items_metadata.yaml

samples:
- title: Analyzing New York City taxi data using big data tools
  url: https://www.arcgis.com/home/item.html?id=27017ef3b3864e74ae1b7587719a3391
  path: ./samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb
  thumbnail: ./static/thumbnails/analyze_new_york_city_taxi_data.png
  snippet: Use big data tools to analye NYC taxi data
  description: This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Taxi"]
- title: Data Visualization - Construction permits, part 1/2
  url: https://www.arcgis.com/home/item.html?id=467bc6806c9e40dc8222744e0937b80c
  path: ./samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part1.ipynb
  thumbnail: ./static/thumbnails/analyze_patterns_in_construction_permits_part1.jpg
  snippet: Observe spatial and temporal growth trends in construction permits
  description: In this notebook, you'll explore Montgomery County permit data. First, you'll add the permit data from ArcGIS Living Atlas of the World. You'll explore the data and become familiar with exactly what kind of information it contains. Then, you'll analyze the data to detect patterns and find out why growth is occurring. Once you've gathered your findings from your exploration and analysis, you'll share your work online.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Construction", "Permits"]
- title: Data Summarization - Construction permits, part 2/2
  url: https://www.arcgis.com/home/item.html?id=0d980b0273b14908bcd5b159757e93e1
  path: ./samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part2.ipynb
  thumbnail: ./static/thumbnails/analyze_patterns_in_construction_permits_part2.png
  snippet: Run spatial analysis tools to predict permit spikes
  description: In this lesson, we'll move beyond exploration and run spatial analysis tools to answer specific questions that can't be answered by the data itself. In particular, we want to know why permits spiked in Germantown in 2011 and predict where future permit spikes - and, by extension, future growth - are likely to occur.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Construction", "Permits", "Featured"]
- title: Analyzing United States tornadoes
  url: https://www.arcgis.com/home/item.html?id=ab5d87bffc684f9088c84d2120782e28
  path: ./samples/04_gis_analysts_data_scientists/analyze_us_tornadoes.ipynb
  thumbnail: ./static/thumbnails/analyze_us_tornadoes.jpg
  snippet: Analyze tornadoes in the USA
  description: In this notebook, we demonstrate how to use aggregation analysis to summarize the number of data points within each polygon
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Tornadoes", "USA"]
- title: Analyzing the factors of growth and spatial distribution of Airbnb properties across New York City
  url: https://www.arcgis.com/home/item.html?id=acc8b4e5e0d5422d8af19166c1fc21d5
  path: ./samples/04_gis_analysts_data_scientists/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.ipynb
  thumbnail: ./static/thumbnails/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.png
  snippet: Analyze growth factors of Arbnb properties in New York
  description: A study is carried out in this sample notebook to understand the factors that are fuelling widespread growth in the number of Airbnb listings
  licenseInfo: ""
  tags: ["Data Science", "GIS", "airbnb"]
- title: Analyzing violent crime
  url: https://www.arcgis.com/home/item.html?id=f19cbf3595de4bf898e7228a46b79ffd
  path: ./samples/04_gis_analysts_data_scientists/analyzing_violent_crime.ipynb
  thumbnail: ./static/thumbnails/analyzing_violent_crime.png
  snippet: Analyze violent crime in Chicago
  description: Through this sample, we will demonstrate the utility of a number of spatial analysis methods including hot spot analysis, feature overlay, data enrichment and spatial selection using ArGIS API for Python.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Violent Crime", "Featured"]
- title: Automate Building Footprint Extraction using Deep learning
  url: https://www.arcgis.com/home/item.html?id=342919a470044ddaac8a299820a51204
  path: ./samples/04_gis_analysts_data_scientists/automate_building_footprint_extraction_using_instance_segmentation.ipynb
  thumbnail: ./static/thumbnails/automate_building_footprint_extraction_using_instance_segmentation.png
  snippet: Use deep learning to automate building footprints
  description: This sample shows how ArcGIS API for Python can be used to train a deep learning model to extract building footprints using satellite images. The trained model can be deployed on ArcGIS Pro or ArcGIS Enterprise to extract building footprints.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Building", "Footprint", "Instance", "Segmentation", "Deep Learning"]
- title: Automate Road Surface Investigation Using Deep Learning
  url: https://www.arcgis.com/home/item.html?id=72b6c19f5944424f830b44346f0fef89
  path: ./samples/04_gis_analysts_data_scientists/automate_road_surface_investigation_using_deep_learning.ipynb
  thumbnail: ./static/thumbnails/automate_road_surface_investigation_using_deep_learning.gif
  snippet: Use deep learning to detect adn classify road cracks
  description: In this notebook, we use a great labeled dataset of asphalt distress images in order to train our model to detect as well as to classify type of road cracks.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Road", "Cracks", "Deep Learning"]
- title: Reconstructing 3D buildings from Aerial LiDAR with Deep Learning
  url: https://www.arcgis.com/home/item.html?id=3b3f9bacf88c4661911cb8c0e1a95757
  path: ./samples/04_gis_analysts_data_scientists/building_reconstruction_using_mask_rcnn.ipynb
  thumbnail: ./static/thumbnails/building_reconstruction_using_mask_rcnn.png
  snippet: Reconstruct 3D building models from aerial LiDAR
  description: In this notebook, we demonstrate how to detect instances of roof segments of various types using instance segmentation to make the process more efficient
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Building", "Reconstruction", "MaskRCNN", "Deep Learning"]
# - title: Calculate Impervious Surfaces from Spectral Imagery
#   url: https://www.arcgis.com/home/item.html?id=bfbc2594a96f48539b1a1b30996fa76a
#   path: ./samples/04_gis_analysts_data_scientists/calculate_impervious_surfaces_from_spectral_imagery.ipynb
#   thumbnail: ./static/thumbnails/calculate_impervious_surfaces_from_spectral_imagery.png
#   snippet: Detect impervious surfaces
#   description: In this notebook, we’ll use a high resolution land cover map obtained from Chesapeake Conservancy to determine which parts of the ground are pervious and impervious.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Impervious Surfaces", "Deep Learning"]
# - title: Raster Analytics - Calculate wildfire landslide risk
#   url: https://www.arcgis.com/home/item.html?id=0d5367ba88754937becabeae4d8c520b
#   path: ./samples/04_gis_analysts_data_scientists/calculate_post_fire_landslide_risk.ipynb
#   thumbnail: ./static/thumbnails/calculate_post_fire_landslide_risk.jpg
#   snippet: Assess landslide vulnerability using raster analytics
#   description: In this notebook, we will provide local emergency management teams a summary of post-wildfire landslide risk, so officials can target mitigation efforts to the most vulnerable watershed basins.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Landslide", "Post Fire"]
# - title: Using weighted overlay analysis to identify areas that are natural and accessible
#   url: https://www.arcgis.com/home/item.html?id=d83bc28a865b4ac281ceeca02c5288f7
#   path: ./samples/04_gis_analysts_data_scientists/calculating_cost_surfaces_using_weighted_overlay_analysis.ipynb
#   thumbnail: ./static/thumbnails/calculating_cost_surfaces_using_weighted_overlay_analysis.jpg
#   snippet: Identify accessible and natural areas
#   description: his sample identifies areas in the State of Washington that are more "natural" and easy to get to
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Weighted", "Overlay"]
# - title: Calculating Origin Destinations nXn Matrix given set of origins and destinations
#   url: https://www.arcgis.com/home/item.html?id=94c7f47fd15745f785618d4e7caa591e
#   path: ./samples/04_gis_analysts_data_scientists/calculating_nXn_od_cost_matrix.ipynb
#   thumbnail: ./static/thumbnails/calculating_nXn_od_cost_matrix.jpg
#   snippet: Create origin-destination pairs
#   description: In this sample notebook , we will use this tool to get OD matrix if given a set of origin and destination points, either as a csv with latitude and longitude or csv file with list of addresses. In later part of this sample, we will format the table to get n by n matrix.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Cost Matrix", "Network Analysis"]
# - title: California wildfires 2017 - Thomas Fire analysis
#   url: https://www.arcgis.com/home/item.html?id=decad31cff114ae0b5881778cfdb6d84
#   path: ./samples/04_gis_analysts_data_scientists/california_wildfires_2017_thomas_fire_analysis.ipynb
#   thumbnail: ./static/thumbnails/california_wildfires_2017_thomas_fire_analysis.png
#   snippet: Analyze the December 2017 thomas fire
#   description: The Thomas Fire was a massive wildfire that started in early December 2017 in Ventura and Santa Barbara counties and grew into California's largest fire ever.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Thomas Fire", "California"]
- title: Chennai Floods 2015 - A Geographic Analysis
