├── .github ├── FUNDING.yml ├── ISSUE_TEMPLATE │ ├── bexm.yml │ ├── bnd.yml │ ├── bug.yml │ ├── bup.yml │ ├── byod.yml │ └── config.yml └── workflows │ ├── badges.yml │ ├── catalog-stats.yml │ ├── community_data_parse.yml │ ├── examples_zip.yml │ ├── json_sync.yml │ ├── publish-site.yml │ ├── search_sync.yml │ └── substack_simple.yml ├── CNAME ├── CODE_OF_CONDUCT.md ├── LICENSE ├── README.md ├── awesome-gee-catalog-examples.zip ├── awesome-gee-catalog-examples ├── agriculture-vegetation-forestry │ ├── ABOVEGROUND-CARBON-GLOBAL-MONOCULTURE │ ├── ACES-BHUTAN │ ├── AF-CROPLAND-MASK-30M-2016 │ ├── BRAZIL-SECONDARY-FOREST-AGE │ ├── CA-DISTANCE-2-SECOND-CLASS │ ├── CA-FAO-FOREST-IDENTIFICATION-2019 │ ├── CA-FOREST-AGE-2019 │ ├── CA-FOREST-AGE-2019-APPROACH │ ├── CA-FOREST-HARVEST-1985-2020 │ ├── CA-FORESTED-ECOSYSTEM-LC │ ├── CA-LEAD-TREE-SPECIES │ ├── CA-SBFI │ ├── CA-SPECIES-CLASS-MEM-PROBABILITIES │ ├── CA-SPECIES-TIME-SERIES │ ├── CA-TREE-CANOPY-HEIGHT-GEDI │ ├── CA-TREE-CANOPY-HEIGHT-ICESAT │ ├── CARBON-SECURITY-INDEX │ ├── DEA-CROPLAND-EXTENT │ ├── ESA-CCI-ABOVEGROUND-BIOMASS │ ├── ESA-CCI-ABOVEGROUND-BIOMASS-DIFFERENCE │ ├── FIBOA-UK-FIELDS │ ├── FOREST-ROADS │ ├── GCEP-30-CROPLAND-EXTENT │ ├── GCI30 │ ├── GEESEBAL-ET-SOUTH-AMERICA │ ├── GFCC30TC-TREE-CANOPY-COVER │ ├── GIMMS-NDVI-1982-2022 │ ├── GLOBAL-10m-CANOPY-HEIGHT │ ├── GLOBAL-1m-CANOPY-HEIGHT │ ├── GLOBAL-CROP-PRODUCTION-TILLAGE-PRACTICES │ ├── GLOBAL-ESI-10KM │ ├── GLOBAL-FERTILIZER-USE-CROP-COUNTRY │ ├── GLOBAL-FOREST-CANOPY-HT-GEDI-LANDSAT │ ├── GLOBAL-FOREST-CARBON-FLUXES │ ├── GLOBAL-FOREST-MANAGEMENT-DATASET-2015 │ ├── GLOBAL-FUNGI-DATABASE │ ├── GLOBAL-IRRIGATION-AREAS │ ├── GLOBAL-LEAF-TRAITS │ ├── GLOBAL-NATURAL-PLANTED-FORESTS │ ├── GLOBAL-SUNLIT-SHADED-GPP-VEG-CANOPIES │ ├── KZ-GRIDDED-LIVESTOCK │ ├── LAI-FPAR-2000-2022 │ ├── LANDFIRE-DISTURBANCE │ ├── LANDFIRE-FIRE-REGIME │ ├── LANDFIRE-FUEL │ ├── LANDFIRE-TOPOGRAPHIC │ ├── LANDFIRE-TRANSPORTATION │ ├── LANDFIRE-VEGETATION │ ├── LANDFIRE-VEGETATION-VCC │ ├── LANDFIRE-VEGETATION-VDEP │ ├── LGRIP-30-CROPLAND │ ├── LGRIP-30-CROPLAND-EXTENT │ ├── MSLSP-YEARLY │ ├── NAFD-FOREST-DISTURBANCE │ ├── NASA-HARVEST-CROPLAND │ ├── PS-AFRICA-TREECOVER │ ├── QDANN-30M-YIELD-MAPS │ ├── RANGELAND-ANALYSIS-PLATFORM-EXAMPLE │ ├── TREE-ALLOMETRY-CROWN-ARCH-DATABASE │ ├── US-FRET │ ├── US-NATIONAL-FOREST-GROUP │ ├── US-NATIONAL-FOREST-TYPE │ ├── US-TILE-DRAINED-CROPLANDS │ ├── USDA-CSB-APP │ ├── USGS-MODIS-ET │ ├── USGS-US-TCC │ ├── USGS-VIIRS-ET │ ├── VEGETATION-DRYNESS-WESTERN-US │ ├── VODCA │ └── VODCA_V2 ├── analysis-ready-data │ ├── EXTENDED-SPRING-INDICES │ ├── HISTARFM-V5-EXAMPLE │ ├── HYSPECNET-11K │ ├── OPEN-AERIAL-MAP │ ├── SRER-APP-CODE │ ├── SRER-HIGHRES-DRONE │ ├── SWISSTOPO-S2-SR-HARMONIZED │ ├── USGS-3DEP-CONSOLIDATED-SURVEY-CHECKPOINTS │ ├── USGS-HISTORICAL-AERIAL-IMAGERY │ └── USGS-TOPO-RENDER ├── biodiversity-ecosystems-habitat │ ├── BIODIVERSITY-INTACTNESS-INDEX │ ├── GLOBAL-CONSENSUS-LANDCOVER │ ├── GLOBAL-CONSERVATION-IMP-BIODIV-CARBON-WATER │ ├── GLOBAL-FRESHWATER-VARIABLES │ ├── GLOBAL-HABITAT-HETEROGENEITY │ └── GROUNDWATER-DEP-ECOSYSTEMS ├── elevation-bathymetry │ ├── ASTER-GDEM │ ├── ASTWBD │ ├── COPERNICUS_GLO30 │ ├── DELTA-DTM │ ├── FABDEM │ ├── GEBCO │ ├── GLACIER-ELEVATION │ ├── ICELAND-DEM-10m │ ├── IGN-RGE-France-DEM-5m │ ├── NOAA-CoNED-TBDEM │ ├── NOAA-SLR-DEM │ ├── OPEN-CANADA-HRDEM │ ├── TINITALY-DTM-10m │ └── swiss3D-RASTER ├── fire-monitoring-analysis │ ├── ARCHIVAL-NRT-FIRMS-VIIRS-DATA │ ├── CA-FOREST-AGE-2019 │ ├── CA-FOREST-FIRE-2023 │ ├── CA-FOREST-FIRE-MAGNITUDE-1985-2020 │ ├── CA-NATIONAL-BURNED-AREA-COMPOSITE │ ├── CEMS-FIRE-DAILY │ ├── ESA-FIRE-DISTURBANCE-CCI │ ├── GLOBAL-ANNUAL-BURNED-AREA-MAPS │ ├── GLOBAL-FIRE-ATLAS │ ├── GLOBAL-FIRE-WEATHER-DB │ ├── MONITORING-TRENDS-BURN-SEVERITY │ └── WILDFIRE-RISK-COMMUNITIES ├── geophysical-biological-biogeochemical │ ├── BARE_EARTH_SPECTRA │ ├── EQUI7-GRID │ ├── GEOMORPHO90-FIRST-ORDER-DERIVATIVE │ ├── GEOMORPHO90-GEOMORPHOLOGICAL-FORMS │ ├── GEOMORPHO90-RUGGEDENESS │ ├── GEOMORPHO90-SECOND-ORDER-DERIVATIVE │ ├── GLOBAL-HABITAT-TYPES │ ├── GLOBAL-IRRECOVERABLE-CARBON │ ├── GLOBAL-LAND-SUBSIDENCE │ ├── GLOBAL-WATER-SALINITY │ ├── S1-GLOBAL-BACKSCATTER │ ├── SOIL-CARBON-STOCKS-CANADA │ ├── SOIL-NEMATODE-ABUNDANCE │ └── SOIL-ORGANIC-CARBON-SA ├── global-events-layers │ ├── GEOCODED-DISASTERS-DATASET │ ├── GLOBAL-LANDSLIDE-CATALOG │ ├── GLOBAL-LARGE-FLOOD-EVENTS │ ├── LA-LIDAR-CHANGE │ ├── LA-POSTEVENT-LIDAR │ ├── MAXAR-OPENDATA-MS │ ├── RADD-FOREST-ALERT │ ├── RADD-FOREST-ALERT-EXPORT │ ├── SEA-OF-JAPAN-EQ-2024 │ ├── UMBRA-OPENDATA │ ├── URBAN-SKY-OPENDATA │ ├── USGS-EARTHQUAKES │ └── WYVERN-OPEN-DATA ├── global-landuse-landcover │ ├── CloudSEN12-FOOTPRINT │ ├── CloudSEN12-HIGH-QUALITY │ ├── CloudSEN12-NO-LABEL │ ├── CloudSEN12-SCRIBBLE-QUALITY │ ├── DAYLIGHT-LAND-WATER-POLY │ ├── DAYLIGHT-LANDCOVER │ ├── ESA-10m-WORLDCOVER-IQ │ ├── ESA-GLOBCOVER │ ├── ESRI-10M-LANDCOVER │ ├── ESRI-LULC-2020 │ ├── GLANCE-INDICES │ ├── GLANCE-TRAINING │ ├── GLANCE-VISUALIZATION │ ├── GLC-FCS30D │ ├── GLC10 │ ├── GLOBAL-IMPERVIOUS-30-GISD │ ├── GLOBAL-IMPERVIOUS-SURFACE-AREA │ ├── GLOBAL-INTRA-URBAN-LANDUSE │ ├── GLOBAL-MANGROVE-BIOMASS-HEIGHT │ ├── GLOBAL-MANGROVE-CANOPY-HT-TANDEMX │ ├── GLOBAL-MANGROVE-WATCH │ ├── GLOBAL-OIL-PALM-1990-2021-APP │ ├── GLOBAL-PEATLAND-DATABASE │ ├── GLOBAL-PEATLAND-FRACTIONAL-COVER │ ├── GLOBAL-URBAN-EXTENTS │ ├── GLOBAL-URBAN-SCENARIO-PROJECTIONS │ ├── GWL-FCS30-WETLANDS │ ├── LANDCOVERNET_V1 │ ├── POI-LANDUSE-FRAMEWORK │ ├── RANDOLPH-GLACIER-INVENTORY │ ├── WORLD-SETTLEMENT-FOOTPRINT │ └── WORLD-SETTLEMENT-FOOTPRINT-IDC ├── global-utilities-assets-amenities │ ├── CARBON-MAPPER-METHANE-EMISSIONS │ ├── CLIMATE-TRACE-EMISSIONS │ ├── EOG-VNL-V21 │ ├── FACEBOOK-ELECTRICAL-DIST-GRID-MAPS │ ├── GLOBAL-CARBON-OFFSET-PROJECTS │ ├── GLOBAL-CEMENT-PRODUCTION-ASSETS │ ├── GLOBAL-CEMENT-SUPP-PROD-DB │ ├── GLOBAL-COMBINED-BUILDING-FOOTPRINTS-VIDA │ ├── GLOBAL-FIXED-MOBILE-NETWORK-PERF-RASTER │ ├── GLOBAL-FIXED-MOBILE-NETWORK-PERFORMANCE │ ├── GLOBAL-HEALTHSITES-MAPPING-PROJECT │ ├── GLOBAL-INDUSTRIAL-LAND │ ├── GLOBAL-IRON-STEEL-PRODUCTION-ASSETS │ ├── GLOBAL-MINING-AND-VALIDATION │ ├── GLOBAL-MINING-FOOTPRINTS │ ├── GLOBAL-NPP-VIIRS-LIKE-NTL │ ├── GLOBAL-OFFSHORE-WIND-TURBINES │ ├── GLOBAL-PHOTOVOLTAICS-INVENTORY │ ├── GLOBAL-POWERPLANT-DATABASE │ ├── GLOBAL-ROADS-INVENTORY-PROJECT │ ├── GLOBAL-SRUNET-NPP-VIIRS-LIKE-NTL │ ├── HARMONIZED-GLOBAL-NTL │ ├── HARMONIZED-WIND-SOLAR-FARMS │ ├── MLAB-EXTRACTS-NETWORK-SPEED │ ├── MS-GLOBAL-ROADS │ ├── NSI │ ├── OGIM │ ├── OOKLA-5G-MAP │ ├── OVERTURE-BUILDINGS-EXTRACT │ ├── PREDICTED-GLOBAL-POWER-SYSTEMS │ ├── SYNTHETIC-NTL-VIIRS-INDIA │ └── TZERO-GLOBAL-SOLAR-MAPPER ├── hydrology │ ├── AQUEDUCT-v30 │ ├── CYANOBACTERIA-AGG-MANUAL-LABELS │ ├── DYNQUAL-EXAMPLE │ ├── FLOODPLAIN-LAND-USE-CHANGE │ ├── GLOBAL-CHANNEL-BELT │ ├── GLOBAL-COASTAL-RIVERS-ENV-VARIABLES │ ├── GLOBAL-DAM-TRACKER │ ├── GLOBAL-DAM-WATCH-DATABASE │ ├── GLOBAL-GEOREF-DATABASE-DAMS │ ├── GLOBAL-HEIGHT-ABV-NEAREST-DRAINAGE │ ├── GLOBAL-HIGHRES-FLOODPLAINS │ ├── GLOBAL-HYDROLOGIC-CURVE-NUMBER │ ├── GLOBAL-RIVER-CLASSIFICATION(GLORIC) │ ├── GLOBAL-RIVER-DELTAS-VULNERABILITY │ ├── GLOBAL-RIVER-NETWORKS-WATER-RESOURCE-ZONES │ ├── GLOBAL-RIVER-OBSTRUCTION-DATABASE │ ├── GLOBAL-RIVER-WIDTH-LANDSAT │ ├── GLOBGM-GROUNDWATER-MODEL │ ├── GLOBathy │ ├── GLWD-V2 │ ├── HRES-INLAND-WB-NA │ ├── HYDROATLAS │ ├── HYDROGRAPHY90-BASE-NETWORK │ ├── HYDROGRAPHY90-FLOW-INDEX │ ├── HYDROGRAPHY90-STREAM-CHANNEL │ ├── HYDROGRAPHY90-STREAM-ORDER │ ├── HYDROGRAPHY90-STREAM-OUTLET-DIST │ ├── HYDROGRAPHY90-STREAM-SLOPE │ ├── HYDROLAKES │ ├── HYDROWASTE │ ├── LANDSAT-DSWE │ ├── NATIONAL-HYDROGRAPHY-DATASET │ ├── NATIONAL-WETLANDS-INVENTORY │ ├── OSM-WATER-SURFACE │ ├── RAIN4PE-GRIDDED-PRECIP-DAILY │ ├── RAIN4PE-GRIDDED-PRECIP-MONTHLY │ ├── RAIN4PE-GRIDDED-PRECIP-MONTHLY-CLIM │ ├── RAIN4PE-GRIDDED-PRECIP-YEARLY │ ├── RealSAT-GLOBAL-RESERVOIRS-LAKES │ ├── SARL-APP │ ├── STREAMFLOW-RECONSTRUCTION-INDIAN-SUBCONTINENT │ ├── SURFACE-AREA-RIVER-LAKES │ ├── SWORD-NODES-REACHES │ ├── SWORD-NODES-REACHES-MERGED │ ├── TEMPORAL-TRENDS-INDIAN-RIVERS-BASINS │ ├── TENSORFLOW-HYDRA-FLOOD-MODELS │ └── US-GROUNDWATER-WELL-DATABASE ├── oceans-shorelines │ ├── AQUALINK-SUBSET-2020 │ ├── ARGOFLOAT-SUBSET │ ├── DEA-Shorelines │ ├── DEAF-Shorlines-V040 │ ├── GLOBAL-GRIDDED-SST │ ├── GLOBAL-STORM-SURGE-RC │ ├── GLOBAL_SHORELINES │ ├── GLODAP-V2_2023_MERGED │ ├── MISMANAGED-PLASTIC-WASTE │ └── PLASTIC-INPUT-RIVERS ├── population-socioeconomics │ ├── AMD0-EDGEMATCHED │ ├── COD-EDGEMATCHED │ ├── CRITICAL-INF-SPATIAL-INDEX(CISI) │ ├── FACEBOOK-HRSL-30m │ ├── GAUL-2024 │ ├── GEOBOUNDARIES │ ├── GLOBAL-GDP-HDI │ ├── GLOBAL-HUMAN-MODIFICATION │ ├── GLOBAL-ML-BUILDINGS │ ├── GLOBPOP-COUNT-DENSITY │ ├── GPW-v4 │ ├── GRIDDED-ELECTRICITY-CONSUMPTION │ ├── GRIDDED-ELECTRICITY-CONSUMPTION-GDP │ ├── HUMANITARIAN-EDGEMATCHED │ ├── INDIGENOUS-LAND-MAPS │ ├── JRC-GHSL-2023 │ ├── LANDSCAN-GLOBAL │ ├── LANDSCAN-HD │ ├── LANDSCAN-POPULATION-COMPARE │ ├── LANDSCAN-USA │ ├── OPEN-EDGEMATCHED │ ├── ORNL-US-STRUCTURES │ ├── POMELO-POP-DENSITY │ ├── POPCORN-POPULATION-DENSITY │ ├── RELATIVE-WEALTH-INDEX(RWI) │ ├── RURAL-ACCESS-INDEX │ ├── SOCIAL-CONNECTEDNESS-INDEX(SCI) │ ├── UT-GLOBUS │ ├── WEST_AFRICA-COASTAL-VULN │ └── WORLDPOP-GRIDDED-SCHOOL-AGE ├── regional-landuse-landcover │ ├── AMAZONIA-PEATMAP │ ├── CANADA-FORESTED-ECOSYSTEM-LC │ ├── CCAP-HRLC-AS │ ├── CCAP-HRLC-CA │ ├── CCAP-HRLC-CT │ ├── CCAP-HRLC-GU │ ├── CCAP-HRLC-HI │ ├── CCAP-HRLC-LA │ ├── CCAP-HRLC-MA │ ├── CCAP-HRLC-ME │ ├── CCAP-HRLC-MP │ ├── CCAP-HRLC-OH │ ├── CCAP-HRLC-OR │ ├── CCAP-HRLC-PR │ ├── CCAP-HRLC-RI │ ├── CCAP-HRLC-VI │ ├── CCAP-IMPERVIOUS │ ├── CCAP-LC-BETA │ ├── CCAP-WETLAND-POTENTIAL │ ├── CCI-LC-20M-AFRICA │ ├── CHESEPEAKE_BAY_2013-2014 │ ├── DEA-LANDSAT-LC │ ├── EUROPE-10m-LULC │ ├── GLOBAL-INDUSTRIAL-SMALLHOLDER-OIL-PALM │ ├── LCMAP │ ├── LCMAP-REFERENCE │ ├── MISSISSIPPI-RIVER-BASIN-LUC │ ├── NLCD-ANNUAL-LANDCOVER │ ├── NLCD-ANNUAL-LANDCOVER-LAYERS │ ├── OIL-PALM-PLANTATION-LAYERS │ ├── PK-LANCOVER-CARBON-STOCK │ ├── RASTERIZED-BUILDING-FOOTPRINT-US │ ├── SOUTH-AFRICA-LULC │ ├── URBAN-WATCH-CITIES │ ├── VT-BASE-LC-2016 │ └── WEST-AFRICA-LULC ├── soil-properties │ ├── CRSL-SOIL-PROPERTIES-800 │ ├── GLHYMPS │ ├── GLOBAL-SOIL-SALINITY │ ├── HWSD-V2-SMU │ ├── HiHYDRO-SOIL-LAYERS │ ├── ISRIC-SOIL-GRID-250 │ ├── NATIONAL-SOIL-ERODABILITY-DATASET-PK │ ├── POLARIS-PROBABILISTIC-SOIL-PROPERTIES-30 │ ├── SOIL-BIOCLIM │ └── gNATSGO-DATABASE └── weather-climate │ ├── AGERA5-DATASETS │ ├── ANUSPLIN-GRID │ ├── BR-DWDG-EXAMPLE │ ├── CANADA-DROUGHT-OUTLOOK │ ├── CE-HRDPA-DAILY │ ├── CE-HRDPS-DAILY │ ├── CE-RDPA-DATASETS │ ├── CE-RDPS-DAILY │ ├── CHIRPS-PRELIM │ ├── CMIP6-CURRENT-FUTURE-BIOCLIMATIC │ ├── CMIP6-CURRENT-FUTURE-SCENARIOS │ ├── CPC-MORPH │ ├── ERA5-HEAT │ ├── GHAP-DATASETS │ ├── GLOBAL-ARIDITY-INDEX │ ├── GLOBAL-DAILY-NEAR-SURFACE-AIR-TEMP │ ├── GLOBAL-ET0 │ ├── GLOBAL-EXTREME-HEAT-HAZARD │ ├── GLOBAL-MOD10A261-Snow-Cover-8-Day │ ├── GLOBAL-MODIS-SNOWCOVER │ ├── GLOBAL-PRECIP-MEASUREMENT │ ├── GLOBAL-SATELLITE-PM25 │ ├── GLOBAL-SOLAR-ATLAS │ ├── GLOBAL-WIND-ATLAS │ ├── GLOUTCI-MONTHLY │ ├── GSHTD │ ├── HIGHRES-THERMAL-STRESS-INDICES │ ├── HXG-CLOUD-COVER │ ├── LONG-TERM-HIGHRES-AIR-POLLUTANTS │ ├── MERRA-2 │ ├── MODIS-GAPFILLED-LST-DAILY │ ├── NADM-MONTHLY │ ├── NOAA-NCLIM-GRID │ ├── NOAA-NRCC-ACIS │ ├── REFERENCE-ET-GRIDDED-PERU │ ├── SNODAS-DAILY │ ├── TERRACLIMATE-CLIMATE-FUTURES │ ├── UNITED-STATES-DROUGHT-MONITOR │ ├── URBAN-HEAT-ISLAND-INTENSITY │ ├── US-DROUGHT-OUTLOOK │ ├── US-EPA-TDEP │ ├── US-SEASONAL-DROUGHT-OUTLOOK │ └── VEGETATION-DROUGHT-RESPONSE-INDEX ├── browse.html ├── community_datasets.csv ├── community_datasets.json ├── community_datasets.jsonl ├── docs ├── CNAME ├── about_us.md ├── blog │ ├── .authors.yml │ ├── .meta.yml │ └── index.md ├── browser │ └── index.md ├── changelog.md ├── code_of_conduct.md ├── contributing │ ├── bug.md │ ├── example.md │ ├── index.md │ ├── submit.md │ └── update.md ├── forum │ └── index.md ├── history.md ├── images │ ├── logo_cropped.jpg │ └── tinitaly.gif ├── index.md ├── insiders │ ├── index.md │ └── insiders_program.md ├── involved.md ├── license.md ├── medium_blogs.md ├── projects │ ├── GPWv4.md │ ├── S2TSLULC.md │ ├── aces_bhutan.md │ ├── af_cmask.md │ ├── af_trees.md │ ├── agera5_datasets.md │ ├── ai0.md │ ├── airtemp.md │ ├── amazon_peat.md │ ├── annual_nlcd.md │ ├── anusplin.md │ ├── aogcm_cmip6.md │ ├── aqualink.md │ ├── argo.md │ ├── aster.md │ ├── astwbd.md │ ├── avhrr-ltdr.md │ ├── bii.md │ ├── br_dwgd.md │ ├── bss.md │ ├── ca_canopy_ht.md │ ├── ca_fa.md │ ├── ca_fao.md │ ├── ca_fires.md │ ├── ca_forest_fire.md │ ├── ca_forest_harvest.md │ ├── ca_lc.md │ ├── ca_sbfi.md │ ├── ca_species.md │ ├── ca_species_ts.md │ ├── caml.md │ ├── can_drought_outlook.md │ ├── canopy.md │ ├── carbon_projects.md │ ├── cc.md │ ├── ccap_lc.md │ ├── ccap_mlc.md │ ├── ccap_wpotential.md │ ├── cci_agb.md │ ├── cci_lc.md │ ├── cems_fire.md │ ├── cflux.md │ ├── chirps_prelim.md │ ├── cisi.md │ ├── climate_trace.md │ ├── cloudsen12.md │ ├── cmapper.md │ ├── cpc_morph.md │ ├── csb.md │ ├── csi.md │ ├── daily_lst.md │ ├── daylight_maps.md │ ├── dea_croplands.md │ ├── dea_lc.md │ ├── dea_shorlines.md │ ├── deaf_shorlines.md │ ├── delta_dtm.md │ ├── dynqual.md │ ├── edge_matched.md │ ├── elc.md │ ├── elc_gdp.md │ ├── electric_grid.md │ ├── energy_farms.md │ ├── eog_viirs_ntl.md │ ├── era5_heat.md │ ├── esa_iq.md │ ├── esrilc2020.md │ ├── et0.md │ ├── fabdem.md │ ├── fiboa_uk.md │ ├── firms_vector.md │ ├── flood.md │ ├── floodplain_lc.md │ ├── forest_roads.md │ ├── fpar.md │ ├── france5m.md │ ├── fret.md │ ├── gabam.md │ ├── gaul.md │ ├── gcb.md │ ├── gcc.md │ ├── gcd.md │ ├── gcd_assets.md │ ├── gcep30.md │ ├── gci.md │ ├── gci30.md │ ├── gcl.md │ ├── gcn250.md │ ├── gdat.md │ ├── gde.md │ ├── gdis.md │ ├── gdw.md │ ├── gebco.md │ ├── gee_sebal.md │ ├── geoboundary.md │ ├── geomorpho90.md │ ├── gfa.md │ ├── gfch.md │ ├── gfm_100.md │ ├── gfplain250.md │ ├── gfv.md │ ├── gfwed.md │ ├── ghap.md │ ├── ghh.md │ ├── ghm.md │ ├── ghsl.md │ ├── gid.md │ ├── gimms_ndvi.md │ ├── gisa.md │ ├── gisd30.md │ ├── giulu.md │ ├── glacier.md │ ├── glance.md │ ├── glance_training.md │ ├── glc10.md │ ├── glc_fcs.md │ ├── glo30.md │ ├── global-mining.md │ ├── global_buildings.md │ ├── global_earthquakes.md │ ├── global_esi.md │ ├── global_fertilizer.md │ ├── global_ftype.md │ ├── global_fungi.md │ ├── global_irrigation.md │ ├── global_mining.md │ ├── global_palm_oil.md │ ├── global_pm25.md │ ├── global_power.md │ ├── global_pv.md │ ├── global_salinity.md │ ├── global_tcc.md │ ├── globathy.md │ ├── globcover_esa.md │ ├── globgm.md │ ├── globpop.md │ ├── globutci.md │ ├── glodap.md │ ├── gloric.md │ ├── glwd.md │ ├── gmd.md │ ├── gnatsgo.md │ ├── goodd.md │ ├── gowt.md │ ├── gpm.md │ ├── gridded_gdp_hdi.md │ ├── gridded_livestock.md │ ├── gridded_ppt.md │ ├── grip.md │ ├── grn_wrz.md │ ├── grod.md │ ├── grwl.md │ ├── gsa.md │ ├── gshtd.md │ ├── gssr.md │ ├── gue.md │ ├── gwa.md │ ├── gwl_fcs.md │ ├── habitat.md │ ├── hand.md │ ├── harvest.md │ ├── health_sites.md │ ├── heat-hazard.md │ ├── hihydro_soil.md │ ├── histarfm.md │ ├── historical_us.md │ ├── hitisae.md │ ├── hntl.md │ ├── hrdem.md │ ├── hrdpa.md │ ├── hrdps.md │ ├── hrsl.md │ ├── hwsd.md │ ├── hydra_water.md │ ├── hydro90.md │ ├── hydroatlas.md │ ├── hydrolakes.md │ ├── hydrowaste.md │ ├── hyspecnet.md │ ├── iceland_dem.md │ ├── index.md │ ├── india_river_trends.md │ ├── irc.md │ ├── isccp_hxg.md │ ├── isric.md │ ├── japan_eq2024.md │ ├── ladem.md │ ├── land_subsidence.md │ ├── landfire.md │ ├── landscan.md │ ├── landslide.md │ ├── lcmap.md │ ├── lcnet.md │ ├── lghap.md │ ├── lgrip30.md │ ├── ltrait.md │ ├── mangrove.md │ ├── mangrove_ht_tandemx.md │ ├── mapbiomas.md │ ├── maxar_opendata.md │ ├── merrav2.md │ ├── meta_trees.md │ ├── mlab_extracts.md │ ├── modis_8day_snow.md │ ├── monoculture.md │ ├── mpw.md │ ├── msbuildings.md │ ├── mslsp.md │ ├── msroads.md │ ├── mtbs.md │ ├── nadm.md │ ├── nafd.md │ ├── native.md │ ├── nawbd.md │ ├── nbac.md │ ├── nclim_grid.md │ ├── nhd.md │ ├── noaa_acis.md │ ├── npp_viirs_ntl.md │ ├── nsi.md │ ├── nwi.md │ ├── oam.md │ ├── ogim.md │ ├── oil-palm.md │ ├── ookla_5g.md │ ├── osm_water.md │ ├── overture_buildings.md │ ├── peatland.md │ ├── peatland_ml.md │ ├── piscoeo.md │ ├── pk_lulc.md │ ├── pk_nssed.md │ ├── plastic.md │ ├── poi_lu.md │ ├── polaris.md │ ├── pomelo.md │ ├── popcorn.md │ ├── pwplants.md │ ├── qdann.md │ ├── radd.md │ ├── rai.md │ ├── rap.md │ ├── rdpa.md │ ├── rdps.md │ ├── realsat.md │ ├── rgi.md │ ├── river_deltas.md │ ├── rivermouth.md │ ├── rwi.md │ ├── s1gbm.md │ ├── s2hswiss.md │ ├── sa_nlc.md │ ├── salinity.md │ ├── sarl.md │ ├── sci.md │ ├── scs.md │ ├── secondary_forest.md │ ├── shd_sun_gpp.md │ ├── shoreline.md │ ├── slrdem.md │ ├── snodas.md │ ├── snow_cover.md │ ├── soc.md │ ├── soil_bioclim.md │ ├── soil_nematode.md │ ├── soilprop.md │ ├── speedtest.md │ ├── spring_indices.md │ ├── srer_drone.md │ ├── srunet_npp_viirs_ntl.md │ ├── sstg.md │ ├── streamflow_india.md │ ├── survey_checkpoints.md │ ├── swiss3d.md │ ├── sword.md │ ├── syn_ntl.md │ ├── tallo.md │ ├── tbdem.md │ ├── tdep.md │ ├── terraclim.md │ ├── tile.md │ ├── tillage.md │ ├── tinitaly.md │ ├── tzero.md │ ├── uhii.md │ ├── umbra_opendata.md │ ├── urban-watch.md │ ├── urban_projection.md │ ├── urbansky.md │ ├── us_ftype_fgroup.md │ ├── us_tcc.md │ ├── usa_structures.md │ ├── usbuild_raster.md │ ├── usdm.md │ ├── usgs_modis_et.md │ ├── usgs_topo.md │ ├── usgs_viirs.md │ ├── usgwd.md │ ├── ussdo.md │ ├── utglobus.md │ ├── veg_dri.md │ ├── veg_dry.md │ ├── vodca.md │ ├── vodca_v2.md │ ├── vt_lc.md │ ├── wa_lulc.md │ ├── wacvm.md │ ├── wpschool.md │ ├── wrc.md │ ├── wsf.md │ └── wyvern.md ├── publications │ └── index.md ├── reference │ └── index.md ├── search_features │ └── index.md ├── startup │ ├── catalog-assets.md │ ├── catalog-examples.md │ └── navigation.md ├── stats.md ├── substack_blogs.md ├── thumbnails │ ├── GPWv4.png │ ├── S2TSLULC.png │ ├── aces_bhutan.png │ ├── af_cmask.png │ ├── af_trees.png │ ├── agera5_datasets.png │ ├── ai0.png │ ├── airtemp.png │ ├── amazon_peat.png │ ├── annual_nlcd.png │ ├── annual_nlcd_frac_imperv_surface.png │ ├── annual_nlcd_impervious_desc.png │ ├── annual_nlcd_lc_confidence.png │ ├── annual_nlcd_spec_change_doy.png │ ├── anusplin.png │ ├── aogcm_cmip6.png │ ├── aqualink.png │ ├── argo.png │ ├── aster.png │ ├── astwbd.png │ ├── avhrr-ltdr.png │ ├── bii.png │ ├── br_dwgd.png │ ├── bss.png │ ├── ca_canopy_ht.png │ ├── ca_fa.png │ ├── ca_fao.png │ ├── ca_fires.png │ ├── ca_forest_fire.png │ ├── ca_forest_harvest.png │ ├── ca_lc.png │ ├── ca_sbfi.png │ ├── ca_species.png │ ├── ca_species_ts.png │ ├── caml.png │ ├── can_drought_outlook.png │ ├── canopy.png │ ├── carbon_projects.png │ ├── cc.png │ ├── ccap_lc.png │ ├── ccap_mlc.png │ ├── ccap_wpotential.png │ ├── cci_agb.png │ ├── cci_lc.png │ ├── cems_fire.png │ ├── cflux.png │ ├── chirps_prelim.png │ ├── cisi.png │ ├── climate_trace.png │ ├── cloudsen12.png │ ├── cmapper.png │ ├── cpc_morph.png │ ├── csb.png │ ├── csi.png │ ├── daily_lst.png │ ├── daylight_maps.png │ ├── dea_croplands.png │ ├── dea_lc.png │ ├── dea_shorlines.png │ ├── deaf_shorlines.png │ ├── delta_dtm.png │ ├── dynqual.png │ ├── edge_matched.png │ ├── elc.png │ ├── elc_gdp.png │ ├── electric_grid.png │ ├── energy_farms.png │ ├── eog_viirs_ntl.png │ ├── era5_heat.png │ ├── esa_iq.png │ ├── esrilc2020.png │ ├── et0.png │ ├── fabdem.png │ ├── fiboa_uk.png │ ├── firms_vector.png │ ├── flood.png │ ├── floodplain_lc.png │ ├── forest_roads.png │ ├── fpar.png │ ├── france5m.png │ ├── fret.png │ ├── gabam.png │ ├── gaul.png │ ├── gcb.png │ ├── gcc.png │ ├── gcd.png │ ├── gcd_assets.png │ ├── gcep30.png │ ├── gci.png │ ├── gci30.png │ ├── gcl.png │ ├── gcn250.png │ ├── gdat.png │ ├── gde.png │ ├── gdis.png │ ├── gdw.png │ ├── gebco.png │ ├── gee_sebal.png │ ├── geoboundary.png │ ├── geomorpho90.png │ ├── gfa.png │ ├── gfch.png │ ├── gfm_100.png │ ├── gfplain250.png │ ├── gfv.png │ ├── gfwed.png │ ├── ghap.png │ ├── ghh.png │ ├── ghm.png │ ├── ghsl.png │ ├── gid.png │ ├── gimms_ndvi.png │ ├── gisa.png │ ├── gisd30.png │ ├── giulu.png │ ├── glacier.png │ ├── glance.png │ ├── glance_training.png │ ├── glc10.png │ ├── glc_fcs.png │ ├── glo30.png │ ├── global-mining.png │ ├── global_buildings.png │ ├── global_earthquakes.png │ ├── global_esi.png │ ├── global_fertilizer.png │ ├── global_ftype.png │ ├── global_fungi.png │ ├── global_irrigation.png │ ├── global_mining.png │ ├── global_palm_oil.png │ ├── global_pm25.png │ ├── global_power.png │ ├── global_pv.png │ ├── global_salinity.png │ ├── global_tcc.png │ ├── globathy.png │ ├── globcover_esa.png │ ├── globgm.png │ ├── globutci.png │ ├── glodap.png │ ├── gloric.png │ ├── glwd.png │ ├── gmd.png │ ├── gnatsgo.png │ ├── goodd.png │ ├── gowt.png │ ├── gpm.png │ ├── gridded_gdp_hdi.png │ ├── gridded_livestock.png │ ├── gridded_ppt.png │ ├── grip.png │ ├── grn_wrz.png │ ├── grod.png │ ├── grwl.png │ ├── gsa.png │ ├── gshtd.png │ ├── gssr.png │ ├── gue.png │ ├── gwa.png │ ├── gwl_fcs.png │ ├── habitat.png │ ├── hand.png │ ├── harvest.png │ ├── health_sites.png │ ├── heat-hazard.png │ ├── hihydro_soil.png │ ├── histarfm.png │ ├── historical_us.png │ ├── hitisae.png │ ├── hntl.png │ ├── hrdem.png │ ├── hrdpa.png │ ├── hrdps.png │ ├── hrsl.png │ ├── hwsd.png │ ├── hydra_water.png │ ├── hydro90.png │ ├── hydroatlas.png │ ├── hydrolakes.png │ ├── hydrowaste.png │ ├── hyspecnet.png │ ├── iceland_dem.png │ ├── india_river_trends.png │ ├── irc.png │ ├── isccp_hxg.png │ ├── isric.png │ ├── japan_eq2024.png │ ├── ladem.png │ ├── land_subsidence.png │ ├── landfire.png │ ├── landscan.png │ ├── landslide.png │ ├── lcmap.png │ ├── lcnet.png │ ├── lghap.png │ ├── lgrip30.png │ ├── ltrait.png │ ├── mangrove.png │ ├── mangrove_ht_tandemx.png │ ├── mapbiomas.png │ ├── maxar_opendata.png │ ├── merrav2.png │ ├── meta_trees.png │ ├── mlab_extracts.png │ ├── modis_8day_snow.png │ ├── monoculture.png │ ├── mpw.png │ ├── msbuildings.png │ ├── mslsp.png │ ├── msroads.png │ ├── mtbs.png │ ├── nadm.png │ ├── nafd.png │ ├── native.png │ ├── nawbd.png │ ├── nbac.png │ ├── nclim_grid.png │ ├── nhd.png │ ├── noaa_acis.png │ ├── npp_viirs_ntl.png │ ├── nsi.png │ ├── nwi.png │ ├── oam.png │ ├── ogim.png │ ├── oil-palm.png │ ├── ookla_5g.png │ ├── ooklag.png │ ├── osm_water.png │ ├── overture_buildings.png │ ├── peatland.png │ ├── peatland_ml.png │ ├── piscoeo.png │ ├── pk-carbon-stock.png │ ├── pk-landcover.png │ ├── pk_nssed.png │ ├── plastic.png │ ├── polaris.png │ ├── pomelo.png │ ├── popcorn.png │ ├── pwplants.png │ ├── qdann.png │ ├── radd.png │ ├── rai.png │ ├── rap.png │ ├── rdpa.png │ ├── rdps.png │ ├── realsat.png │ ├── rgi.png │ ├── river_deltas.png │ ├── rivermouth.png │ ├── rwi.png │ ├── s1gbm.png │ ├── s2hswiss.png │ ├── sa_nlc.png │ ├── salinity.png │ ├── sarl.png │ ├── sci.png │ ├── scs.png │ ├── secondary_forest.png │ ├── shd_sun_gpp.png │ ├── shoreline.png │ ├── slrdem.png │ ├── snodas.png │ ├── snow_cover.png │ ├── soc.png │ ├── soil_bioclim.png │ ├── soil_nematode.png │ ├── soilprop.png │ ├── speedtest.png │ ├── spring_indices.png │ ├── srer_drone.png │ ├── srunet_npp_viirs_ntl.png │ ├── sstg.png │ ├── streamflow_india.png │ ├── survey_checkpoints.png │ ├── swiss3d.png │ ├── sword.png │ ├── syn_ntl.png │ ├── tallo.png │ ├── tbdem.png │ ├── tdep.png │ ├── terraclim.png │ ├── tile.png │ ├── tillage.png │ ├── tzero.png │ ├── uhii.png │ ├── umbra_opendata.png │ ├── urban-watch.png │ ├── urban_projection.png │ ├── urbansky.png │ ├── us_ftype_fgroup.png │ ├── usa_structures.png │ ├── usbuild_raster.png │ ├── usdm.png │ ├── usgs_modis_et.png │ ├── usgs_topo.png │ ├── usgs_viirs.png │ ├── usgwd.png │ ├── ussdo.png │ ├── utglobus.png │ ├── veg_dri.png │ ├── veg_dry.png │ ├── vodca.png │ ├── vodca_v2.png │ ├── vt_lc.png │ ├── wa_lulc.png │ ├── wacvm.png │ ├── wpschool.png │ ├── wrc.png │ ├── wsf.png │ └── wyvern.png └── tutorials │ ├── examples │ ├── dswe_landsat.md │ ├── gaul_aggregate.md │ ├── glc_fcs30d_lulc.md │ ├── global_shorelines.md │ └── landscan_extracts.md │ └── index.md ├── mkdocs.yml ├── overrides ├── assets │ ├── javascripts │ │ ├── custom.a678ee80.min.js │ │ ├── custom.a678ee80.min.js.map │ │ └── iconsearch_index.json │ └── stylesheets │ │ ├── custom.f7ec4df2.min.css │ │ └── custom.f7ec4df2.min.css.map └── main.html ├── search.html └── substack_feedparser.py /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | github: samapriya 2 | ko_fi: samapriya 3 | custom: 4 | - buymeacoffee.com/samapriya 5 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/config.yml: -------------------------------------------------------------------------------- 1 | blank_issues_enabled: true 2 | contact_links: 3 | - name: Discussions 4 | about: General discussions about Awesome GEE Community Catalog 5 | url: https://github.com/samapriya/awesome-gee-community-datasets/discussions 6 | - name: Linkedin 7 | about: Find me on Linkedin 8 | url: https://www.linkedin.com/in/samapriya/ 9 | -------------------------------------------------------------------------------- /.github/workflows/badges.yml: -------------------------------------------------------------------------------- 1 | name: Badges 2 | 3 | on: 4 | push: 5 | branches: 6 | - master 7 | 8 | jobs: 9 | update-badges: 10 | name: Update Badges 11 | runs-on: ubuntu-latest 12 | steps: 13 | - name: Checkout Repository 14 | uses: actions/checkout@v2 15 | - name: Download jq 16 | run: sudo apt-get update -y && sudo apt-get install -y jq 17 | - name: Get the Numbers 18 | run: | 19 | echo "JSON_COUNT=$(jq length community_datasets.json)" >> $GITHUB_ENV 20 | - name: JSON counter 21 | uses: schneegans/dynamic-badges-action@v1.1.0 22 | with: 23 | auth: ${{ secrets.GIST_SECRET }} 24 | gistID: 34bc0c1280d475d3a69e3b60a706226e 25 | filename: community.json 26 | label: Community Datasets 27 | message: ${{ env.JSON_COUNT }} 28 | color: orange 29 | -------------------------------------------------------------------------------- /.github/workflows/json_sync.yml: -------------------------------------------------------------------------------- 1 | name: Upload File to GCS on Change 2 | on: 3 | workflow_dispatch: 4 | schedule: 5 | - cron: "0 18 * * *" 6 | jobs: 7 | upload_to_gcs: 8 | runs-on: ubuntu-latest 9 | 10 | steps: 11 | - name: Checkout repository 12 | uses: actions/checkout@v2 13 | 14 | - id: 'auth' 15 | uses: 'google-github-actions/auth@v2' 16 | with: 17 | credentials_json: '${{ secrets.GCP_CREDENTIALS }}' 18 | 19 | - name: Set up Cloud SDK 20 | uses: 'google-github-actions/setup-gcloud@v2' 21 | 22 | - name: Use gcloud CLI 23 | run: 'gcloud info' 24 | 25 | - name: Upload file to GCS using gcloud 26 | run: | 27 | gcloud storage cp community_datasets.jsonl ${{ secrets.GCS_FILE_PATH }} 28 | -------------------------------------------------------------------------------- /CNAME: -------------------------------------------------------------------------------- 1 | gee-community-catalog.org -------------------------------------------------------------------------------- /awesome-gee-catalog-examples.