximum and mean values.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Everything applied to the datacube at this point is neither executed locally on your machine nor executed on the back-end.\nIt just defines the input data and process chain the back-end needs to apply when it sends the datacube to the back-end and executes it there.\nHow this can be done is the topic of the next chapter.")])]),t._v(" "),e("p",[t._v("After applying all processes you want to execute, we need to tell the back-end to export the datacube, for example as GeoTiff:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("formats "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_file_formats"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\nresult "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("save_result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" reduced"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" format "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" formats"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("output"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("GTIFF"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The first line retrieves the back-ends offered input and output formats. The second line creates the result node, which stores the data as a zipped GeoTiff.")]),t._v(" "),e("h2",{attrs:{id:"batch-job-management"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-job-management"}},[t._v("#")]),t._v(" Batch Job Management")]),t._v(" "),e("p",[t._v("After you have finished working on your (user-defined) process, we can now send it to the back-end and start the execution.\nIn openEO, an execution of a (user-defined) process is called a "),e("a",{attrs:{href:"https://openeo.org/documentation/1.0/glossary.html#data-processing-modes",target:"_blank",rel:"noopener noreferrer"}},[t._v("(batch) job"),e("OutboundLink")],1),t._v(".\nTherefore, we need to create a job at the back-end using our datacube, giving it the title "),e("code",[t._v("Example Title")]),t._v(".")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" create_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("graph"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" title "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Example Title"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("code",[t._v("create_job")]),t._v(" method sends all necessary information to the back-end and creates a new job, which gets returned.\nAfter this, the job is just created, but has not started the execution at the back-end yet.\nIt needs to be queued for processing explicitly:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("start_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("It can be annoying to manage and monitor batch jobs via code.\nIf you want to use an interface for your batch jobs (or other resources) that is easier to use, you can also open the "),e("a",{attrs:{href:"https://editor.openeo.cloud",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Platform Editor"),e("OutboundLink")],1),t._v('.\nAfter login, you\'ll be able to manage and monitor your batch jobs in a near-realtime interactive environment; Look out for the "Data Processing" tab.\nYou may shut down your device or log out during the job runs on the backend. You can retrieve the status and results later and from any client.')])]),t._v(" "),e("p",[t._v("After the job was executed, status updates can be fetched by using the "),e("code",[t._v("list_jobs()")]),t._v(" function. This function returns a list of job descriptions, which can be indexed with the jobs ID to limit the search results. But remember that only "),e("code",[t._v("list_jobs()")]),t._v(" refreshes this list. So, to monitor a job you have to iteratively call the job ("),e("code",[t._v("describe_job()")]),t._v(") or the job list "),e("code",[t._v("list_jobs()")]),t._v(".")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("jobs "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_jobs"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\njobs "),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# printed as a tibble or data.frame, but the object is a list")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or use the job id (in this example 'cZ2ND0Z5nhBFNQFq') as index to get a particular job overview")]),t._v("\njobs"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("cZ2ND0Z5nhBFNQFq\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# alternatively request detailed information about the job")]),t._v("\ndescribe_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("When the job is finished, calling "),e("code",[t._v("download_results()")]),t._v(" will download the results of a job. Using "),e("code",[t._v("list_results()")]),t._v(" will return an overview about the created files and their download link or it states the error message, in case of an error.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# list the processed results")]),t._v("\nlist_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# download all the files into a folder on the file system")]),t._v("\ndownload_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" folder "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"/some/folder/on/filesystem"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("The printing behavior and the actual data structure might differ!")])])])}),[],!1,null,null,null);e.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[72],{495:function(t,e,a){"use strict";a.r(e);var s=a(4),n=Object(s.a)({},(function(){var t=this,e=t._self._c;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h1",{attrs:{id:"get-started-with-the-openeo-r-client"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#get-started-with-the-openeo-r-client"}},[t._v("#")]),t._v(" Get started with the openEO R Client")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("You need to "),e("a",{attrs:{href:"https://openeo.cloud/#plans",target:"_blank",rel:"noopener noreferrer"}},[t._v("get an openEO Platform account"),e("OutboundLink")],1),t._v(" to access the processing infrastructure.")])]),t._v(" "),e("h2",{attrs:{id:"useful-links"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#useful-links"}},[t._v("#")]),t._v(" Useful links")]),t._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/index.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Documentation"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/articles/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Vignettes"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-client",target:"_blank",rel:"noopener noreferrer"}},[t._v("Code Repository"),e("OutboundLink")],1)]),t._v(" "),e("li",[t._v("for function documentation, use R's "),e("code",[t._v("?")]),t._v(" function or see the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/index.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("online documentation"),e("OutboundLink")],1)])]),t._v(" "),e("h2",{attrs:{id:"installation"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#installation"}},[t._v("#")]),t._v(" Installation")]),t._v(" "),e("p",[t._v("Before you install the R client module into your R environment, please make sure that you have at least R version 3.6. Older versions might also work, but were not tested.")]),t._v(" "),e("p",[t._v("Stable releases can be installed from CRAN:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("install.packages"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"openeo"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Installing the development version")]),t._v(" "),e("p",[t._v("If you want to install the development version, you can install from GitHub. It may contain more features, but may also be unstable.")]),t._v(" "),e("p",[t._v("You need to have the package 'devtools' installed. If it is not installed use "),e("code",[t._v('install.packages("devtools")')]),t._v(".")]),t._v(" "),e("p",[t._v("Now you can use "),e("code",[t._v("install_github")]),t._v(" from the devtools package to install the development version:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("devtools"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("::")]),t._v("install_github"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("repo"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Open-EO/openeo-r-client"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" dependencies"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token boolean"}},[t._v("TRUE")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" ref"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"develop"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("If this gives you an error, something went wrong with the installation so please check the requirements again.")])]),t._v(" "),e("p",[t._v("If you have still troubles installing the package, feel free to leave an issue at the "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-client/issues",target:"_blank",rel:"noopener noreferrer"}},[t._v("GitHub project"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("p",[t._v("Now that the installation was successfully finished, we can load the package and connect to openEO Platform.\nIn the following chapters we quickly walk through the main features of the R client.")]),t._v(" "),e("h2",{attrs:{id:"connect-to-openeo-platform-and-explore"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#connect-to-openeo-platform-and-explore"}},[t._v("#")]),t._v(" Connect to openEO Platform and explore")]),t._v(" "),e("p",[t._v("First we need to establish a connection to the openEO Platform back-end,\nwhich is available at connection URL "),e("code",[t._v("https://openeo.cloud")]),t._v(", or just in short:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("library"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("openeo"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\ncon "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" connect"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("host "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://openeo.cloud"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The object stored as variable "),e("code",[t._v("con")]),t._v(" is a connection and resembles the "),e("code",[t._v("OpenEOClient")]),t._v(" - an object, that bundles all information and functions to interact with the back-end. It can be used explicitly in all of the functions to determine which connection has to be used (usually parameter "),e("code",[t._v("con")]),t._v("). If only one connection is active, then you can omit the parameter, because the last active connection is always stored in a package environment and used if no specific connection was present (see "),e("code",[t._v("?active_connection")]),t._v(" in the package documentation).")]),t._v(" "),e("p",[t._v("The capabilities of the back-end and the collections are generally publicly available, unless the data collections are proprietary and licensing issues prevent the back-end provider from publishing the collection. For the publicly available information you do not need to have an account on the back-end for reading them.")]),t._v(" "),e("h3",{attrs:{id:"collections"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),e("p",[t._v('The EO data available at a back-end is organised in so-called collections.\nFor example, a back-end might provide fundamental satellite collections like "Sentinel 1" or "Sentinel 2",\nor preprocessed collections like "NDVI".\nCollections are used as input data for your openEO jobs.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v('More information on how openEO "collections" relate to terminology used in other systems can be found in\n'),e("a",{attrs:{href:"https://openeo.org/documentation/1.0/glossary.html#eo-data-collections",target:"_blank",rel:"noopener noreferrer"}},[t._v("the openEO glossary"),e("OutboundLink")],1),t._v(".")])]),t._v(" "),e("p",[t._v("To get the collection list can be indexed by the collections ID to get the more details about the overview information. With the "),e("code",[t._v("describe_collection")]),t._v(" function you can get an even more detailed information about the collection.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("collections "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_collections"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# print an overview of the available collections (printed as data.frame or tibble)")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("collections"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# to print more of the reduced overview metadata")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("collections"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("SENTINEL1_GRD"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# Dictionary of the full metadata of the "SENTINEL1_GRD" collection (dict)')]),t._v("\ns2 "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" describe_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"SENTINEL1_GRD"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or use the collection entry from the list, e.g. collections$SENTINEL1_GRD")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("s2"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("In general all metadata objects are based on lists, so you can use "),e("code",[t._v("str()")]),t._v(" to get the structure of the list and address fields by the "),e("code",[t._v("$")]),t._v(" operator.