  url: https://www.arcgis.com/home/item.html?id=44c4fc1e56654768840a03971feb1e77
  path: ./samples/04_gis_analysts_data_scientists/chennai_floods_analysis.ipynb
  thumbnail: ./static/thumbnails/chennai_floods_analysis.jpg
  snippet: Analyze the rainfall in Chennai, India
  description: This sample showcases not just the analysis and visualization capabilities of your GIS, but also the ability to store illustrative text, graphics and live code in a Jupyter notebook.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Floods", "Chennai"]
# - title: Constructing drive time based service areas
#   url: https://www.arcgis.com/home/item.html?id=500bd10ce9d241c1b0769d7e954e0d67
#   path: ./samples/04_gis_analysts_data_scientists/constructing_drive_time_based_service_areas.ipynb
#   thumbnail: ./static/thumbnails/constructing_drive_time_based_service_areas.jpg
#   snippet: Use the `network` module to construct drive time based service areas
#   description: This sample shows how the `network` module of the ArcGIS API for Python can be used to construct service areas. In this sample, we generate service areas for two of the fire stations in central Tokyo, Japan.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Drive Time"]
# - title: Counting features in satellite images using scikit-image
#   url: https://www.arcgis.com/home/item.html?id=4398498b6ff048c09c4b0ed90f84a1e6
#   path: ./samples/04_gis_analysts_data_scientists/counting_features_in_satellite_images_using_scikit_image.ipynb
#   thumbnail: ./static/thumbnails/counting_features_in_satellite_images_using_scikit_image.jpg
#   snippet: Use scikit-image to count features
#   description: The example below uses scikit-image library to detect circular features in farms using center pivot irrigation in Saudi Arabia.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Scikit Image", "Features"]
# - title: Mapping the 2019 Novel Coronavirus Pandemic
#   url: https://www.arcgis.com/home/item.html?id=1584e1ce508f460c9d40360adfb3e022
#   path: ./samples/04_gis_analysts_data_scientists/covid19_part1_mapping_the_pandemic.ipynb
#   thumbnail: ./static/thumbnails/covid19_part2_timeseries_analysis.png
#   snippet: This notebook is to perform analysis and time series charting of 2019 novel coronavirus disease (COVID-19) globally
#   description: This notebook is to perform analysis and time series charting of 2019 novel coronavirus disease (COVID-19) globally
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Covid19", "Time series", "Part 2"]
# - title: Time Series Analysis of the 2019 Novel Coronavirus Pandemic
#   url: https://www.arcgis.com/home/item.html?id=1584e1ce508f460c9d40360adfb3e022
#   path: ./samples/04_gis_analysts_data_scientists/covid19_part2_timeseries_analysis.ipynb
#   thumbnail: ./static/thumbnails/covid19_part2_timeseries_analysis.png
#   snippet: This notebook is to perform analysis and time series charting of 2019 novel coronavirus disease (COVID-19) globally
#   description: This notebook is to perform analysis and time series charting of 2019 novel coronavirus disease (COVID-19) globally
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Covid19", "Time series", "Part 2"]
# - title: Predictive Analysis of the 2019 Novel Coronavirus Pandemic
#   url: https://www.arcgis.com/home/item.html?id=36e563f54cd446378d140cd0ceb8125c
#   path: ./samples/04_gis_analysts_data_scientists/covid19_part3_predictive_analysis.ipynb
#   thumbnail: ./static/thumbnails/covid19_part3_predictive_analysis.jpg
#   snippet: Use tools to analyze COVID-19
#   description: This notebook provides you with tools and methods that you can try yourself in performing data modeling, analyzing, and predicting the spread of COVID-19 with the ArcGIS API for Python, and other libraries such as pandas and numpy
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Predictive", "Covid19", "Part 3"]
- title: Creating hurricane tracks using Geoanalytics
  url: https://www.arcgis.com/home/item.html?id=c6106b0ead3f49059b326646eda85f9a
  path: ./samples/04_gis_analysts_data_scientists/creating_hurricane_tracks_using_geoanalytics.ipynb
  thumbnail: ./static/thumbnails/creating_hurricane_tracks_using_geoanalytics.png
  snippet: Use GeoAnalytics to create hurricane tracks
  description: The sample code below uses big data analytics (GeoAnalytics) to reconstruct hurricane tracks using data registered on a big data file share in the GIS
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Hurricane", "Tracks", "GeoAnalytics"]
# - title: Creating Raster Information Product using Raster Analytics
#   url: https://www.arcgis.com/home/item.html?id=f0423a7df2064096a78e150a6fbf5ae4
#   path: ./samples/04_gis_analysts_data_scientists/creating_raster_information_product_using_raster_analytics.ipynb
#   thumbnail: ./static/thumbnails/creating_raster_information_product_using_raster_analytics.gif
#   snippet: This sample show the capabilities of imagery layers and raster analytics.
#   description: This sample show the capabilities of imagery layers and raster analytics.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Raster Analytics", "Product"]
- title: Crime analysis and clustering using geoanalytics and pyspark.ml
  url: https://www.arcgis.com/home/item.html?id=1410a28d3a8d4d2aa353efcf9b606b69
  path: ./samples/04_gis_analysts_data_scientists/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.ipynb
  thumbnail: ./static/thumbnails/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.png
  snippet: Analyze crime in Chicago
  description: Through this sample, we will demonstrate the utility of a number of geoanalytics tools including find_hot_spots, aggregate_points and calculate_density to visually understand geographical patterns.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Crime", "Clustering", "GeoAnalytics", "PySpark"]
- title: Designate Bike Routes for Commuting Professionals
  url: https://www.arcgis.com/home/item.html?id=62b874f4e705448a95abac0240f3053d
  path: ./samples/04_gis_analysts_data_scientists/designate_bike_routes_for_commuting_professionals.ipynb
  thumbnail: ./static/thumbnails/designate_bike_routes_for_commuting_professionals.png
  snippet: Analyze biking streets in Seattle, WA
  description: This sample uses ArcGIS API for Python to analyze and select streets for making bike routes for people commuting to and from work in the City of Seattle, Washington.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Bike", "Routes", "Commute"]
- title: Detecting and Categorizing Brick Kilns from Satellite Imagery
  url: https://www.arcgis.com/home/item.html?id=59a916aaa9114354a89e07a3941f9c98
  path: ./samples/04_gis_analysts_data_scientists/detecting_and_categorizing_brick_kilns_from_satellite_imagery.ipynb
  thumbnail: ./static/thumbnails/detecting_and_categorizing_brick_kilns_from_satellite_imagery.png
  snippet: Categorize Brick Kilns using the Python API
  description: In this sample, we will use Deep Learning on ArcGIS Platform to detect the location and design category of all brick kilns around Delhi NCR area in India to find the brick kilns which are not following the directions from CPCB.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Brick Kilns", "Categorization", "Deep Learning"]
# - title: Detecting settlements using supervised classification and deep learning
#   url: https://www.arcgis.com/home/item.html?id=4a4828703f1a494e89ec973086cad715
#   path: ./samples/04_gis_analysts_data_scientists/detecting_settlements_using_supervised_classification_and_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/detecting_settlements_using_supervised_classification_and_deep_learning.png
#   snippet: Use deep learning to detect settlements
#   description: In this notebook we have attempted to use the supervised classification approach to generate the required volumes of data which after cleaning was used to come through the requirement of larger training data for Deep Learning model.
#   licenseInfo: ""
#   runtime: advanced_gpu
#   tags: ["Data Science", "GIS", "Settlement", "Deep Learning", "Supervised Classification"]
# - title: Detecting Swimming Pools using Satellite Imagery and Deep Learning
#   url: https://www.arcgis.com/home/item.html?id=122fb4b5a2094f3398b1d96381022f64
#   path: ./samples/04_gis_analysts_data_scientists/detecting_swimming_pools_using_satellite_image_and_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/detecting_swimming_pools_using_satellite_image_and_deep_learning.jpg
#   snippet: Detect swimming pools using remote sensing imagery
#   description: This notebook demonstrates an end-to-end deep learning workflow in using ArcGIS API for Python. The workflow consists of three major steps. (1) extracting training data, (2) train a deep learning object detection model, (3) deploy the model for inference and create maps.
#   licenseInfo: ""
#   runtime: advanced_gpu
#   tags: ["Data Science", "GIS", "Swimming", "Deep Learning", "Pools"]
# - title: Detecting Super Blooms Using Satellite Image Classification
#   url: https://www.arcgis.com/home/item.html?id=50d6c2001e864d44ab5278e7b439bf41
#   path: ./samples/04_gis_analysts_data_scientists/detect_super_blooms_using_satellite_image_classification.ipynb
#   thumbnail: ./static/thumbnails/detect_super_blooms_using_satellite_image_classification.jpg
#   snippet: Determine the occurance of super blooms in the study area for a given year
#   description: This sample is to study three poppy fields where people often go for watching super blooms, compare the sites with historic scenes, capture the differences in vegetation conditions, and calculate the vegetation density of blooms.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Super Blooms", "Classification"]
# - title: Drive time analysis for opioid epidemic
#   url: https://www.arcgis.com/home/item.html?id=c109ab8bb83e42819ffab65d71abb34c
#   path: ./samples/04_gis_analysts_data_scientists/drive_time_analysis_for_opioid_epidemic.ipynb
#   thumbnail: ./static/thumbnails/drive_time_analysis_for_opioid_epidemic.jpg
#   snippet: Perform drive time analysis for the opioid epidemic
#   description: This notebook performs drive time analysis for clinics of opioid epidemic treatment and/or prevention centers in the county of Oakland, Michigan.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Opioid Epidemic", "Drive Time"]
- title: Extracting Building Footprints From Drone Data
  url: https://www.arcgis.com/home/item.html?id=802ccd7dc3a748eeafb20b05d2a8f67c
  path: ./samples/04_gis_analysts_data_scientists/extracting_building_footprints_from_drone_data.ipynb
  thumbnail: ./static/thumbnails/extracting_building_footprints_from_drone_data.png
  snippet: Extract Building footprints from drone data
  description: This sample shows how ArcGIS API for Python can be used to train a deep learning model to extract building footprints from drone data.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Building", "Foorprint", "Deep Learning"]
- title: Extracting Slums from Satellite Imagery
  url: https://www.arcgis.com/home/item.html?id=5b5461f3df814fc1b65539365668904d
  path: ./samples/04_gis_analysts_data_scientists/extracting_slums_from_satellite_imagery.ipynb
  thumbnail: ./static/thumbnails/extracting_slums_from_satellite_imagery.png
  snippet:  Extract slum boundaries from satellite imagery
  description: This sample shows how we can extract the slum boundaries from satellite imagery using the learn module in ArcGIS API for Python.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Deep Learning", "Slums", "Extracting"]
# - title: Feature Categorization using Satellite Imagery and Deep Learning
#   url: https://www.arcgis.com/home/item.html?id=c5de157941034407bc719c5bde8e1ea7
#   path: ./samples/04_gis_analysts_data_scientists/feature_categorization_using_satellite_imagery_and_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/feature_categorization_using_satellite_imagery_and_deep_learning.jpg
#   snippet: Use deep learning to perform feature categorization
#   description: This sample notebook demonstrates the use of deep learning capabilities in ArcGIS to perform feature categorization.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Feature", "Categorization", "Deep Learning"]
- title: Fighting California forest fires using spatial analysis
  url: https://www.arcgis.com/home/item.html?id=6bd7735c79024e1687d342b66a1b313c
  path: ./samples/04_gis_analysts_data_scientists/fighting_california_forest_fires_using_spatial_analysis.ipynb
  thumbnail: ./static/thumbnails/fighting_california_forest_fires_using_spatial_analysis.png
  snippet: This sample demonstrates the application of spatial analysis tools such as buffer and overlay.
  description: This notebook describes a scenario wherein an analyst automates the process of identifying facilities at risk from forest fires and sharing this information with other departments such as the fire department, etc.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "California", "Forest Fires"]
- title: Finding a New Home
  url: https://www.arcgis.com/home/item.html?id=a4518082dbe14885b45680ee54c74aed
  path: ./samples/04_gis_analysts_data_scientists/finding_a_new_home.ipynb
  thumbnail: ./static/thumbnails/finding_a_new_home.png
  snippet: Study the housing market for the goal of finding a new home
  description: In this case study, we will explore the current housing market, estimate average house prices in their area and hunt for a new one.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Home"]
- title: Monitoring hydrologic water quality in pasturelands through spatial overlay analysis
  url: https://www.arcgis.com/home/item.html?id=aeb9f2a680eb451b9186dfb68353143d
  path: ./samples/04_gis_analysts_data_scientists/finding_grazing_allotments.ipynb
  thumbnail: ./static/thumbnails/finding_grazing_allotments.png
  snippet: Monitor watersheds using spatial overlay analysis
  description: This sample uses ArcGIS API for Python to find out which watershed, or watersheds, each grazing allotment falls in, for water quality monitoring.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Grazing", "Allotments", "Featured"]
- title: Find hospitals closest to an incident
  url: https://www.arcgis.com/home/item.html?id=9056733512624eeda8eb1b32625d518b
  path: ./samples/04_gis_analysts_data_scientists/finding_hospitals_closest_to_an_incident.ipynb
  thumbnail: ./static/thumbnails/finding_hospitals_closest_to_an_incident.png
  snippet: Use the network module to find a hospital closest to an incident
  description: In this sample, we see how to find the hospital that is closest to an incident.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Hospitals", "Featured"]
# - title: Finding routes for appliance delivery with VRP solver
#   url: https://www.arcgis.com/home/item.html?id=4b1fcd1af9ea4f0f9dce8f71a729890b
#   path: ./samples/04_gis_analysts_data_scientists/finding_routes_for_appliance_delivery_with_VRP_solver.ipynb
#   thumbnail: ./static/thumbnails/finding_routes_for_appliance_delivery_with_VRP_solver.jpg
#   snippet: Find hospitals using the network module
#   description: In this sample, we see how to find the hospital that is closest to an incident using the `network` module.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Routes", "VRP Solver"]
# - title: Finding suitable spots for placing heart defibrillator equipments in public
#   url: https://www.arcgis.com/home/item.html?id=225ff27ffb504c88be4ff069d5d34b60
#   path: ./samples/04_gis_analysts_data_scientists/finding_suitable_spots_for_AED_devices_using_raster_analytics.ipynb
#   thumbnail: ./static/thumbnails/finding_suitable_spots_for_AED_devices_using_raster_analytics.png
#   snippet: Find suitable spots for AED devices
#   description: In this sample, we will observe how site suitability analyses can be performed using the ArcGIS API for Python.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "AED Devices", "Raster Analytics", "Featured"]
# - title: Historical Wildfire Analysis
#   url: https://www.arcgis.com/home/item.html?id=eb4eaad6661b45d689a6ec5b9852ac8d
#   path: ./samples/04_gis_analysts_data_scientists/historical_wildfire_analysis.ipynb
#   thumbnail: ./static/thumbnails/historical_wildfire_analysis.png
#   snippet: Analyze historical wildfire trends
#   description: Use the ArcGIS API for Python to answer if wildfires are increasing over time.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Wildfire"]
- title: How much green is Delhi as on 15 Oct 2017?
  url: https://www.arcgis.com/home/item.html?id=8094be16f34e46e48880883a1ae6a4f1
  path: ./samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-15-oct-2017.ipynb
  thumbnail: ./static/thumbnails/how-much-green-is-Delhi-as-on-15-oct-2017.jpg
  snippet: Use Landsat 8 imagery to detect green cover of New Delhi, India
  description: This sample shows the capabilities of spectral indices such as Normalized Difference Vegetation index (NDVI) for the calculation of green cover in Delhi, India on 15 October 2017 using Landsat 8 imagery.
  licenseInfo: ""
  tags: '["Data Science", "GIS"]'
# - title: Identifying suitable sites for new ALS clinics using location allocation analysis
#   url: https://www.arcgis.com/home/item.html?id=948ed526d07b481a9f401f643f02e97b
#   path: ./samples/04_gis_analysts_data_scientists/identifying-suitable-sites-for-als-clinics-using-location-allocation-analysis.ipynb
#   thumbnail: ./static/thumbnails/identifying-suitable-sites-for-als-clinics-using-location-allocation-analysis.png
#   snippet: Use network analysis to identify potential sites for new ALS clinics
#   description: This notebook demonstrates how ArcGIS can perform network analysis to identify potential sites for new ALS clinics in California to improve access for patients who do not live near a clinic.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS"]
- title: Increase Image Resolution using SuperResolution
  url: https://www.arcgis.com/home/item.html?id=f02fe8a68e5e444d8a15aaf0cd18cb65
  path: ./samples/04_gis_analysts_data_scientists/increase-image-resolution-using-superresolution.ipynb
  thumbnail: ./static/thumbnails/increase-image-resolution-using-superresolution.gif
  snippet: Increase image resolution using the ArcGIS API for Python
  description: This sample notebook demonstrates how the SuperResolution model in arcgis.learn module can be used to increase image resolution.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Super Resolution", "Deep learning"]
- title: Information extraction from Madison city crime incident reports using Deep Learning
  url: https://www.arcgis.com/home/item.html?id=c1e38321865b40d4b01ec5de17b27442
  path: ./samples/04_gis_analysts_data_scientists/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.ipynb
  thumbnail: ./static/thumbnails/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.png
  snippet: Extract info from crime reports
  description: In this notebook we will extract information from crime incident reports obtained from Madison police department [1]using arcgis.learn.EntityRecognizer().