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("If the package is used with RStudio the metadata can also be nicely rendered as a web page in the viewer panel by running "),e("code",[t._v('collection_viewer(x="SENTINEL1_GRD")')]),t._v(".")])]),t._v(" "),e("h3",{attrs:{id:"processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Please note that the any of this code only creates a process graph representation of your EO analysis and does not take major processing power from you device.")])]),t._v(" "),e("p",[t._v('Processes in openEO are operations that can be applied on (EO) data\n(e.g. calculate the mean of an array, or mask out observations outside a given polygon).\nThe output of one process can be used as the input of another process,\nand by doing so, multiple processes can be connected that way in a larger "process graph":\na new (user-defined) processes that implements a certain algorithm.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Check "),e("a",{attrs:{href:"https://openeo.org/documentation/1.0/glossary.html#processes",target:"_blank",rel:"noopener noreferrer"}},[t._v("the openEO glossary"),e("OutboundLink")],1),t._v("\nfor more details on pre-defined, user-defined processes and process graphs.")])]),t._v(" "),e("p",[t._v("The following code snippet shows how to get the available processes:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# List of available openEO processes with full metadata")]),t._v("\nprocesses "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# List of available openEO processes by identifiers (string)")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("names"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# print metadata of the process with ID "load_collection"')]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("processes"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("code",[t._v("list_processes()")]),t._v(" method returns a list of process metadata objects that the back-end provides.\nEach process list entry is a more complex list object (called 'ProcessInfo') and contains the process identifier and additional metadata about the process, such as expected arguments and return types.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("As for the collection, processes can also be rendered as a web page in the viewer panel, if RStudio is used. In order to open the viewer use "),e("code",[t._v("process_viewer()")]),t._v(" with either a particular process ("),e("code",[t._v('process_viewer("load_collection")')]),t._v(") or you can pass on all processes ("),e("code",[t._v("process_viewer(processes)")]),t._v("). When all processes are chosen, there is also a search bar and a category tree.")])]),t._v(" "),e("h2",{attrs:{id:"authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("p",[t._v("In the code snippets above we did not need to log in\nsince we just queried publicly available back-end information.\nHowever, to run non-trivial processing queries one has to authenticate\nso that permissions, resource usage, etc. can be managed properly.")]),t._v(" "),e("p",[t._v("To handle authentication, openEO leverages "),e("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect (OIDC)"),e("OutboundLink")],1),t._v(".\nIt offers some interesting features (e.g., a user can securely reuse an existing account),\nbut is a fairly complex topic, discussed in more depth on the "),e("RouterLink",{attrs:{to:"/join/free_trial.html"}},[t._v("Free Trial")]),t._v(" page.")],1),t._v(" "),e("p",[t._v("The following code snippet shows how to log in via OIDC authentication:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("login"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Calling this method opens your system web browser, with which you can authenticate yourself on the back-end authentication system.\nAfter that the website will give you the instructions to go back to the R client, where your connection has logged your account in.\nThis means, that every call that comes after that via the connection variable is executed by your user account.")]),t._v(" "),e("h2",{attrs:{id:"creating-a-user-defined-process"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#creating-a-user-defined-process"}},[t._v("#")]),t._v(" Creating a (user-defined) process")]),t._v(" "),e("p",[t._v("Now that we know how to discover the back-end and how to authenticate, lets continue by creating a new batch job to process some data.")]),t._v(" "),e("p",[t._v("First we need to create a process builder object that carries all the available predefined openEO processes of the connected back-end as attached R functions with the parameters stated in the process metadata.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("p "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The functions of the builder return process nodes, which represent a particular result in the workflow. As one of the first steps we need to select the source data collection.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("datacube "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n id "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"SENTINEL1_GRD"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n spatial_extent"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("list"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("west "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" south "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" east "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.65")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" north "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.35")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n temporal_extent"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("c"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"2017-03-01"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"2017-04-01"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n bands"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("c"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"VV"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"VH"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("This results in a process node that represents a "),e("a",{attrs:{href:"https://openeo.org/documentation/1.0/glossary.html#spatial-datacubes",target:"_blank",rel:"noopener noreferrer"}},[t._v("datacube"),e("OutboundLink")],1),t._v(' and contains the "SENTINEL1_GRD" data restricted to the given spatial extent, the given temporal extent and the given bands .')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Sample Data Retrieval")]),t._v(" "),e("p",[t._v("In order to get a better understanding about the processing mechanisms and the data structures used in openEO Platform, it helps to check the actual data from time to time.\nThe function "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/reference/get_sample.html",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("get_sample")]),e("OutboundLink")],1),t._v(" aids the user in downloading data for a very small spatial extent. It is automatically loaded into R so that you can directly inspect it with "),e("code",[t._v("stars")]),t._v('.\nRead the vignette on "'),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/articles/openeo-02-sample_data.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Sample Data Retrieval"),e("OutboundLink")],1),t._v('" for more details.')])]),t._v(" "),e("p",[t._v("Having the input data ready, we want to apply a process on the datacube.\nTherefore, we can call the process directly on the datacube object, which then returns a datacube with the process applied.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("min_reducer "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("function")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("context"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v(" \n return"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("min"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n\nreduced "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("reduce_dimension"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" reducer "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" min_reducer"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" dimension"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"t"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The datacube is now reduced by the time dimension named "),e("code",[t._v("t")]),t._v(', by taking the minimum value of the timeseries values. Now the datacube has no time dimension left. Other so called "reducer" processes exist, e.g. for computing maximum and mean values.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Everything applied to the datacube at this point is neither executed locally on your machine nor executed on the back-end.\nIt just defines the input data and process chain the back-end needs to apply when it sends the datacube to the back-end and executes it there.\nHow this can be done is the topic of the next chapter.")])]),t._v(" "),e("p",[t._v("After applying all processes you want to execute, we need to tell the back-end to export the datacube, for example as GeoTiff:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("formats "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_file_formats"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\nresult "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("save_result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" reduced"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" format "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" formats"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("output"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("GTIFF"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The first line retrieves the back-ends offered input and output formats. 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You can retrieve the status and results later and from any client.')])]),t._v(" "),e("p",[t._v("After the job was executed, status updates can be fetched by using the "),e("code",[t._v("list_jobs()")]),t._v(" function. This function returns a list of job descriptions, which can be indexed with the jobs ID to limit the search results. But remember that only "),e("code",[t._v("list_jobs()")]),t._v(" refreshes this list. 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As mentioned before, training is done after feature extraction outside of openEO, and you can then pickle your model and store it on a repository.\nNext, you define a UDF that unpickles your model, predicts, and returns a new DataCube instance that contains the predicted values instead of the features.")]),t._v(" "),a("CodeSwitcher",{scopedSlots:t._u([{key:"py",fn:function(){return[a("div",{staticClass:"language-python extra-class"},[a("pre",{pre:!0,attrs:{class:"language-python"}},[a("code",[t._v("udf_rf "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token triple-quoted-string string"}},[t._v('"""\nfrom openeo_udf.api.datacube import DataCube\nimport pickle\nimport urllib.request\nimport xarray\nfrom openeo.udf.xarraydatacube import XarrayDataCube\n\ndef apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:\n array = cube.get_array()\n stacked_array = array.stack(pixel=("x","y"))\n stacked_array = stacked_array.transpose()\n clf = pickle.load(urllib.request.urlopen("https://artifactory.vgt.vito.be:443/auxdata-public/openeo/rf_model.pkl"))\n pred_array = clf.predict(stacked_array)\n return DataCube(xarray.DataArray(pred_array.reshape(1,*array.shape[1:]), dims=["bands","y","x"]))\n"""')]),t._v("\n\nclf_results "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" features"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("apply_dimension"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("code"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("udf_rf"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" runtime"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),a("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Python"')]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" dimension"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),a("span",{pre:!0,attrs:{class:"token string"}},[t._v('"bands"')]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("rename_labels"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),a("span",{pre:!0,attrs:{class:"token string"}},[t._v('"bands"')]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),a("span",{pre:!0,attrs:{class:"token string"}},[t._v('"pixel"')]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])])]},proxy:!0},{key:"js",fn:function(){return[a("p",[a("em",[t._v("No JavaScript code available yet.")])])]},proxy:!0}])}),t._v(" "),a("p",[t._v("Note that if your labels are strings, you will have to map them to integers.\nYou can then download the classification results and plot it. Congratulations!")]),t._v(" "),a("p",[t._v("To see a fully working example, you can check out "),a("a",{attrs:{href:"https://github.com/openEOPlatform/SRR2_notebooks/blob/main/UC3%20-%20Crop%20type%20feature%20engineering%20(rule-based).ipynb",target:"_blank",rel:"noopener noreferrer"}},[t._v("this Python notebook on rule-based classification"),a("OutboundLink")],1),t._v(" or "),a("a",{attrs:{href:"https://github.com/openEOPlatform/SRR2_notebooks/blob/main/UC3%20-%20Crop%20type%20feature%20engineering%20using%20random%20forest.ipynb",target:"_blank",rel:"noopener noreferrer"}},[t._v("this Python notebook on classification using Random Forest"),a("OutboundLink")],1),t._v(".")]),t._v(" "),a("p",[t._v("We ran the code in that notebook for ~120 MGRS tiles to end up with a crop cover map for 5 countries in Europe, which looks like this:")]),t._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/10434651/138342824-32d38fbe-7931-497c-8da3-47bbab7a3870.png",alt:"cropcover_5_countries"}})])],1)}),[],!1,null,null,null);a.default=e.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[77],{502:function(t,a,s){"use strict";s.r(a);var n=s(4),e=Object(n.a)({},(function(){var t=this,a=t._self._c;return a("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[a("h1",{attrs:{id:"crop-classification"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#crop-classification"}},[t._v("#")]),t._v(" Crop Classification")]),t._v(" "),a("p",[t._v("The goal of this section is to show how openEO functionality can be integrated into a basic feature engineering pipeline. 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Next we can read in all of these features with their respective label in a pandas dataframe, which can subsequently used for training. Training a model happens outside of openEO and will therefore not be explained in detail here,\nbut you can have a look at the random forest notebook if that is something you need help with.")]),t._v(" "),a("h2",{attrs:{id:"classification"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#classification"}},[t._v("#")]),t._v(" Classification")]),t._v(" "),a("h3",{attrs:{id:"rule-based-classification"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#rule-based-classification"}},[t._v("#")]),t._v(" Rule-based classification")]),t._v(" "),a("p",[t._v("A simple approach is to define rules based on your features to classify crops. For example, when looking at temporal profiles of corn, we can see that the NDVI of may is smaller than the NDVI of june. 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This page describes some best practices based on the example\nof "),t("a",{attrs:{href:"https://github.com/openEOPlatform/openeo-classification",target:"_blank",rel:"noopener noreferrer"}},[e._v("processing a croptype map for all 27 countries in the European Union"),t("OutboundLink")],1),e._v(". We do encourage you to get in touch with backend providers about your\nspecific case, as not every workflow is the same, and some advance planning may be needed to ensure that you get sufficient\nprocessing resources.")]),e._v(" "),t("figure",[t("img",{attrs:{src:"https://raw.githubusercontent.com/openEOPlatform/openeo-classification/60aa7a869f9000b1795afe2c9dde0d7977bcdbc6/docs/full_europe.png",alt:"Crop type map for EU27"}}),e._v(" "),t("figcaption",[e._v("EU27 croptype map, processed on openEO platform")])]),e._v(" "),t("p",[e._v("The approach desribed here is based on local files to track the production. This is a low-cost approach that does not require\nspecial IT knowledge, but comes with some risks such as loosing your local files. A more robust approach for production-grade projects\nwould typically rely on some sort of database or STAC catalog service to monitor processing. Such a setup is however quite similar in many aspects.")]),e._v(" "),t("p",[e._v("The basic strategy for processing large areas is to split it up into smaller areas, usually according to a regular tile grid.\nSplitting reduces the size of the area that needs to be processed by one batch job, and avoids running into all kinds of limitations.\nFor instance, when processing in a specific projection, you anyway have to stay within the bounds of that projection. Also the output\nfile size of a job often becomes impractical when working over huge areas. Or you will hit bottlenecks in the backend implementation that\ndo not occur for normally sized jobs. Also, when a smaller job fails or requires reprocessing, the cost will be smaller.")]),e._v(" "),t("h2",{attrs:{id:"relevant-openeo-features"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#relevant-openeo-features"}},[e._v("#")]),e._v(" Relevant openEO features")]),e._v(" "),t("p",[e._v("We want to highlight a few key elements that made us choose openEO for large scale processing:")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://openeo.org/documentation/1.0/developers/backends/performance.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Performance & scalability"),t("OutboundLink")],1)]),e._v(" "),t("li",[e._v("STAC metadata is generated for you, making your output dissemination ready without becoming a metadata expert yourself.")]),e._v(" "),t("li",[e._v("Where relevant FAIR principles are taken into account automatically, such as providing provenance information.")]),e._v(" "),t("li",[e._v("Cloud optimized file formats are generated by default.")]),e._v(" "),t("li",[e._v("Processing can be distributed over multiple backends.")])]),e._v(" "),t("h2",{attrs:{id:"preparation"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#preparation"}},[e._v("#")]),e._v(" Preparation")]),e._v(" "),t("p",[e._v("The idea is that we first create and persist the list of tiles to produce, with all attributes required to produce that\nspecific tile. This gives us a very good visual overview of the processing that will be performed.")]),e._v(" "),t("p",[e._v("Having job parameters in a file is also useful for debugging afterwards. Determining parameters at runtime means you don't\nhave absolute certainty over the value of a specific argument, as there may be bugs in your code.")]),e._v(" "),t("h2",{attrs:{id:"prepare-tiling-grid"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#prepare-tiling-grid"}},[e._v("#")]),e._v(" Prepare tiling grid")]),e._v(" "),t("p",[e._v("The choice of tiling grid depends on your preferred projection system, which depends on your area of interest.\nFor Europe, the EPSG:3035 projection can be used, while for global processing you may want to work with different projections according\nto UTM zones.")]),e._v(" "),t("p",[e._v("The size of tiles in your grid is also important, and often ranges from 20km to 100km. For relatively light workflows, a 100km grid can work well,\nwhile for more demanding cases, a 20km grid is better. In our example, we choose to work with 20km tiles because the workflow was quite demanding. A smaller\ntile size can also result in less unneeded processing when your target area has an irregular shape, like most countries and continents.")]),e._v(" "),t("p",[e._v("A couple of basic grids can be found here:\n"),t("a",{attrs:{href:"https://artifactory.vgt.vito.be/webapp/#/artifacts/browse/tree/General/auxdata-public/grids",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://artifactory.vgt.vito.be/webapp/#/artifacts/browse/tree/General/auxdata-public/grids"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("The images below illustrate the overlap in the UTM grids versus a regular LAEA grid.")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",{staticStyle:{"text-align":"center"}},[e._v("UTM 100km")]),e._v(" "),t("th",{staticStyle:{"text-align":"center"}},[e._v("LAEA 100km")])])]),e._v(" "),t("tbody",[t("tr",[t("td",{staticStyle:{"text-align":"center"}},[t("img",{attrs:{src:"https://user-images.githubusercontent.com/5937096/231963581-1c51a512-c240-4d23-b557-30a3577c9027.png",width:"400",height:"300"}})]),e._v(" "),t("td",{staticStyle:{"text-align":"center"}},[t("img",{attrs:{src:"https://user-images.githubusercontent.com/5937096/231963750-562b921c-7b5b-4ec1-86ca-cf1fd75e625d.png",width:"400",height:"300"}})])])])]),e._v(" "),t("p",[e._v("A grid can be masked based on the countries we want to load, the following script shows an example:")]),e._v(" "),t("div",{staticClass:"language-python extra-class"},[t("pre",{pre:!0,attrs:{class:"language-python"}},[t("code",[t("span",{pre:!0,attrs:{class:"token keyword"}},[e._v("import")]),e._v(" geopandas "),t("span",{pre:!0,attrs:{class:"token keyword"}},[e._v("as")]),e._v(" gpd\neurope "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" gpd"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("read_file"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("gpd"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("datasets"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("get_path"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"naturalearth_lowres"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),e._v("\neurope "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" europe"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("[")]),e._v("europe"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("continent"),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("==")]),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"Europe"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("]")]),e._v("\ncountries "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" europe"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("[")]),e._v("europe"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("name"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("isin"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("EU27"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("]")]),e._v("\ndf "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" gpd"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("read_file"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"https://artifactory.vgt.vito.be/auxdata-public/grids/LAEA-20km.gpkg"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("mask"),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v("countries"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),e._v("\n")])])]),t("h2",{attrs:{id:"prepare-job-attributes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#prepare-job-attributes"}},[e._v("#")]),e._v(" Prepare job attributes")]),e._v(" "),t("p",[e._v("Next to the tiling grid, we recommend to also determine other properties required by your processing jobs up front. This allows you to properly\nreview those properties before starting to process. Examples include simple things like a job title or tile specific processing parameters, but also an\nattribute to determine processing order.")]),e._v(" "),t("p",[e._v("In this step, you may also want to make sure to already determine the correct tile extent in the coordinate system of your tile grid.\nProviding exact coordinates in the right projection is necessary to ensure pixel-perfect alignment of your tiles.")]),e._v(" "),t("h2",{attrs:{id:"tuning-your-processing-job"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#tuning-your-processing-job"}},[e._v("#")]),e._v(" Tuning your processing job")]),e._v(" "),t("p",[e._v("Before kicking off large processing, you want to be very sure that the correct output is generated, and that you have sufficient credits and resources\navailable to finish your job in time. This can be done by simply running various jobs, and using the statistics reported in the metadata to determine average parameters. (The map production section below shows a way to collect these parameters in a CSV.)")]),e._v(" "),t("p",[e._v("For instance, for the case of processing the EU27 croptype map, consisting of ~11000 20km tiles, we made the following calculations op front:")]),e._v(" "),t("ul",[t("li",[e._v("Average runtime was 30 minutes, which means that it would take ~15 days of continous processing with 15 parallel jobs.")]),e._v(" "),t("li",[e._v("Average cost was below 100 credits, so we would be able to process with a budget of 1100000 credits.")])]),e._v(" "),t("p",[e._v("To achieve these numbers, we did have to optimize batch job settings and also the overall workflow to reduce resource usage!")]),e._v(" "),t("p",[e._v("A common bottleneck to parallellization is the memory consumption, and it can be useful to know the maximum memory allocation on a single machine in\nyour backend of choice. For instance, in a cloud environment with 16GB per machine and 4 cpu's, using slightly less than 4GB per worker is efficient as you can fit 4 parallel workers on a single VM, while requiring 6GB would fit only 2 workers and leave about 4GB unused.")]),e._v(" "),t("p",[e._v("In our example, we used the Geotrellis backends, which has "),t("RouterLink",{attrs:{to:"/federation/#customizing-batch-job-resources-on-terrascope"}},[e._v("these execution options")]),e._v(".")],1),e._v(" "),t("h2",{attrs:{id:"starting-map-production"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#starting-map-production"}},[e._v("#")]),e._v(" Starting map production")]),e._v(" "),t("p",[e._v("The openEO Python client provides a useful tool to run multiple processing jobs in multiple backends:")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/cookbook/job_manager.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://open-eo.github.io/openeo-python-client/cookbook/job_manager.html"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("This class takes a GeoJSON corresponding to your tile grid and job properties per tiles, and triggers a function provided by you whenever a new\njob needs to be created. You can configure multiple backends, and set the number of parallel jobs per backend.")]),e._v(" "),t("p",[e._v("This class also takes care of error handling, and can be considered more resilient compared to writing a simple loop yourself.")]),e._v(" "),t("p",[e._v("A full example of how we use this can be found "),t("a",{attrs:{href:"https://github.com/openEOPlatform/openeo-classification/blob/main/src/openeo_classification/scripts/cropmap_eu27.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("This script uses a CSV file to track your jobs, and whenever it is interrupted it can simply resume from that CSV file, making it tolerant to failure.