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Information", "Extraction", "deep Learning", "Madison", "Crime", "Featured"]
- title: Land cover classification using sparse training data
  url: https://www.arcgis.com/home/item.html?id=b82a104720a349fe96d47f0f12ed86a8
  path: ./samples/04_gis_analysts_data_scientists/land_cover_classification_using_sparse_training_data.ipynb
  thumbnail: ./static/thumbnails/land_cover_classification_using_sparse_training_data.png
  snippet: Perform land cover classification
  description: This notebook showcases an approach to performing land cover classification using sparse training data and multispectral imagery.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Sparse", "Land cover", "Classification", "Deep Learning"]
# - title: Land Cover Classification using Satellite Imagery and Deep Learning
#   url: https://www.arcgis.com/home/item.html?id=8a3f6601f67049f1a311e88c7ba02125
#   path: ./samples/04_gis_analysts_data_scientists/land_cover_classification_using_unet.ipynb
#   thumbnail: ./static/thumbnails/land_cover_classification_using_unet.png
#   snippet: Use the ArcGIS API for Python for land cover classification
#   description: This notebook showcases an end-to-end to land cover classification workflow using ArcGIS API for Python.
#   licenseInfo: ""
#   runtime: advanced_gpu
#   tags: ["Data Science", "GIS", "Land Cover", "Classification", "Deep Learning"]
- title: Locating a new retirement community
  url: https://www.arcgis.com/home/item.html?id=95236a13179b40c39c9fc01ab96719e3
  path: ./samples/04_gis_analysts_data_scientists/locating_a_new_retirement_community.ipynb
  thumbnail: ./static/thumbnails/locating_a_new_retirement_community.png
  snippet: Locate new retirement communites
  description: This sample demonstrates the utility of ArcGIS API for Python to identify some great locations for a new retirement community, which will satisfy these needs of senior citizens.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Retirement", "Community", "Featured"]
# - title: Data Preparation - Hurricane analysis, part 1/3
#   url: https://www.arcgis.com/home/item.html?id=a93787d32d71458ba733c432f231be19
#   path: ./samples/04_gis_analysts_data_scientists/part1_prepare_hurricane_data.ipynb
#   thumbnail: ./static/thumbnails/part1_prepare_hurricane_data.jpg
#   snippet: Analyze meteorological data of hurricanes
#   description: In this notebook, we use meteorological data of hurricanes recorded in the past 169 years to analyze their location, intensity and investigate if there are any statistically significant trends.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Hurricane", "GeoAnalytics", "Part 1"]
# - title: Exploratory Statistics - Hurricane analysis, part 2/3
#   url: https://www.arcgis.com/home/item.html?id=55bc3720bb4f4d44a2326d771a3eab9b
#   path: ./samples/04_gis_analysts_data_scientists/part2_explore_hurricane_tracks.ipynb
#   thumbnail: ./static/thumbnails/part2_explore_hurricane_tracks.png
#   snippet: Analyze aggregate tracks of hurricanes
#   description: In this notebook you will analyze the aggregated tracks to investigate the communities that are most affected by hurricanes, as well as as answer important questions about the prevalance of hurricanes, their seasonality, their density, and places where they make landfall.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Hurricane", "Tracks", "GeoAnalytics", "Part 2"]
# - title: Correlation - Hurricane analysis, part 3/3
#   url: https://www.arcgis.com/home/item.html?id=5a34fa2a151b40659894c0b4f0d30704
#   path: ./samples/04_gis_analysts_data_scientists/part3_analyze_hurricane_tracks.ipynb
#   thumbnail: ./static/thumbnails/part3_analyze_hurricane_tracks.png
#   snippet: Analyze hurricane severity and trends over time
#   description: In this notebook you will analyze the aggregated tracks to answer important questions about hurricane severity and how they correlate over time.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Hurricane", "Tracks", "GeoAnalytics", "Part 3"]
- title: Predict Floods with Unit Hydrographs
  url: https://www.arcgis.com/home/item.html?id=2bbf431943304ddeba48d00d14f8c34f
  path: ./samples/04_gis_analysts_data_scientists/predict-floods-with-unit-hydrographs.ipynb
  thumbnail: ./static/thumbnails/predict-floods-with-unit-hydrographs.png
  snippet: Estimate stream runoff during a predicted rainstorm in Vermont.
  description: Estimate stream runoff during a predicted rainstorm in Vermont.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Raster", "Floods", "Prediction", "Hydrograph"]
# - title: Predicting El Niño–Southern Oscillation through correlation and time series analysis/deep learning
#   url: https://www.arcgis.com/home/item.html?id=69df9348e964433d86a5c0fb8aaa48de
#   path: ./samples/04_gis_analysts_data_scientists/predicting_enso.ipynb
#   thumbnail: ./static/thumbnails/predicting_enso.png
#   snippet: Predict ENSO from climate variables and indices
#   description: This example uses correlation analysis and time series analysis to predict El Niño–Southern Oscillation (ENSO) based on climate variables and indices.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Predict", "ENSO"]
# - title: Analyzing and predicting Service Request Types in DC
#   url: https://www.arcgis.com/home/item.html?id=08219e66481d4c809ce1bf46350f1995
#   path: ./samples/04_gis_analysts_data_scientists/predict_service_request_types.ipynb
#   thumbnail: ./static/thumbnails/predict_service_request_types.png
#   snippet: Predict service request types in DF
#   description: This notebook constructs models that predicts service types
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Service Request", "Predict"]
- title: Safe Streets to Schools
  url: https://www.arcgis.com/home/item.html?id=6e0aa56305284ed7b430432506b97a07
  path: ./samples/04_gis_analysts_data_scientists/safe_streets_to_schools.ipynb
  thumbnail: ./static/thumbnails/safe_streets_to_schools.png
  snippet: Improve pedestrian and bicycle safety
  description: The sample uses ArcGIS API for Python to help city officials in improving pedestrian and bicycle safety near schools in the city.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Schools", "Danger Zones"]
- title: Shipwrecks detection using bathymetric data
  url: https://www.arcgis.com/home/item.html?id=e7a53954d99144be868bd30e8cde2523
  path: ./samples/04_gis_analysts_data_scientists/shipwrecks_detection_using_bathymetric_data.ipynb
  thumbnail: ./static/thumbnails/shipwrecks_detection_using_bathymetric_data.png
  snippet: Use bathymetric data to detect shipwrecks
  description: In this notebook, we will use bathymetry data provided by NOAA to detect shipwrecks from the Shell Bank Basin area located near New York City in United States.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Shipwrecks", "Bathymetric", "Deep Learning"]
- title: Snow Avalanche Hazard Mapping for Lake Tahoe
  url: https://www.arcgis.com/home/item.html?id=bd6e2767cb294b88b09c5cb38441131e
  path: ./samples/04_gis_analysts_data_scientists/snow_avalanche_hazard_mapping_for_lake_tahoe.ipynb
  thumbnail: ./static/thumbnails/snow_avalanche_hazard_mapping_for_lake_tahoe.png
  snippet: Map avalanches in Lake Tahoe, California
  description: Weighted Linear Combination (WLC) method based on combined GIS and Remote Sensing techniques is used in the sample to create a potential hazard map for avalanches.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Avalanche", "Mapping"]
- title: Spatial and temporal distribution of service calls using big data tools
  url: https://www.arcgis.com/home/item.html?id=7b6991aa6f4d4ce0be6e43badb04d117
  path: ./samples/04_gis_analysts_data_scientists/spatial_and_temporal_trends_of_service_calls.ipynb
  thumbnail: ./static/thumbnails/spatial_and_temporal_trends_of_service_calls.png
  snippet: Use big data tools for spatial and temporal distribution
  description: This sample demonstrates ability of ArcGIS API for Python to perform big data analysis on your infrastructure.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Service Calls", "GeoAnalytics", "Trends", "Spatial", "Temporal"]
- title: Temperature forecast using time series data
  url: https://www.arcgis.com/home/item.html?id=cf173caaba3f495f9592a9f180361ee4
  path: ./samples/04_gis_analysts_data_scientists/temperature_forecast_using_time_series_data.ipynb
  thumbnail: ./static/thumbnails/temperature_forecast_using_time_series_data.png
  snippet: Use time series data to forecast temperature
  description: This sample showcases two autoregressive methods. one using a deep learning and another using a machine learning framework to predict temperature of England.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Time Series", "Temperature", "Forecast"]
# - title: Plant species identification using a TensorFlow-Lite model within mobile devices
#   url: https://www.arcgis.com/home/item.html?id=fc21cc2f4a014a8e88f72d846b5afff1
#   path: ./samples/04_gis_analysts_data_scientists/train_a_tensorflow-lite_model_for_identifying_plant_species.ipynb
#   thumbnail: ./static/thumbnails/train_a_tensorflow-lite_model_for_identifying_plant_species.png
#   snippet: Identify plant species using a tensorflow model
#   description: This notebook intends to showcase this capability to train a deep learning model that can be used in mobile applications for a real time inferencing using TensorFlow Lite framework.