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/5937096/231968590-f0f0b415-453c-4ab7-9502-82eab795a84e.png",alt:"Tracking jobs by CSV"}})]),e._v(" "),t("h2",{attrs:{id:"errors-during-production"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#errors-during-production"}},[e._v("#")]),e._v(" Errors during production")]),e._v(" "),t("p",[e._v("It is expected to see jobs failing during production, which can be considered normal as long as the failure rate is not too high. We advice to quickly inspect error logs, and if no obvious reason for failure is found, a simple retry might be sufficient. In other cases it may be needed to increase memory.\nWe also see a limited number of cases where for instance issues in the underlying product archive cause failures or artifacts. These are harder to resolve, and may require interaction with the backend to resove!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[80],{507:function(e,t,a){"use strict";a.r(t);var r=a(4),o=Object(r.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"large-scale-processing"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#large-scale-processing"}},[e._v("#")]),e._v(" Large scale processing")]),e._v(" "),t("p",[e._v("Processing of larger areas up to global scale is one of the more challenging tasks in earth observation,\nbut certainly one that this platform wants to address. This page describes some best practices based on the example\nof "),t("a",{attrs:{href:"https://github.com/openEOPlatform/openeo-classification",target:"_blank",rel:"noopener noreferrer"}},[e._v("processing a croptype map for all 27 countries in the European Union"),t("OutboundLink")],1),e._v(". We do encourage you to get in touch with backend providers about your\nspecific case, as not every workflow is the same, and some advance planning may be needed to ensure that you get sufficient\nprocessing resources.")]),e._v(" "),t("figure",[t("img",{attrs:{src:"https://raw.githubusercontent.com/openEOPlatform/openeo-classification/60aa7a869f9000b1795afe2c9dde0d7977bcdbc6/docs/full_europe.png",alt:"Crop type map for EU27"}}),e._v(" "),t("figcaption",[e._v("EU27 croptype map, processed on openEO platform")])]),e._v(" "),t("p",[e._v("The approach desribed here is based on local files to track the production. This is a low-cost approach that does not require\nspecial IT knowledge, but comes with some risks such as loosing your local files. A more robust approach for production-grade projects\nwould typically rely on some sort of database or STAC catalog service to monitor processing. Such a setup is however quite similar in many aspects.")]),e._v(" "),t("p",[e._v("The basic strategy for processing large areas is to split it up into smaller areas, usually according to a regular tile grid.\nSplitting reduces the size of the area that needs to be processed by one batch job, and avoids running into all kinds of limitations.\nFor instance, when processing in a specific projection, you anyway have to stay within the bounds of that projection. Also the output\nfile size of a job often becomes impractical when working over huge areas. Or you will hit bottlenecks in the backend implementation that\ndo not occur for normally sized jobs. Also, when a smaller job fails or requires reprocessing, the cost will be smaller.")]),e._v(" "),t("h2",{attrs:{id:"relevant-openeo-features"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#relevant-openeo-features"}},[e._v("#")]),e._v(" Relevant openEO features")]),e._v(" "),t("p",[e._v("We want to highlight a few key elements that made us choose openEO for large scale processing:")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://openeo.org/documentation/1.0/developers/backends/performance.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Performance & scalability"),t("OutboundLink")],1)]),e._v(" "),t("li",[e._v("STAC metadata is generated for you, making your output dissemination ready without becoming a metadata expert yourself.")]),e._v(" "),t("li",[e._v("Where relevant FAIR principles are taken into account automatically, such as providing provenance information.")]),e._v(" "),t("li",[e._v("Cloud optimized file formats are generated by default.")]),e._v(" "),t("li",[e._v("Processing can be distributed over multiple backends.")])]),e._v(" "),t("h2",{attrs:{id:"preparation"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#preparation"}},[e._v("#")]),e._v(" Preparation")]),e._v(" "),t("p",[e._v("The idea is that we first create and persist the list of tiles to produce, with all attributes required to produce that\nspecific tile. This gives us a very good visual overview of the processing that will be performed.")]),e._v(" "),t("p",[e._v("Having job parameters in a file is also useful for debugging afterwards. Determining parameters at runtime means you don't\nhave absolute certainty over the value of a specific argument, as there may be bugs in your code.")]),e._v(" "),t("h2",{attrs:{id:"prepare-tiling-grid"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#prepare-tiling-grid"}},[e._v("#")]),e._v(" Prepare tiling grid")]),e._v(" "),t("p",[e._v("The choice of tiling grid depends on your preferred projection system, which depends on your area of interest.\nFor Europe, the EPSG:3035 projection can be used, while for global processing you may want to work with different projections according\nto UTM zones.")]),e._v(" "),t("p",[e._v("The size of tiles in your grid is also important, and often ranges from 20km to 100km. For relatively light workflows, a 100km grid can work well,\nwhile for more demanding cases, a 20km grid is better. In our example, we choose to work with 20km tiles because the workflow was quite demanding. A smaller\ntile size can also result in less unneeded processing when your target area has an irregular shape, like most countries and continents.")]),e._v(" "),t("p",[e._v("A couple of basic grids can be found here:\n"),t("a",{attrs:{href:"https://artifactory.vgt.vito.be/webapp/#/artifacts/browse/tree/General/auxdata-public/grids",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://artifactory.vgt.vito.be/webapp/#/artifacts/browse/tree/General/auxdata-public/grids"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("The images below illustrate the overlap in the UTM grids versus a regular LAEA grid.")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",{staticStyle:{"text-align":"center"}},[e._v("UTM 100km")]),e._v(" "),t("th",{staticStyle:{"text-align":"center"}},[e._v("LAEA 100km")])])]),e._v(" "),t("tbody",[t("tr",[t("td",{staticStyle:{"text-align":"center"}},[t("img",{attrs:{src:"https://user-images.githubusercontent.com/5937096/231963581-1c51a512-c240-4d23-b557-30a3577c9027.png",width:"400",height:"300"}})]),e._v(" "),t("td",{staticStyle:{"text-align":"center"}},[t("img",{attrs:{src:"https://user-images.githubusercontent.com/5937096/231963750-562b921c-7b5b-4ec1-86ca-cf1fd75e625d.png",width:"400",height:"300"}})])])])]),e._v(" "),t("p",[e._v("A grid can be masked based on the countries we want to load, the following script shows an example:")]),e._v(" "),t("div",{staticClass:"language-python extra-class"},[t("pre",{pre:!0,attrs:{class:"language-python"}},[t("code",[t("span",{pre:!0,attrs:{class:"token keyword"}},[e._v("import")]),e._v(" geopandas "),t("span",{pre:!0,attrs:{class:"token keyword"}},[e._v("as")]),e._v(" gpd\neurope "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" gpd"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("read_file"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("gpd"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("datasets"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("get_path"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"naturalearth_lowres"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),e._v("\neurope "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" europe"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("[")]),e._v("europe"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("continent"),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("==")]),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"Europe"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("]")]),e._v("\ncountries "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" europe"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("[")]),e._v("europe"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("name"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("isin"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("EU27"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("]")]),e._v("\ndf "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" gpd"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),e._v("read_file"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"https://artifactory.vgt.vito.be/auxdata-public/grids/LAEA-20km.gpkg"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v("mask"),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v("countries"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),e._v("\n")])])]),t("h2",{attrs:{id:"prepare-job-attributes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#prepare-job-attributes"}},[e._v("#")]),e._v(" Prepare job attributes")]),e._v(" "),t("p",[e._v("Next to the tiling grid, we recommend to also determine other properties required by your processing jobs up front. This allows you to properly\nreview those properties before starting to process. Examples include simple things like a job title or tile specific processing parameters, but also an\nattribute to determine processing order.")]),e._v(" "),t("p",[e._v("In this step, you may also want to make sure to already determine the correct tile extent in the coordinate system of your tile grid.\nProviding exact coordinates in the right projection is necessary to ensure pixel-perfect alignment of your tiles.")]),e._v(" "),t("h2",{attrs:{id:"tuning-your-processing-job"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#tuning-your-processing-job"}},[e._v("#")]),e._v(" Tuning your processing job")]),e._v(" "),t("p",[e._v("Before kicking off large processing, you want to be very sure that the correct output is generated, and that you have sufficient credits and resources\navailable to finish your job in time. This can be done by simply running various jobs, and using the statistics reported in the metadata to determine average parameters. (The map production section below shows a way to collect these parameters in a CSV.)")]),e._v(" "),t("p",[e._v("For instance, for the case of processing the EU27 croptype map, consisting of ~11000 20km tiles, we made the following calculations op front:")]),e._v(" "),t("ul",[t("li",[e._v("Average runtime was 30 minutes, which means that it would take ~15 days of continous processing with 15 parallel jobs.")]),e._v(" "),t("li",[e._v("Average cost was below 100 credits, so we would be able to process with a budget of 1100000 credits.")])]),e._v(" "),t("p",[e._v("To achieve these numbers, we did have to optimize batch job settings and also the overall workflow to reduce resource usage!")]),e._v(" "),t("p",[e._v("A common bottleneck to parallellization is the memory consumption, and it can be useful to know the maximum memory allocation on a single machine in\nyour backend of choice. For instance, in a cloud environment with 16GB per machine and 4 cpu's, using slightly less than 4GB per worker is efficient as you can fit 4 parallel workers on a single VM, while requiring 6GB would fit only 2 workers and leave about 4GB unused.")]),e._v(" "),t("p",[e._v("In our example, we used the Geotrellis backends, which has "),t("RouterLink",{attrs:{to:"/federation/#customizing-batch-job-resources-on-terrascope"}},[e._v("these execution options")]),e._v(".")],1),e._v(" "),t("h2",{attrs:{id:"starting-map-production"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#starting-map-production"}},[e._v("#")]),e._v(" Starting map production")]),e._v(" "),t("p",[e._v("The openEO Python client provides a useful tool to run multiple processing jobs in multiple backends:")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/cookbook/job_manager.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://open-eo.github.io/openeo-python-client/cookbook/job_manager.html"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("This class takes a GeoJSON corresponding to your tile grid and job properties per tiles, and triggers a function provided by you whenever a new\njob needs to be created. 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n=0;nn||(t.hash?!r.a.$vuepress.$get("disableScrollBehavior")&&{selector:decodeURIComponent(t.hash)}:{x:0,y:0})});!function(t){t.beforeEach((e,n,r)=>{if(Fe(t,e.path))r();else if(/(\/|\.html)$/.test(e.path))if(/\/$/.test(e.path)){const n=e.path.replace(/\/$/,"")+".html";Fe(t,n)?r(n):r()}else r();else{const n=e.path+"/",o=e.path+".html";Fe(t,o)?r(o):Fe(t,n)?r(n):r()}})}(n);const o={};try{await Promise.all(Ue.filter(t=>"function"==typeof t).map(e=>e({Vue:r.a,options:o,router:n,siteData:Me,isServer:t})))}catch(t){console.error(t)}return{app:new r.a(Object.assign(o,{router:n,render:t=>t("div",{attrs:{id:"app"}},[t("RouterView",{ref:"layout"}),t("div",{class:"global-ui"},Ie.map(e=>t(e)))])})),router:n}}(!1).then(({app:t,router:e})=>{e.onReady(()=>{t.$mount("#app")})})}]); \ No newline at end of file diff --git a/data-collections/index.html b/data-collections/index.html index 670088ac1..7aa7a9154 100644 --- a/data-collections/index.html +++ b/data-collections/index.html @@ -8,7 +8,7 @@ - + @@ -145,6 +145,6 @@
Contact (opens new window)

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- + diff --git a/federation/accounting.html b/federation/accounting.html index b8b3b58da..8718eada3 100644 --- a/federation/accounting.html +++ b/federation/accounting.html @@ -8,7 +8,7 @@ - + @@ -162,6 +162,6 @@ costs a few euros, but then you would also notice that your job is taking multiple hours to run.

In any case, once you've established an initial cost for a small area, you can extrapolate to a larger area. If simple linear extrapolation shows that a larger job is affordable, then run the job on larger areas, like 50ha or up to 100x100km. This will show you how your job scales, and what kind of costs you will be incurring! If at any point the cost appears unreasonable, please contact the platform!

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- + diff --git a/federation/backends/api.html b/federation/backends/api.html index 81b83cda4..79d1fbaf3 100644 --- a/federation/backends/api.html +++ b/federation/backends/api.html @@ -8,7 +8,7 @@ - + @@ -147,6 +147,6 @@ (opens new window)

# Federation API

The general contract is the openEO API (opens new window) in the latest stable version of the 1.x branch.

The aggregator that proxies the back-ends in the federation also implements the same API, but it also implements the "Federation Extension" (currently in draft state).

# Authentication and authorization

This important aspect of the federation is standardized by the AARC Blueprint Architecture (opens new window). EGI Check-in is the concrete implementation that is currently in use.

# Authentication

The openEO platform federation standardizes on the use of EGI Check-in (opens new window) as identity provider. Backends have to support the use of openID connect + PKCE, to enable this and register a client with EGI Check-in.

# Authorization

# Entitlements

Users of the federation are organized under the 'vo.openeo.cloud' virtual organization in EGI Check-in. Inside the virtual organization, different roles can be assigned to a user, to indicate that they have a certain subscription, or even on a more fine-grained level are entitled to specific actions or resources. The mechanism to check this, is again supported by EGI Check-in, under the 'eduperson_entitlement' claim: https://docs.egi.eu/providers/check-in/sp/#claims

# Credits

The second criterium for authorization is based on credits that are available to a user. Credits allow the platform to limit the volume of data access and processing operations that a user can perform during a given time frame. The amount of available credits depends on the subscription. When the credit balance of a user goes below zero, processing operations can be blocked.

# Aggregator rules

Based on the subscription and available credits, the aggregator can implement these rules:

  1. Credit checks to block starting of batch jobs, synchronous requests to /result and viewing services.
  2. Rate limiting (TBD)

# Backend rules

Some authorization rules will need to be enforced by the backends themselves:

  1. Basic access and access to user specific resources based on subscription role.
  2. Number of concurrent batch jobs
  3. Available processing resources, batch job priorities
  4. Batch job result data volume
  5. Access to restricted collections
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- + diff --git a/federation/backends/collections.html b/federation/backends/collections.html index 1325a8f9a..1221fa7dd 100644 --- a/federation/backends/collections.html +++ b/federation/backends/collections.html @@ -8,7 +8,7 @@ - + @@ -153,6 +153,6 @@ considered non-experimental.

  1. Collections need to indicate the key 'providers' that are responsible for ensuring access to the data and continuity in the case of active missions. The user may depend on the guarantees offered by these providers with respect to the properties (timeliness, completeness,...) of a specific collection. The providers with role 'host' and 'producer' are mandatory.

  2. The collection description and extents needs to specify known limitations with respect to the original collection. For instance, if only a subset of the full archive is available, this should be indicated. Extents can be rough approximations to avoid requiring very detailed geometry in the metadata.

  3. Collections without an end time are assumed to be active missions. By default, 99% of items in these collections should be available within 48 hours after being published by the producer. This gives users a basic guarantee with respect to timeliness of products.

  4. Collection metadata should be valid STAC metadata and must include all extensions in stac_extensions. Tools such as STAC-validator (opens new window) can indicate obvious issues.

  5. FAIR principle R1: (Meta)data are richly described with a plurality of accurate and relevant attributes (opens new window)

  6. Collections have to follow harmonization guidelines specified below, if applicable.

  7. Collections naming (id, dimensions, bands) should remain constant.

  8. Backwards incompatible changes or removal need to be announced with a lead time of 6 months, together with a migration path.

  9. Minimum availability of non-experimental collections is 98% on a monthly basis. The backend availability is used here if availability is not measured per collection.

  10. Availability of a collection means that a simple process graph (e.g. using load_collection) returns a correct result. Collections may have special conditions to work, for instance in the case of commercial data.

# Harmonization

When back-ends offer/mirror the same datasets, it is required to align names and metadata. For the following collections and metadata an agreement has been achieved. These are all Copernicus Missions, and the standard names refer to the archives prepared and distributed by ESA. If it is not possible/desirable to use this name as collection id, a 'common_name' can be added next to the 'id' property to identify the collection as a standard archive.

# Common naming convention

In order to achieve a uniform structure for all collections on the platform and thus make it easier for users to navigate between collections, it is recommended to follow the common naming convention*:

  • Names should be written in capital letters ("all caps")
  • Names should consist of a combination of different optional attributes (see table)
  • The different attributes should be separated by an underscore

Very roughly speaking, collections can be divided into two groups (in reality it is more of a spectrum with all gradations in between):

  • collections containing raw data (or processing levels of that data) measured directly by a satellite (or an other measurement platform) and often distributed by the platform operator (e.g., ESA)
  • derived collections, which are based on (pre-processed) raw data that has been processed to create a collection with a specific purpose (e.g., a land cover map) and are often distributed by the institution (or a service of an institution) that created the collection
Attribute Type Description Examples
Provider string Often used for derived collections produced or order by the listed provider. ESA, CNSE, EMODNET, TERRASCOPE. CAMS, CGLS
Satellite/Platform string Name of the satellite/platform that acquired the data in the collection. SENTINEL2, LANDSAT8, PALSAR2
Processing level string Name of the level to which the data was processed (often processed raw data). L2A, L3, L2_1
Version string Often used for derived collections that are produced in several versions. V1, V2
Resolution string (number + unit or string) Usually added, if the resolution is of particular importance for the collection (e.g., novel product with this resolution) for the collection. 10M, 120M, EUROPE, GLOBAL
Product Description string Human readable description of the data within the collection. Can also be an abbreviation or acronym. LAND_COVER_MAP, WORLDCOVER, NDVI, LAI
Year number Often used for derived products that where updated in the specified year or created based on data of the specified year. 2022

Collections containing raw data or processing levels of that data often use a combination of satellite/platform and processing level (e.g., SENTINEL2_L1C ). Derived collections often use a combination of provider and product description (e.g., CNES_LAND_COVER_MAP).