#   licenseInfo: ""
#   runtime: advanced_gpu
#   tags: ["Data Science", "GIS", "TansorFlow Lite", "Plant Species", "Deep Learning"]
- title: Vehicle detection and tracking using deep learning
  url: https://www.arcgis.com/home/item.html?id=871057ea4c864343900f36f3bf64b675
  path: ./samples/04_gis_analysts_data_scientists/vehicle_detection_and_tracking.ipynb
  thumbnail: ./static/thumbnails/vehicle_detection_and_tracking.gif
  snippet: Use deep learning to track vehicles
  description: In this notebook, we'll demonstrate how we can use deep learning to detect vehicles and then track them in a video.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Vehicle", "Detection", "Deep Learning", "Tracking", "Featured"]
# - title: Visualize monthly changes in Hirakund reservoir using video
#   url: https://www.arcgis.com/home/item.html?id=8fdbf15d65674f69a947cbe449eb3647
#   path: ./samples/04_gis_analysts_data_scientists/visualize_monthly_changes_in_hirakund_reservoir_using_video.ipynb
#   thumbnail: ./static/thumbnails/visualize_monthly_changes_in_hirakund_reservoir_using_video.gif
#   snippet: Visualize monthly changes in the Hirakund reservoir
#   description: This notebook creates a movie to visualize monthly changes in Hirakund reservoir, Odisha.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Changes", "Video"]
- title: Which areas are good cougar habitat?
  url: https://www.arcgis.com/home/item.html?id=74f55ed014014f90a1ff81d469abbf22
  path: ./samples/04_gis_analysts_data_scientists/which_areas_are_good_cougar_habitat.ipynb
  thumbnail: ./static/thumbnails/which_areas_are_good_cougar_habitat.png
  snippet: Use spatial analysis tools to identify cougar habitat areas
  description: Through this notebook, we will demonstrate the utility of a number of spatial analysis tools including create_buffer, extract_data, dissolve_boundaries, and derive_new_locations.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Cougar Habitat", "Featured"]
- title: Which college district has the fewest low-income families?
  url: https://www.arcgis.com/home/item.html?id=abae8a0ca9554ae2b46e7ada352e502a
  path: ./samples/04_gis_analysts_data_scientists/which_college_district_has_the_fewest_low_income_families.ipynb
  thumbnail: ./static/thumbnails/which_college_district_has_the_fewest_low_income_families.png
  snippet: Use the `summarize_within` tool to get the number of low-income families
  description: This case study uses ArcGIS API for Python to find districts that have the fewest low income families in order to empower these students.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "low income families"]
# - title: Pawnee Fire Analysis
#   url: https://www.arcgis.com/home/item.html?id=927ff9cf8d9241308af317317224bd81
#   path: ./samples/04_gis_analysts_data_scientists/wildfire_analysis_using_sentinel-2_imagery.ipynb
#   thumbnail: ./static/thumbnails/wildfire_analysis_using_sentinel-2_imagery.jpg
#   snippet: Use remote sensing to analyze the pawnee fire
#   description: In this notebook example, we used Sentinel-2 data in order to perform remote sensing.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Wildfire", "Sentinel-2"]
# - title:
#   url: https://www.arcgis.com/home/item.html?id=fca4cf6436a04325bbd62b7330830a80
#   path: ./samples/04_gis_analysts_data_scientists/wildfire_analysis_using_sentinel-2_imagery.ipynb
#   thumbnail: ./static/thumbnails/wildfire_analysis_using_sentinel-2_imagery.jpg
#   snippet: Use remote sensing to analyze the pawnee fire
#   description: In this notebook example, we used Sentinel-2 data in order to perform remote sensing.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Wildfire", "Sentinel-2"]
- title: Interactive raster analytics using Jupyter Dashboards
  url: https://www.arcgis.com/home/item.html?id=f4e7815c90064f4bb058a1f7c9fdc745
  path: ./samples/02_power_users_developers/jupyter_dashboard_for_raster_analytics.ipynb
  thumbnail: ./static/thumbnails/jupyter_dashboard_for_raster_analytics.png
  snippet: Jupyter dashboard for raster analytics
  description: This sample illustrates one such app which can be used to detect the changes in vegetation between the two dates.
  licenseInfo: ''
  tags: ['Data Science', 'GIS', "Dashboard"]
- title: Exploring OpenStreetMap using Pandas and the Python API
  url: https://www.arcgis.com/home/item.html?id=1fcf2158a8844b3fa2fec87b9b9bbc51
  path: ./samples/02_power_users_developers/openstreetmap_exploration.ipynb
  thumbnail: ./static/thumbnails/openstreetmap_exploration.png
  snippet: OpenStreetMap exploration
  description: Develop map-driven tools to explore OSM with the full capabilities of the ArcGIS platform
  licenseInfo: ''
  tags: ['Data Science', 'GIS', "Open Street Map"]
- title: A dashboard to explore world population
  url: https://www.arcgis.com/home/item.html?id=a2d94e983a354b608ee60e29011ed02f
  path: ./samples/02_power_users_developers/population_exploration_dashboard.ipynb
  thumbnail: ./static/thumbnails/population_exploration_dashboard.png
  snippet: Population exploration dashboard
  description: This sample illustrates one such app which can be used to detect the changes in vegetation between the two dates. Increases in vegetation are shown in green, and decreases are shown in magenta.
  licenseInfo: ''
  tags: ['Data Science', 'GIS', "Population", "Dashboard"]
- title: Tour the World with Landsat Imagery and Raster Functions
  url: https://www.arcgis.com/home/item.html?id=6e08b2b2be7948258440cfca8821d7b8
  path: ./samples/02_power_users_developers/tour_the_world_with_landsat_imagery_and_raster_functions.ipynb
  thumbnail: ./static/thumbnails/tour_the_world_with_landsat_imagery_and_raster_functions.png
  snippet: tour the world with landsat imagery and raster functions
  description: This notebook provides links to interesting locations using different band combinations of Landsat 8 imagery.
  licenseInfo: ''
  tags: ['Data Science', 'GIS', "Raster", "Functions", "Landsat"]
- title: Using Geometry Functions
  url: https://www.arcgis.com/home/item.html?id=4d09e890e36b446f8aaa17e366e58b80
  path: ./samples/02_power_users_developers/using_geometry_functions.ipynb
  thumbnail: ./static/thumbnails/using_geometry_functions.png
  snippet: using geometry functions
  description: This notebook uses the arcgis.geometry module to compute the length of a path that the user draws on the map.
  licenseInfo: ''
  tags: ['Data Science', 'GIS', "Geometry"]
- title: Using Geoprocessing Tools
  url: https://www.arcgis.com/home/item.html?id=5a5839d87b4645e685bcd46d79995358
  path: ./samples/02_power_users_developers/using_geoprocessing_tools.ipynb
  thumbnail: ./static/thumbnails/using_geoprocessing_tools.png
  snippet: using geoprocessing tools
  description:  The analysis below uses a geoprocessing tool to deduce the path that the debris of a crashed airplane would take if it went down at different places in the ocean.
  licenseInfo: ''
  tags: ['Data Science', 'GIS', "Geoprocessing tools"]
# - title: Hey GIS, Give me a map of the recent natural disasters
#   url: https://www.arcgis.com/home/item.html?id=7eae3c9f586f4d7ab7494b0494c9a97c
#   path: ./samples/05_content_publishers/hey_gis_give_me_a_map_of_the_recent_natural_disasters.ipynb
#   thumbnail: ./static/thumbnails/hey_gis_give_me_a_map_of_the_recent_natural_disasters.png
#   snippet: hey gis give me a map of the recent natural disasters
#   description: The sample notebook takes advantage of NASA's Earth Observatory Natural Event Tracker (EONET) API to collect a curated and continuously updated set of natural event metadata, and transform them into ArcGIS FeatureCollection(s) and save them into Web Maps in your GIS.