*Some existing collections may not strictly follow this naming convention as they were added to the platform prior to this agreement.

# Sentinel2-L2A

The common name for this collection is 'SENTINEL2_L2A'. It refers to the L2A products generated by the Sen2Cor software, which can be configured to be compatible with the ESA generated products. Note that the products in the ESA archive were also processed with different versions of Sen2Cor, so it is not possible to specify a very specific version or configuration of the processing chain.

# Bands

Band names for spectral bands follow the Bxx naming convention used by ESA. For example: B01, B02, B03, B08, B8A, B12

  • SCL = the Sen2Cor scene classification band
  • approximateViewAzimuth = collective term for the mean and accurate viewing azimuth angle. Depending on which backend is processing the data, the mean angle (for Sentinel Hub) or the accurate angle (for Terrascope) is used. If the accurate angle (viewAzimuthAngles) or the mean angle (viewAzimuthMean) is explicitly specified, the data is processed on the backend that holds the specified band.
  • viewZenithMean = collective term for the mean and accurate viewing zenith angle. Depending on which backend is processing the data, the mean angle (for Sentinel Hub) or the accurate angle (for Terrascope) is used. If the accurate angle (viewZenithMean) or the mean angle (viewZenithAngles) is explicitly specified, the data is processed on the backend that holds those bands.
  • sunAzimuthAngles/sunZenithAngles = collective term for the exact sun azimuth and sun zenith angle.

# Common Properties

We list here a set of common properties, that can be relevant for multiple collections. Collections are strongly encouraged to use these properties instead of using a different name for the same property.

# Common

# Optical instruments

# SAR instruments

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- + diff --git a/federation/backends/fileformats.html b/federation/backends/fileformats.html index 090d9b408..4b799cde0 100644 --- a/federation/backends/fileformats.html +++ b/federation/backends/fileformats.html @@ -8,7 +8,7 @@ - + @@ -149,6 +149,6 @@
  • georeferenced (x/y dimensions)
  • can store multiple bands (band dimension)
  • can store multiple timestamps (time dimension)
  • self-describing, portable and scalable
  • GeoTiff: ideal for storing several bands in one file in cloud optimized format
    • georeferenced
    • can store multiple bands
    • a single GeoTiff corresponds to one timestamp (in combination with STAC, multi-temporal collections can be supported)
    • cloud optimized

    • # Federation agreement file formats

    If back-ends offer/mirror the same file formats for both import and export, it is required to align them.

    For file export through save_result for example, the output parameters and the structure of the data that is written to storage needs to be defined. For the following file formats an agreement has been achieved:

    • GeoTiff
    • netCDF

    The idea of these guidelines is to align with what the formats and corresponding toolchains support as much as possible.

    # GeoTiff

    Defaults:

    # netCDF

    Defaults:

    • The full datacube is written to a single netCDF.
    • The openEO dimension metadata is preserved in the netCDF file.
    • CF conventions (https://cfconventions.org/) are used where applicable.
    • Data is chunked and compressed

    More information on all supported file formats, can be found here.

    Edit this page (opens new window)
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    - + diff --git a/federation/backends/index.html b/federation/backends/index.html index dbdda1182..4b14fe4ac 100644 --- a/federation/backends/index.html +++ b/federation/backends/index.html @@ -8,7 +8,7 @@ - + @@ -152,6 +152,6 @@ the provider is expected to stop working on new features and improve reliability, or to mark the component as experimental. Reverting a 'stable' feature to 'experimental' should be considered a backwards incompatible change, requiring communication towards the user and proper consideration of the impact.

    Note

    To join the federation, it is required to (mostly) fulfill these requirements and document differences for users in the "Federation Aspects and Known Issues". Nevertheless, these requirements are negotiable if there are good arguments for a change as the current state of the "contract" is just the compromise that the existing providers have agreed upon and if a new back-end joins the federation new compromises may need to be made.

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    - + diff --git a/federation/backends/processes.html b/federation/backends/processes.html index 014dc263b..b4148ff7e 100644 --- a/federation/backends/processes.html +++ b/federation/backends/processes.html @@ -8,7 +8,7 @@ - + @@ -146,6 +146,6 @@ Contact (opens new window)

    # Processes

    # Core Profile

    As the openEO project defines a lot of processes, we need to define a core profile (i.e. a subset) that needs to be implemented on each back-end. All processes specifications can be found at https://processes.openeo.org (opens new window)

    # Data Cubes

    • add_dimension: Add a new dimension
    • aggregate_spatial: Zonal statistics for geometries
    • aggregate_temporal: Temporal aggregations
    • aggregate_temporal_period: Temporal aggregations based on calendar hierarchies
    • apply: Apply a process to each pixel
    • apply_dimension: Apply a process to pixels along a dimension
    • apply_kernel: Apply a spatial convolution with a kernel
    • dimension_labels: Get the dimension labels
    • drop_dimension: Remove a dimension
    • filter_bands: Filter the bands by names
    • filter_bbox: Spatial filter using a bounding box
    • filter_spatial: Spatial filter using geometries
    • filter_temporal: Temporal filter for temporal intervals
    • load_collection: Load a collection
    • load_result: Load batch job results - experimental
    • mask: Apply a raster mask
    • mask_polygon: Apply a polygon mask
    • merge_cubes: Merge two data cubes
    • reduce_dimension: Reduce dimensions
    • rename_dimension: Rename a dimension
    • rename_labels: Rename dimension labels -> needed often for apply_dimension
    • resample_cube_spatial: Resample the spatial dimensions to match a target data cube
    • resample_cube_temporal: Resample temporal dimensions to match a target data cube - experimental
    • resample_spatial: Resample and warp the spatial dimensions
    • save_result: Save processed data

    # Arrays / Reducers

    • array_append: Append a value to an array - experimental
    • array_apply: Apply a process to each array element
    • array_concat: Merge two arrays - experimental
    • array_contains: Check whether the array contains a given value
    • array_create: Create an array - experimental
    • array_element: Get an element from an array
    • array_filter: Filter an array based on a condition
    • array_find: Get the index for a value in an array
    • array_interpolate_linear: One-dimensional linear interpolation for arrays - experimental
    • array_labels: Get the labels for an array
    • array_modify: Change the content of an array (insert, remove, update) - experimental
    • count: Count the number of elements
    • extrema: Minimum and maximum values
    • first: First element
    • last: Last element
    • max: Maximum value
    • mean: Arithmetic mean (average)
    • median: Statistical median
    • min: Minimum value
    • order: Create a permutation
    • product: Compute the product by multiplying numbers
    • rearrange: Rearrange an array based on a permutation
    • sort: Sort data
    • sum: Compute the sum by adding up numbers

    # Math

    • absolute: Absolute value
    • add: Addition of two numbers
    • arccos: Inverse cosine
    • arcosh: Inverse hyperbolic cosine
    • arcsin: Inverse sine
    • arctan: Inverse tangent
    • arctan2: Inverse tangent of two numbers
    • arsinh: Inverse hyperbolic sine
    • artanh: Inverse hyperbolic tangent
    • ceil: Round fractions up
    • clip: Clip a value between a minimum and a maximum
    • constant: Define a constant value -> pretty easy implementation
    • cos: Cosine
    • cosh: Hyperbolic cosine
    • divide: Division of two numbers
    • e: Euler's number
    • exp: Exponentiation to the base e
    • floor: Round fractions down
    • int: Integer part of a number
    • linear_scale_range: Linear transformation between two ranges
    • ln: Natural logarithm
    • log: Logarithm to a base
    • mod: Modulo
    • multiply: Multiplication of two numbers
    • nan - Not a Number - experimental
    • pi: Pi
    • power: Exponentiation
    • round: Round to a specified precision
    • sgn: Signum
    • sin: Sine
    • sinh: Hyperbolic sine
    • sqrt: Square root
    • subtract: Subtraction of two numbers
    • tan: Tangent
    • tanh: Hyperbolic tangent

    # Statistics / Indices

    • ndvi: Normalized Difference Vegetation Index
    • normalized_difference: Normalized difference
    • quantiles: Quantiles
    • sd: Standard deviation
    • variance: Variance

    # Logic

    • and: Logical AND
    • all: Are all of the values true?
    • any: Is at least one value true?
    • between: Between comparison
    • eq: Equal to comparison
    • gt: Greater than comparison
    • gte: Greater than or equal to comparison
    • if: If-Then-Else conditional
    • is_infinite: Value is an infinite number - experimental
    • is_nan: Value is not a number
    • is_nodata: Value is a no-data value
    • is_valid: Value is valid data
    • lt: Less than comparison
    • lte: Less than or equal to comparison
    • neq: Not equal to comparison
    • not: Inverting a boolean
    • or: Logical OR
    • xor: Logical XOR (exclusive or)
    Edit this page (opens new window)
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    - + diff --git a/federation/index.html b/federation/index.html index 05af183b0..2b53e4d34 100644 --- a/federation/index.html +++ b/federation/index.html @@ -8,7 +8,7 @@ - + @@ -250,6 +250,6 @@

    This is a short overview of the various options:

    • executor-memory: memory assigned to your workers, for the JVM that executes most predefined processes
    • executor-memoryOverhead: memory assigned on top of the JVM, for instance to run UDF's
    • executor-cores: number of CPUs per worker (executor). The number of parallel tasks is executor-cores/task-cpus
    • task-cpus: CPUs assigned to a single task. UDF's using libraries like Tensorflow can benefit from further parallellization on the level of individual tasks.
    • executor-request-cores: this settings is only relevant for Kubernetes based backends, allows to overcommit CPU
    • max-executors: the maximum number of workers assigned to your job. Maximum number of parallel tasks is max-executors*executor-cores/task-cpus. Increasing this can inflate your costs, while not necessarily improving performance!
    • driver-memory: memory assigned to the spark 'driver' JVM that controls execution of your batch job
    • driver-memoryOverhead: memory assigned to the spark 'driver' on top of JVM memory, for Python processes.
    • logging-threshold: the threshold for logging, set to 'info' by default, can be set to 'debug' to generate much more logging
    • udf-dependency-archives: an array of urls pointing to zip files with extra dependencies, see below

    # Custom UDF dependencies

    User defined functions often depend on (specific versions of) libraries or require small auxiliary data files. The UDF specifications do not yet define a standardized manner to provide this other than having the ability of selecting from a predefined set of 'runtimes' that than again have a predefined configuration.

    The Terrascope/Geotrellis backends solve this via the udf-dependency-archives job option, that allows to specify a list of zip files that should be included in the working directory of the UDF.

    This enables the following example workflow for Python UDF's:

    1. Create a Python 'virtualenv' with your dependencies
    2. Based on the 'site-packages' directory of the virtualenv, create a zip file with all dependencies
    3. Upload the zip to a url that can be reached by the backend.
    4. In job options, add "udf-dependency-archives": ['https://yourhost.com/myEnv.zip#tmp/mydir'] The #tmp/mydir suffix indicates where you want to unzip your files, relative to the working directory.
    5. In your UDF, before trying to import libraries, add your directory to the Python path: sys.path.insert(0, 'tmp/mydir')
    6. Now your libraries should be loaded before anything else!

    Known limitations:

    • Your dependencies need to be compatible with the Python version of the backend, currently 3.8.
    • Your dependencies need to be compatible with the OS of the backend, currently AlmaLinux 8.
    • The backend has a limited set of Python dependences that are preloaded, and cannot be changed, such as numpy.

    # Learning more

    The topic of resource optimization is a complex one, and here we just give a short summary. The goal of openEO is to hide most of these details from the user, but we realize that advanced users sometimes want to have a bit more insight, so in the spirit of being open, we give some hints.

    To learn more about these options, we point to the piece of code that handles this:

    https://github.com/Open-EO/openeo-geopyspark-driver/blob/faf5d5364a82e870e42efd2a8aee9742f305da9f/openeogeotrellis/backend.py#L1213

    Most memory related options are translated to Apache Spark configuration settings, which are documented here:

    https://spark.apache.org/docs/3.3.1/configuration.html#application-properties

    # Batch job results on Sentinel Hub

    If you are processing data and the underlying back-end is Sentinel Hub, the output extent of your batch job results is currently larger than your input extent because Sentinel Hub processes whole tiles (this may change in the future and the data will be cropped to your input extent).

    Edit this page (opens new window)
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    - + diff --git a/file-formats/index.html b/file-formats/index.html index ebaeaa490..98152fa8f 100644 --- a/file-formats/index.html +++ b/file-formats/index.html @@ -8,7 +8,7 @@ - + @@ -145,6 +145,6 @@
    Contact (opens new window)

    Loading data...

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    - + diff --git a/getting-started/client-side-processing/index.html b/getting-started/client-side-processing/index.html index f699ec9c4..743560441 100644 --- a/getting-started/client-side-processing/index.html +++ b/getting-started/client-side-processing/index.html @@ -8,7 +8,7 @@ - + @@ -245,6 +245,6 @@ 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:

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    - + diff --git a/getting-started/editor/index.html b/getting-started/editor/index.html index 471749af0..66c17d9b5 100644 --- a/getting-started/editor/index.html +++ b/getting-started/editor/index.html @@ -8,7 +8,7 @@ - + @@ -145,6 +145,6 @@
    Contact (opens new window)

    • Get started with the openEO Platform Editor

    # Get started with the openEO Platform Editor

    Note

    You need to get an openEO Platform account (opens new window) to access the processing infrastructure.

    The openEO Platform Editor (also called Web Editor) is a browser-based graphical user interface for openEO Platform. It allows to use the openEO Platform services without any coding experience. You can explore the service offerings such as data collections and processes, but also create and run custom processes on our infrastructure and then visualize the results. Result visualization is still a bit limited, but all other features of the Platform are supported.

    The Editor is available at https://editor.openeo.cloud (opens new window) and loads up in "Discovery mode" by default, which means you can explore the service offerings without being logged in. On the left side you can find the service offerings like data collections and processes and on the right side the process editor is shown.

    To enable more functionality, e.g. to compute something in a batch job, you have to login. Hover over the button with the text "Guest" in the top right corner and it will show you a "Login" button. Once you clicked on it, the login screen shows up. Here you can simply click the "Log in with EGI Check-in" button and the login procedure will start. See the chapters on the Free Tier or the available plans (opens new window) for more details on the procedure to register and log in.

    After you've completed this the login procedure, the Editor shows up again and you'll notice that a new area in the lower middle part of the Editor aprears. This is the user workspace, where you can see all your stored data, e.g. batch jobs or uploaded files.

    If you need any more help you can always click the "Help" button in the top right area of the Editor and you'll start a guided tour through the Editor. If there are any additional questions, please contact us.

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    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/getting-started/javascript/index.html b/getting-started/javascript/index.html index 98ccc37d8..8752b9152 100644 --- a/getting-started/javascript/index.html +++ b/getting-started/javascript/index.html @@ -8,7 +8,7 @@ - + @@ -246,6 +246,6 @@

    There's also the method downloadResults to download the results directly. Unfortunately, you can only download files from a Node.js environment where file access to your local drive is possible. In a Browser environment, it is also an option to download the STAC Item or Collection for the results using the getResultsAsStac method and point a STAC client (opens new window) to it for downloading.

    # Additional Information

    Edit this page (opens new window)
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    - + diff --git a/getting-started/jupyterlab/index.html b/getting-started/jupyterlab/index.html index 1d9784c7d..a04779e4b 100644 --- a/getting-started/jupyterlab/index.html +++ b/getting-started/jupyterlab/index.html @@ -8,7 +8,7 @@ - + @@ -146,6 +146,6 @@ Contact (opens new window)

    • Get started with openEO Platform in JupyterLab (Python)

    # Get started with openEO Platform in JupyterLab (Python)

    Important

    You need to get an openEO Platform account (opens new window) to access the processing infrastructure.

    A hosted JupyterLab environment for openEO Platform is available at lab.openeo.cloud (opens new window).

    It has the openEO Python client pre-installed, but it does not support running the R or JavaScript clients.

    You need to authenticate before you can use it:

    1. Select from the "Sign in" dropdown menu the "openEO Platform" option
    2. It will start the EGI Authentication workflow for openEO Platform. If you haven't you need to get an openEO Platform account (opens new window) before you proceed.
    3. After you have logged in via EGI, the "Server Options" appear and you are requested to "Select your desired stack". Please choose "openEO Platform Lab" and click "Start".
    4. You are logged in, now. The JupyterLab should be usable like a normal JupyterLab instance that has the openEO Python client and some other tools pre-installed.
    5. You can now open a new Python 3 Notebook and, for example, start to follow the general Python Getting Started Guide.

      Note

      You can skip the "Installation" section in the Getting Started Guide, but unfortunately you need to authenticate with the Python client again! We'll try to remove this annoyance in the future.

    Note

    You may shut down your device or log out during the job runs on the backend. You can retrieve the status and results later and from any client.

    If you require any additional packages to be installed into your JupyterLab environment please refrain from installing them via pip and install them via conda. Anaconda documentation (opens new window)

    Please also refer to the the official documentation for the openEO Python Client (opens new window) and JupyterLab (opens new window) for more details.

    Edit this page (opens new window)
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    - + diff --git a/getting-started/python/index.html b/getting-started/python/index.html index 789cf27b1..c59d00e82 100644 --- a/getting-started/python/index.html +++ b/getting-started/python/index.html @@ -8,7 +8,7 @@ - + @@ -253,6 +253,6 @@

    When everything completes successfully, the processing result will be downloaded as a GeoTIFF file in a folder "output".