#   licenseInfo: ''
#   tags: ['Data Science', 'GIS', "Natural Disasters", "Map"]
- title: HTML Table to Pandas Data Frame to Portal Item
  url: https://www.arcgis.com/home/item.html?id=8bbc583569244b2daabe8079c5644fc2
  path: ./samples/05_content_publishers/html_table_to_pandas_data_frame_to_portal_item.ipynb
  thumbnail: ./static/thumbnails/html_table_to_pandas_data_frame_to_portal_item.png
  snippet: html table to pandas data frame to portal item
  description: This sample shows how Pandas can be used to extract data from a table within a web page (in this case, a Wikipedia article) and how it can be then brought into the GIS for further analysis and visualization.
  licenseInfo: ''
  tags: ['Data Science', 'GIS', "Table", "DataFrame"]
- title: Identify Items That Use Insecure URLs
  url: https://www.arcgis.com/home/item.html?id=beb39a173457489f8a23e8254a8112ef
  path: ./samples/05_content_publishers/Identify_Items_That_Use_Insecure_URLs.ipynb
  thumbnail: ./static/thumbnails/Identify_Items_That_Use_Insecure_URLs.png
  snippet: Identify Items That Use Insecure URLs
  description: This notebook will search through all WebMap/WebScene/App Items in a portal/organization, identifying the 'insecure' ones if one or more service URLs use http\://.
  licenseInfo: ''
  tags: ['Data Science', 'GIS', "Insecure URL"]
# - title: Overwriting feature layers
#   url: https://www.arcgis.com/home/item.html?id=691578df03c04f88862bc61774501699
#   path: ./samples/05_content_publishers/overwriting_feature_layers.ipynb
#   thumbnail: ./static/thumbnails/overwriting_feature_layers.png
#   snippet: overwriting feature layers
#   description: In this sample, we edit individual features as updated datasets are available
#   licenseInfo: ''
#   tags: ['Data Science', 'GIS', "Overwrite", "Feature", "Layers"]
# - title: PDF Table to PDF Map
#   url: https://www.arcgis.com/home/item.html?id=d4124579b757443fa0577a93ad1d07ab
#   path: ./samples/05_content_publishers/pdf_table_to_pdf_map.ipynb
#   thumbnail: ./static/thumbnails/pdf_table_to_pdf_map.png
#   snippet: pdf table to pdf map
#   description: This sample shows how Pandas can be used to extract data from a table within a PDF file into the GIS for further analysis and visualization
#   licenseInfo: ''
#   tags: ['Data Science', 'GIS', "pdf", "Table", "Map"]
# - title: Publishing packages as web layers
#   url: https://www.arcgis.com/home/item.html?id=d759771e21344942b5b67cf34439c91c
#   path: ./samples/05_content_publishers/publishing_packages_as_web_layers.ipynb
#   thumbnail: ./static/thumbnails/publishing_packages_as_web_layers.png
#   snippet: publishing packages as web layers
#   description: In this sample, we will observe how to publish web layers from tile, vector tile and scene layer packages.
#   licenseInfo: ''
#   tags: ['Data Science', 'GIS', "Web", "Publish", "Layers"]
# - title: Publishing SDs, Shapefiles, and CSVs
#   url: https://www.arcgis.com/home/item.html?id=a1db6db172bc49a8932daacc2ed3d3ac
#   path: ./samples/05_content_publishers/publishing_sd_shapefiles_and_csv.ipynb
#   thumbnail: ./static/thumbnails/publishing_sd_shapefiles_and_csv.png
#   snippet: publishing sd shapefiles and csv
#   description: This sample notebook shows how different types of GIS datasets can be added to the GIS, and published as web layers.
#   licenseInfo: ''
#   tags: ['Data Science', 'GIS', "Shapefiles", "Publish", "CSV"]
# - title: Publishing web maps and web scenes
#   url: https://www.arcgis.com/home/item.html?id=9840ca386ee7480a880d84a497db41de
#   path: ./samples/05_content_publishers/publishing_web_maps_and_web_scenes.ipynb
#   thumbnail: ./static/thumbnails/publishing_web_maps_and_web_scenes.png
#   snippet: publishing web maps and web scenes
#   description: This sample demonstrates how to create and publish simple examples of web maps and scenes using the Python API.
#   licenseInfo: ''
#   tags: ['Data Science', 'GIS', "Maps", "Web Scenes", "Publish"]
- title: Building a change detection app using Jupyter Dashboard
  url: https://www.arcgis.com/home/item.html?id=e3a0e48329cf4213a15574dd4b6b7694
  path: ./samples/02_power_users_developers/building_a_change_detection_app_using_jupyter_dashboard.ipynb
  thumbnail: ./static/thumbnails/jupyter_dashboard_change.png
  snippet: Create an interactive jupyter dashboard
  description: This sample illustrates an interactive Jupyter dashboard web app which can be used to detect the changes in vegetation between the two dates.
  licenseInfo: ''
  tags: ['Jupyter', 'Dashboard', "Vegetation", "raster"]
- title: Identifying country names from incomplete house addresses
  url: https://www.arcgis.com/home/item.html?id=d52e28b3cd854c7fa92157f5cc46ca2c
  path: ./samples/04_gis_analysts_data_scientists/identifying-country-names-from-incomplete-house-addresses.ipynb
  thumbnail: ./static/thumbnails/identifying_country_names_from_incomplete_house_addresses.jpg
  snippet: Build a classifier to predict the country for incomplete house addresses.
  description: In this notebook we will build a classifier using TextClassifier class of arcgis.learn.text module to predict the country for these incomplete house addresses.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Text", "Classification", "Deep Learning"]
- title: SAR to RGB image translation using CycleGAN
  url: https://www.arcgis.com/home/item.html?id=489faf70fcc2475fa41050fa98ea27ee
  path: ./samples/04_gis_analysts_data_scientists/sar_to_rgb_image_translation_using_cyclegan.ipynb
  thumbnail: ./static/thumbnails/sar_to_rgb_image_translation_using_cyclegan.jpg
  snippet: Train a deep learning model to translate SAR imagery to RGB imagery.
  description: In this notebook we will train a deep learning model to translate SAR imagery to RGB imagery, thereby making optical data (translated) available even in extreme weather days and cloudy areas.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Image", "Translation", "Deep Learning"]
# - title: Automatic road extraction using deep learning
#   url: https://www.arcgis.com/home/item.html?id=b8f7bbb077b94cb8ac5195b940278cb6
#   path: ./samples/04_gis_analysts_data_scientists/automatic_road_extraction_using_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/automatic_road_extraction_using_deep_learning.jpg
#   snippet: Classify roads, utilizing API's Multi-Task Road Extractor model.
#   description: In this notebook we will train a deep learning model (Multi-Task Road Extractor model) to extract the road network from satellite imagery.
#   licenseInfo: ""
#   runtime: advanced_gpu
#   tags: ["Data Science", "GIS", "Road", "Extraction", "Deep Learning"]
# - title: Creating building models using point cloud classification
#   url: https://www.arcgis.com/home/item.html?id=739fc8e4641d4e4e9068f56b9437d33e
#   path: ./samples/04_gis_analysts_data_scientists/creating_building_models_using_point_cloud_classification.ipynb
#   thumbnail: ./static/thumbnails/creating_building_models_using_point_cloud_classification.jpg
#   snippet: Classify building points using API's PointCNN model and generate 3D building multipatches, from classified building points.
#   description: In this notebook we will train a deep learning model to classify building points using API's PointCNN model and generate 3D building multipatches, from classified building points.
#   licenseInfo: ""
#   runtime: advanced_gpu
#   tags: ["Data Science", "GIS", "Building", "Classification", "Deep Learning"]
- title: Address Standardization and Correction using SequenceToSequence model
  url: https://www.arcgis.com/home/item.html?id=959062ea7437471b9f39c4bb3f88d122
  path: ./samples/04_gis_analysts_data_scientists/address-standardization-and-correction-with-sequencetosequence.ipynb
  thumbnail: ./static/thumbnails/address_standardization.jpg
  snippet: Train a model using SequenceToSequence class of arcgis.learn.text module to translate the non-standard and erroneous address to their standard and correct form.
  description: Train a model using SequenceToSequence class of arcgis.learn.text module to translate the non-standard and erroneous address to their standard and correct form.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Text", "Classification", "Deep Learning"]
- title: Using and updating GIS content
  url: https://www.arcgis.com/home/item.html?id=46a02bf3fe8546ec9ec4ffb230b94059
  path: ./samples/05_content_publishers/using_and_updating_GIS_content.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This sample shows you how to update the content of web maps and web scenes.
  description: This sample shows you how to update the content of web maps and web scenes.
  licenseInfo: ""
  tags: ["Data Science", "GIS", "Text", "Classification"]
# - title: Updating features in a feature layer
#   url: https://www.arcgis.com/home/item.html?id=e8e2b0ac54584d079420b35b011d3d50
#   path: ./samples/05_content_publishers/updating_features_in_a_feature_layer.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: As content publishers, you may be required to keep certain web layers upto date. As new data arrives, you may have to append new features, update existing features etc.
#   description: As content publishers, you may be required to keep certain web layers upto date. As new data arrives, you may have to append new features, update existing features etc.