    TIP

    You may shut down your device or log out during the job runs on the backend. You can retrieve the status and results later and from any client.

    The official openEO Python Client documentation has more information on batch job management and downloading results (opens new window)

    # Additional Information

    Additional information and resources about the openEO Python Client Library:

    Edit this page (opens new window)
    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/getting-started/python/shiny.html b/getting-started/python/shiny.html index d77d97aee..2aa0efe56 100644 --- a/getting-started/python/shiny.html +++ b/getting-started/python/shiny.html @@ -8,7 +8,7 @@ - + @@ -228,6 +228,6 @@ return fig

    As in a dashboard, one will probably work with rendering plots mainly, that should be resourceful enough to let anyone start playing with openEO and Shiny in python together. If there are any doubts, do not hesitate to reach the developers and consider even creating an issue in this repository. Please be aware of openEO backend related issues that do not concern this dashboard developers.

    Edit this page (opens new window)
    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/getting-started/r/index.html b/getting-started/r/index.html index dc014c93f..f26ef2aae 100644 --- a/getting-started/r/index.html +++ b/getting-started/r/index.html @@ -8,7 +8,7 @@ - + @@ -230,6 +230,6 @@ # download all the files into a folder on the file system download_results(job = job, folder = "/some/folder/on/filesystem")

    Note

    The printing behavior and the actual data structure might differ!

    Edit this page (opens new window)
    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/index.html b/index.html index f6fbeb080..60fca2e5c 100644 --- a/index.html +++ b/index.html @@ -8,7 +8,7 @@ - + @@ -164,6 +164,6 @@

    You can also go back to the project website at openeo.cloud to find the less technical details.

    - + diff --git a/join/early_adopter.html b/join/early_adopter.html index 2a60cbee6..56131b68b 100644 --- a/join/early_adopter.html +++ b/join/early_adopter.html @@ -8,7 +8,7 @@ - + @@ -181,6 +181,6 @@ https://openeo.cloud and then authenticate through EGI Check-in with the account used above.

    Tip

    For your own convenience, we advise you to always log in with the same identity provider you originally registered with. Otherwise, you run the risk of creating a separate new EGI account, which in turn will have to go through the openEO Platform virtual organization acceptance process again. It is possible to link multiple accounts from multiple identity providers to the same EGI account. However, this must be done before you use these accounts to log in, as explained in the EGI documentation (opens new window).

    See the getting started guides to find out more about how to use the clients for this:

    Edit this page (opens new window)
    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/join/free_trial.html b/join/free_trial.html index 2a60cbee6..56131b68b 100644 --- a/join/free_trial.html +++ b/join/free_trial.html @@ -8,7 +8,7 @@ - + @@ -181,6 +181,6 @@ https://openeo.cloud and then authenticate through EGI Check-in with the account used above.

    Tip

    For your own convenience, we advise you to always log in with the same identity provider you originally registered with. Otherwise, you run the risk of creating a separate new EGI account, which in turn will have to go through the openEO Platform virtual organization acceptance process again. It is possible to link multiple accounts from multiple identity providers to the same EGI account. However, this must be done before you use these accounts to log in, as explained in the EGI documentation (opens new window).

    See the getting started guides to find out more about how to use the clients for this:

    Edit this page (opens new window)
    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/processes/index.html b/processes/index.html index 1d1bd7983..9d3893da3 100644 --- a/processes/index.html +++ b/processes/index.html @@ -8,7 +8,7 @@ - + @@ -145,6 +145,6 @@
    Contact (opens new window)

    - + diff --git a/usecases/ard/index.html b/usecases/ard/index.html index d47f2516a..5a0e860d7 100644 --- a/usecases/ard/index.html +++ b/usecases/ard/index.html @@ -8,7 +8,7 @@ - + @@ -150,6 +150,6 @@ There are also variants with a default parametrization that results in data that is compliant with CEOS CARD4L specifications (opens new window).

    We should note that these operations can be computationally expensive, so certainly affect overall processing time and cost of your final algorithm. Hence, make sure to make an informed decision when you decide to use these methods.

    Examples:

    Edit this page (opens new window)
    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/usecases/ard/msi/index.html b/usecases/ard/msi/index.html index e95e02981..2ddbbb210 100644 --- a/usecases/ard/msi/index.html +++ b/usecases/ard/msi/index.html @@ -8,7 +8,7 @@ - + @@ -216,6 +216,6 @@ var result = builder.save_result(datacube, 'GTiff'); var job = await connection.createJob(result, 'Atmospherically corrected Sentinel-2 MSI');
    Edit this page (opens new window)
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    - + diff --git a/usecases/ard/sar/index.html b/usecases/ard/sar/index.html index 517717e38..828952a67 100644 --- a/usecases/ard/sar/index.html +++ b/usecases/ard/sar/index.html @@ -8,7 +8,7 @@ - + @@ -191,6 +191,6 @@ var result = builder.save_result(datacube, 'GTiff'); var job = await connection.createJob(result, 'Backscatter for Sentinel-1');

    # Orfeo for other GRD collections (provided by VITO / TerraScope)

    When working with other GRD data, an implementation (opens new window) based on Orfeo Toolbox (opens new window) is used.

    The Orfeo implementation currently only supports sigma0 computation, and is not CARD4L compliant.

    Edit this page (opens new window)
    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/usecases/crop-classification/index.html b/usecases/crop-classification/index.html index 3634b9365..1cfe117f1 100644 --- a/usecases/crop-classification/index.html +++ b/usecases/crop-classification/index.html @@ -8,7 +8,7 @@ - + @@ -251,6 +251,6 @@ clf_results = features.apply_dimension(code=udf_rf, runtime="Python", dimension="bands").rename_labels("bands",["pixel"])

    Note that if your labels are strings, you will have to map them to integers. You can then download the classification results and plot it. Congratulations!

    To see a fully working example, you can check out this Python notebook on rule-based classification (opens new window) or this Python notebook on classification using Random Forest (opens new window).

    We ran the code in that notebook for ~120 MGRS tiles to end up with a crop cover map for 5 countries in Europe, which looks like this:

    cropcover_5_countries

    Edit this page (opens new window)
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    - + diff --git a/usecases/forest-change-detection/index.html b/usecases/forest-change-detection/index.html index 157405ca1..6728cb3bb 100644 --- a/usecases/forest-change-detection/index.html +++ b/usecases/forest-change-detection/index.html @@ -8,7 +8,7 @@ - + @@ -175,6 +175,6 @@

    The difference between the training data and the predicted values following the seasonal model is a key information, which is used to perform the change detection with new data. Please have a look at the reference notebook (opens new window) for the complete pipeline.

    Edit this page (opens new window)
    Last Updated: 10/24/2023, 1:57:17 PM
    - + diff --git a/usecases/landcover/index.html b/usecases/landcover/index.html index e72772fc4..518ed4bef 100644 --- a/usecases/landcover/index.html +++ b/usecases/landcover/index.html @@ -8,7 +8,7 @@ - + @@ -195,6 +195,6 @@ inf_job = predicted.execute_batch(out_format="GTiff") inf_job.get_results().download_files(str(base_path / "prediction"))

    tile31UFS

    Edit this page (opens new window)
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    - + diff --git a/usecases/large-scale-processing/index.html b/usecases/large-scale-processing/index.html index 0bd13344d..999e235ab 100644 --- a/usecases/large-scale-processing/index.html +++ b/usecases/large-scale-processing/index.html @@ -8,7 +8,7 @@ - + @@ -174,6 +174,6 @@ your backend of choice. For instance, in a cloud environment with 16GB per machine and 4 cpu's, using slightly less than 4GB per worker is efficient as you can fit 4 parallel workers on a single VM, while requiring 6GB would fit only 2 workers and leave about 4GB unused.

    In our example, we used the Geotrellis backends, which has these execution options.

    # Starting map production

    The openEO Python client provides a useful tool to run multiple processing jobs in multiple backends:

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

    This class takes a GeoJSON corresponding to your tile grid and job properties per tiles, and triggers a function provided by you whenever a new job needs to be created. You can configure multiple backends, and set the number of parallel jobs per backend.

    This class also takes care of error handling, and can be considered more resilient compared to writing a simple loop yourself.

    A full example of how we use this can be found here (opens new window).

    This script uses a CSV file to track your jobs, and whenever it is interrupted it can simply resume from that CSV file, making it tolerant to failure.

    Tracking jobs by CSV

    # Errors during production

    It is expected to see jobs failing during production, which can be considered normal as long as the failure rate is not too high. We advice to quickly inspect error logs, and if no obvious reason for failure is found, a simple retry might be sufficient. In other cases it may be needed to increase memory. We also see a limited number of cases where for instance issues in the underlying product archive cause failures or artifacts. These are harder to resolve, and may require interaction with the backend to resove!

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    - + diff --git a/usecases/no2-monitoring/index.html b/usecases/no2-monitoring/index.html index 3f581a57d..0dedd1540 100644 --- a/usecases/no2-monitoring/index.html +++ b/usecases/no2-monitoring/index.html @@ -8,7 +8,7 @@ - + @@ -310,6 +310,6 @@

    # Result

    If you'd visualize the results of running the timeseries analysis for mean, min and max could results in such a chart:

    min/max/mean NO2 chart

    Edit this page (opens new window)
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