#   licenseInfo: ""
#   tags: ["Data Science", "GIS", "Text", "Classification"]
- title: Land Parcel Extraction using Edge Detection model
  url: https://www.arcgis.com/home/item.html?id=e164250b748240b5909159602dee826a
  path: ./samples/04_gis_analysts_data_scientists/land_parcel_extraction_using_edge_detection_deep_learning_model.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This sample shows how ArcGIS API for Python can be used to train a deep learning edge detection model to extract parcels from satellite imagery.
  description: This sample shows how ArcGIS API for Python can be used to train a deep learning edge detection model to extract parcels from satellite imagery.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Edge Detection", "Land Parcel", "Deep Learning"]
- title: Change Detection of Buildings from Satellite Imagery
  url: https://www.arcgis.com/home/item.html?id=d700b1713cbf404bad01d92dfca0c91b
  path: ./samples/04_gis_analysts_data_scientists/change_detection_of_buildings_from_satellite_imagery.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: ChangeDetector is used to identify areas of persistent change between two different time periods using remotely sensed images.
  description: ChangeDetector is used to identify areas of persistent change between two different time periods using remotely sensed images.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Change Detection", "Deep Learning"]
# - title: Generating rgb imagery from digital surface model using Pix2Pix
#   url: https://www.arcgis.com/home/item.html?id=d2d58e9d0e624f4baddd983d8acea3da
#   path: ./samples/04_gis_analysts_data_scientists/generating_rgb_imagery_from_digital_surface_model_using_pix2pix.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: The aim of this notebook is to make use of arcgis.learn Pix2Pix model to translate or convert the gray-scale DSM to a RGB imagery.
#   description: The aim of this notebook is to make use of arcgis.learn Pix2Pix model to translate or convert the gray-scale DSM to a RGB imagery.
#   licenseInfo: ""
#   runtime: advanced_gpu
#   tags: ["Data Science", "GIS", "Image Translation", "Deep Learning"]
# - title: Coastline extraction using Landsat-8 multispectral imagery and band ratio technique
#   url: https://www.arcgis.com/home/item.html?id=4d8d5789a5e045bbbd02a01903841439
#   path: ./samples/04_gis_analysts_data_scientists/coastline_extraction-usa-landsat8_multispectral_imagery.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: Landsat-8 multispectral imagery is used in the current study as it covers a wavelength ranging from 0.43 to 12.51 micrometers, and hence suitable for coastal and aerosol studies.
#   description: Landsat-8 multispectral imagery is used in the current study as it covers a wavelength ranging from 0.43 to 12.51 micrometers, and hence suitable for coastal and aerosol studies.
#   licenseInfo: ""
#   runtime: advanced_gpu
#   tags: ["Data Science", "GIS", "Coastline Extraction", "Imagery"]
- title: Solar Energy prediction using Weather Variables
  url: https://www.arcgis.com/home/item.html?id=7c28e6fce0584127b6656a98d3577d01
  path: ./samples/04_gis_analysts_data_scientists/solar-energy-prediction-using-weather-variables.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: The aim of this notebook is to make use of arcgis.learn tabular models to predict solar energy using weather variables.
  description: The aim of this notebook is to make use of arcgis.learn tabular models to predict solar energy using weather variables.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Solar energy prediction", "Deep Learning"]
- title: Forecast Monthly Rainfall using TimeSeriesModel
  url: https://www.arcgis.com/home/item.html?id=e2bf1e372de8495cbc3e66aed3aade3c
  path: ./samples/04_gis_analysts_data_scientists/forecasting_monthly_rainfall_in_california_using_deeplearning_timeseries_model_from_arcgis_learn.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: The aim of this notebook is to make use of arcgis.learn TimeSeriesModel to forecast monthly rainfall in California.
  description: The aim of this notebook is to make use of arcgis.learn TimeSeriesModel to forecast monthly rainfall in California.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Rainfall forecast", "Deep Learning"]
- title: LandCover Classification using Hyperspectral Imagery and Deep Learning
  url: https://www.arcgis.com/home/item.html?id=f253e07e0d3142ea87b4e024393c8eb0
  path: ./samples/04_gis_analysts_data_scientists/landcover_classification_using_hyperspectral_imagery_and_deep_learning.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: The aim of this notebook is to make use of arcgis.learn UnetClassifier model to extract subclasses of two LULC classes mainly developed areas and forests.
  description: The aim of this notebook is to make use of arcgis.learn UnetClassifier model to extract subclasses of two LULC classes mainly developed areas and forests.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Hyperspectral", "Deep Learning"]
- title: Streams Extraction using MultiTaskRoadExtractor
  url: https://www.arcgis.com/home/item.html?id=356899f6baad407b9db49bb526073ee1
  path: ./samples/04_gis_analysts_data_scientists/streams_extraction_using_multi_task_road_extractor.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.
  description: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.
  licenseInfo: ""
  runtime: advanced_gpu
  tags: ["Data Science", "GIS", "Stream Extraction", "Deep Learning"]
# - title: Supervised learning of tabular data using AutoML
#   url: https://www.arcgis.com/home/item.html?id=06486550d1e148e298a9d572cfedcf5e
#   path: ./samples/04_gis_analysts_data_scientists/tabular_data_supervised_learning_using_automl.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: arcgis.learn users will now be able to use AutoML for supervised learning classification or regression problems involving tabular data.
#   description: arcgis.learn users will now be able to use AutoML for supervised learning classification or regression problems involving tabular data.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Supervised Learning", "Tabular Data"]
# - title: Model explainability for ML Models
#   url: https://www.arcgis.com/home/item.html?id=3eaade48a6204a08861b9cfb2497be83
#   path: ./samples/04_gis_analysts_data_scientists/model_explainability_using_shap_for_tabular_data.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: arcgis.learn has now added explainability feature to all of its models that work with tabular data. This includes all the MLModels and the fully connected networks.
#   description: arcgis.learn has now added explainability feature to all of its models that work with tabular data. This includes all the MLModels and the fully connected networks.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Supervised Learning", "Tabular Data"]
# - title: Determining site suitability for oil palm plantation
#   url: https://www.arcgis.com/home/item.html?id=47d07342d1204449bb661d6cb12d0368
#   path: ./samples/04_gis_analysts_data_scientists/determining_site_suitability_for_oil_palm_plantation.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this notebook, we determine the site suitability for oil palm development.
#   description: In this notebook, we determine the site suitability for oil palm development.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Glacial Terminus Extraction using HRNet
#   url: https://www.arcgis.com/home/item.html?id=e4ee1dbbfdb44853b5cfaef8b2789f7e
#   path: ./samples/04_gis_analysts_data_scientists/glacial_terminus_extraction_using_hrnet.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: This notebook presents the use of an HRNet model from the arcgis.learn module to accomplish the first task of segmenting calving fronts.
#   description: This notebook presents the use of an HRNet model from the arcgis.learn module to accomplish the first task of segmenting calving fronts.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Finetuning Pre-trained Building Footprint Model
#   url: https://www.arcgis.com/home/item.html?id=c90460e8950a4a7e98c2c060deeb5c10
#   path: ./samples/04_gis_analysts_data_scientists/finetuning_pre-trained_building_footprint_model.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this workflow, we will load the training data, finetune a pre-trained model and extract footprints.
#   description: In this workflow, we will load the training data, finetune a pre-trained model and extract footprints.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Multi-class change detection using image segmentation deep learning models
#   url: https://www.arcgis.com/home/item.html?id=b478f6cc89554986a955999054db59c4
#   path: ./samples/04_gis_analysts_data_scientists/multi_class_change_detection_using_segmentation_deep_learning_models.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this notebook, we will show a novel way to detect and classify change using semantic segmentation models available in arcgis.learn.
#   description: In this notebook, we will show a novel way to detect and classify change using semantic segmentation models available in arcgis.learn.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Classification of SfM-derived point clouds using deep learning
#   url: https://www.arcgis.com/home/item.html?id=f39a0594e523450b898d312dcf72badc
#   path: ./samples/04_gis_analysts_data_scientists/classification_of_sfm_derived_point_clouds_using_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this sample notebook, we will be using the term: 'SfM-derived point clouds' with context to describe point clouds generated from ESRI Site Scan for ArcGIS and ArcGIS Drone2Map.
#   description: In this sample notebook, we will be using the term: 'SfM-derived point clouds' with context to describe point clouds generated from ESRI Site Scan for ArcGIS and ArcGIS Drone2Map.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Coastline classification using Feature Classifier
#   url: https://www.arcgis.com/home/item.html?id=9ef3ab6a0bde44e5bcbe68b165b8e6dd
#   path: ./samples/04_gis_analysts_data_scientists/coastline_classification_using_feature_classifier.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this sample notebook, we will see how we can classify these coastlines in the categories mentioned in figure 1, by training a Feature Classifier model.
#   description: In this sample notebook, we will see how we can classify these coastlines in the categories mentioned in figure 1, by training a Feature Classifier model.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Wildlife Species Identification in Camera Trap Images
#   url: https://www.arcgis.com/home/item.html?id=aa06b3add8e143e08014a9a7a5d2c94b
#   path: ./samples/04_gis_analysts_data_scientists/wildlife_species_identification_in_camera_trap_images.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: This notebook will showcase a workflow to classify animal species in camera trap images.
#   description: This notebook will showcase a workflow to classify animal species in camera trap images.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Detecting deforestation in the Amazon rainforest using unsupervised K-means clustering on satellite imagery
#   url: https://www.arcgis.com/home/item.html?id=cd3d2c1b7f4047f98bf07beb10ee2016
#   path: ./samples/04_gis_analysts_data_scientists/detecting-deforestation-using-kmeans-clustering-on-sentinel-imagery.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: This notebook will allow us to detect deforested areas in the Brazilian Amazon rainforest, using satellite imagery.
#   description: This notebook will allow us to detect deforested areas in the Brazilian Amazon rainforest, using satellite imagery.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Detecting Mussel Farms using Deep Learning
#   url: https://www.arcgis.com/home/item.html?id=ff6e52bfa7714fe89c1345fd3fa9b7c0
#   path: ./samples/04_gis_analysts_data_scientists/detecting_mussel_farms_using_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this notebook, we will train a deep learning model to detect mussel farms in high-resolution imagery of the Ria De Arousa region of Spain.
#   description: In this notebook, we will train a deep learning model to detect mussel farms in high-resolution imagery of the Ria De Arousa region of Spain.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Forecasting Air Temperature in California using ResCNN model
#   url: https://www.arcgis.com/home/item.html?id=ecefff2763404106b87c02ef70123930
#   path: ./samples/04_gis_analysts_data_scientists/forecasting_air_temperature_in_california_using_rescnn_model.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this study, the deep learning TimeSeriesModel from arcgis.learn is used to predict monthly air temperature for two years.
#   description: In this study, the deep learning TimeSeriesModel from arcgis.learn is used to predict monthly air temperature for two years.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Count cars in aerial imagery using deep learning
#   url: https://www.arcgis.com/home/item.html?id=b80e02d207aa43a193601afe2390bf86
#   path: ./samples/04_gis_analysts_data_scientists/count_cars_in_aerial_imagery_using_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: The pre-trained Car Detection-USA model is used to detect cars in high-resolution drone or aerial imagery.
#   description: The pre-trained Car Detection-USA model is used to detect cars in high-resolution drone or aerial imagery.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Classify land cover to measure shrinking lakes
#   url: https://www.arcgis.com/home/item.html?id=32f8860e155d4f03987b94be876d73c1
#   path: ./samples/04_gis_analysts_data_scientists/measure_shrinking_lakes_using_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: This sample aims to demonstrate how ArcGIS pre-trained models can be used to generate landcover from imageries of different time periods.
#   description: This sample aims to demonstrate how ArcGIS pre-trained models can be used to generate landcover from imageries of different time periods.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
# - title: Lunar Craters Detection using Deep Learning
#   url: https://www.arcgis.com/home/item.html?id=a1406e406ec746f4b33fcf0aba55e980
#   path: ./samples/04_gis_analysts_data_scientists/lunar_craters_detection_from_dem_using_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: This notebook will demonstrate how the ArcGIS API for Python can be used to train a deep learning crater detection model using a Digital Elevation Model (DEM), which can then be deployed in ArcGIS Pro or ArcGIS Enterprise.
#   description: This notebook will demonstrate how the ArcGIS API for Python can be used to train a deep learning crater detection model using a Digital Elevation Model (DEM), which can then be deployed in ArcGIS Pro or ArcGIS Enterprise.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Raster Analysis"]
# - title: Training a wind turbine detection model using large volumes of training data
#   url: https://www.arcgis.com/home/item.html?id=d127b9f7cfb54283bec551d9e9911b33
#   path: ./samples/04_gis_analysts_data_scientists/training_a_wind_turbine_detection_model_using_large_volume_of_training_data.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: This notebook trains a wind turbine detection model using large volumes of training data.
#   description: This notebook trains a wind turbine detection model using large volumes of training data.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Raster Analysis"]
# - title: Landsat 8 to Sentinel-2 using Pix2Pix
#   url: https://www.arcgis.com/home/item.html?id=3576325e2d4c4dbfa56a4217882134f9
#   path: ./samples/04_gis_analysts_data_scientists/landsat8_to_sentinel2_pix2pix.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this notebook, we use the Pix2Pix model to convert 30 meter resolution Landsat 8 imagery to 10 meter resolution Sentinel-2 imagery.
#   description: In this notebook, we use the Pix2Pix model to convert 30 meter resolution Landsat 8 imagery to 10 meter resolution Sentinel-2 imagery.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Raster Analysis"]
# - title: Image scene classification using FeatureClassifier
#   url: https://www.arcgis.com/home/item.html?id=6ea5821a4a864b3990bb13ea1c77887f
#   path: ./samples/04_gis_analysts_data_scientists/image_scene_classification_using_feature_classifier.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: In this notebook, we will train an object classification model on image data from an external source and use that model for inferencing in ArcGIS Pro.
#   description: In this notebook, we will train an object classification model on image data from an external source and use that model for inferencing in ArcGIS Pro.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Raster Analysis"]
# - title: Covid case forecasting Using TimeSeriesModel from arcgis.learn
#   url: https://www.arcgis.com/home/item.html?id=b0748b483e104389b6ac51dbacb48e8a
#   path: ./samples/04_gis_analysts_data_scientists/covid_case_forecasting_for_alabama_state_using_timeseriesmodel_from_arcgis_learn.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: This notebook uses the deep learning TimeSeriesModel from arcgis.learn to analyze confirmed cases for all counties in Alabama.
#   description: This notebook uses the deep learning TimeSeriesModel from arcgis.learn to analyze confirmed cases for all counties in Alabama.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Raster Analysis"]
# - title: Image Captioning Using Deep Learning
#   url: https://www.arcgis.com/home/item.html?id=e43047eb5ae7407ab651a1324e1ba529
#   path: ./samples/04_gis_analysts_data_scientists/image_captioning_using_deep_learning.ipynb
#   thumbnail: ./static/thumbnails/default.png
#   snippet: This sample shows how ArcGIS API for Python can be used to train ImageCaptioner model using Remote Sensing Image Captioning Dataset.
#   description: This sample shows how ArcGIS API for Python can be used to train ImageCaptioner model using Remote Sensing Image Captioning Dataset.
#   licenseInfo: ""
#   runtime: advanced
#   tags: ["Data Science", "GIS", "Raster Analysis"]

guides: []
labs:
- title: Create Data
  url: https://www.arcgis.com/home/item.html?id=36c78b960dbf4829b2e7b96fef459d82
  path: ./labs/create_data.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Create Data Tutorial.
  description: This is the completed solution for the Create Data Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Search And Geocode
  url: https://www.arcgis.com/home/item.html?id=07ca2480e01145649636a2be213c4592
  path: ./labs/search_and_geocode.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Search and Geocode Tutorial.
  description: This is the completed solution for the Search And Geocode Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Load SeDF
  url: https://www.arcgis.com/home/item.html?id=9ad1644127fd46d3808a49ab1638b5c8
  path: ./labs/load_spatial_data_frame.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Load SeDF Tutorial.
  description: This is the completed solution for the Load SeDF Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Share Your Content
  url: https://www.arcgis.com/home/item.html?id=9ef9f365e8fd4e4b93998eb53c73a876
  path: ./labs/share_your_content.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Share Your Content Tutorial.
  description: This is the completed solution for the Share Your Content Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Download Data
  url: https://www.arcgis.com/home/item.html?id=9e0fd9bde904412fadea09d6114ea569
  path: ./labs/download_data.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Download Data Tutorial.
  description: This is the completed solution for the Download Data Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Display WebScene
  url: https://www.arcgis.com/home/item.html?id=95a5ebb18b3843d9ac65a7a4bf6ad050
  path: ./labs/display_web_scene.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Display WebScene Tutorial.
  description: This is the completed solution for the Display WebScene Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Import Data
  url: https://www.arcgis.com/home/item.html?id=2c0e67d8d1fb47b8a19c676e17a33575
  path: ./labs/import_data.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Import Data Tutorial.
  description: This is the completed solution for the Import Data Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Find Places
  url: https://www.arcgis.com/home/item.html?id=d32823bc0dee465da8042d05e8f5d8f7
  path: ./labs/find_places.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Find Places Tutorial.
  description: This is the completed solution for the Find Places Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Display WebMap
  url: https://www.arcgis.com/home/item.html?id=3ab4fe26fe954783b800803e77fd8937
  path: ./labs/display_web_map.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Display WebMap Tutorial.
  description: This is the completed solution for the Display WebMap Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]
- title: Load Item By Id
  url: https://www.arcgis.com/home/item.html?id=63c2fb1c19f041e8b30ac675a8062676
  path: ./labs/load_item_by_id.ipynb
  thumbnail: ./static/thumbnails/default.png
  snippet: This is the completed solution for the Load Item By Id Tutorial.
  description: This is the completed solution for the Load Item By Id Tutorial.
  licenseInfo: ""
  tags: ["GIS", "Labs"]