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/samapriya/awesome-gee-community-datasets/edb5998d6c17f76974d25945b01eb52f82614a46/awesome-gee-catalog-examples.zip -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/ACES-BHUTAN: -------------------------------------------------------------------------------- 1 | var Bhutan_ACES_Rice_Maps = ee.ImageCollection("projects/servir-sco-assets/assets/Bhutan/Rice_Extent_Mapper/Predicted_Rice_Post_Processed_IC"); 2 | Map.setCenter(90.37, 27.51,8) 3 | var palettes = require('users/gena/packages:palettes'); 4 | 5 | var snazzy = require("users/aazuspan/snazzy:styles"); 6 | snazzy.addStyle("https://snazzymaps.com/style/132/light-gray", "Grayscale"); 7 | 8 | Map.addLayer(Bhutan_ACES_Rice_Maps,{min: 0,max: 1, palette: ["fee6ce","fdae6b","e6550d"]}, 9 | "ACES Rice Maps 2016-2022 ") -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/AF-CROPLAND-MASK-30M-2016: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var af_cropmask_2016 = ee.Image("projects/sat-io/open-datasets/landcover/AF_Cropland_mask_30m_2016_v3"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var style = require('users/gena/packages:style') 5 | Map.centerObject(af_cropmask_2016) 6 | Map.addLayer(af_cropmask_2016.mask(af_cropmask_2016.neq(1).and(af_cropmask_2016.gt(0))),{palette:'#EAC117'},'Ensemble Cropland Mask 2016') 7 | 8 | style.SetMapStyleDark() -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/CARBON-SECURITY-INDEX: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var csi_great_basin = ee.ImageCollection("projects/sat-io/open-datasets/CSI/Great_Basin"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | 5 | var mosaic = csi_great_basin.mosaic(); 6 | 7 | var palette = [ 8 | 'FF0000', 9 | 'FFA500', 10 | 'FFFF00', 11 | 'ADFF2F', 12 | '008000' 13 | ]; 14 | 15 | var visParams = { 16 | min: -1, 17 | max: 1, 18 | palette: palette 19 | }; 20 | 21 | Map.centerObject(csi_great_basin.first(),6) 22 | 23 | Map.addLayer(mosaic.select('CSI'), visParams, 'Great Basin Carbon Security Index'); 24 | 25 | var snazzy = require("users/aazuspan/snazzy:styles"); 26 | snazzy.addStyle("https://snazzymaps.com/style/15/subtle-grayscale", "Greyscale"); 27 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/ESA-CCI-ABOVEGROUND-BIOMASS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var agb = ee.ImageCollection("projects/sat-io/open-datasets/ESA/ESA_CCI_AGB"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var palette = ["#C6ECAE","#A1D490","#7CB970","#57A751","#348E32", "#267A29","#176520","#0C4E15","#07320D","#031807"]; 5 | 6 | var snazzy = require("users/aazuspan/snazzy:styles"); 7 | snazzy.addStyle("https://snazzymaps.com/style/15/subtle-grayscale", "Greyscale"); 8 | 9 | Map.addLayer(agb.filterDate('2009-01-01','2011-01-01').first().select(['AGB']),{min:1,max:450,palette:palette},'Above Ground Biomass 2010') 10 | Map.addLayer(agb.filterDate('2019-01-01','2021-01-01').first().select(['AGB']),{min:1,max:450,palette:palette},'Above Ground Biomass 2020') 11 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/FIBOA-UK-FIELDS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var uk_fields = ee.FeatureCollection("projects/sat-io/open-datasets/UK-FIELDS"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.centerObject(uk_fields.first(),12) 5 | var empty = ee.Image().byte(); 6 | var outline = empty.paint({ 7 | featureCollection: uk_fields, 8 | color: 'random', 9 | width: 3 10 | }); 11 | 12 | Map.addLayer(outline.randomVisualizer(), {opacity:0.8}, 'UK Fields') 13 | Map.setOptions("SATELLITE") -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/FOREST-ROADS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var forest_roads = ee.FeatureCollection("projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.setCenter(12.7254,-1.2772,10) 5 | Map.addLayer(forest_roads,{},'Forest Roads ') 6 | 7 | Map.setOptions('SATELLITE') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/GCEP-30-CROPLAND-EXTENT: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var countries = ee.FeatureCollection("FAO/GAUL/2015/level0"), 3 | gcep30 = ee.ImageCollection("projects/sat-io/open-datasets/GFSAD/GCEP30"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | /* 6 | Class Label Name Description 7 | 0 Water Water bodies/no-data 8 | 1 Non-Cropland Non-Cropland areas 9 | 2 Cropland Cropland areas 10 | */ 11 | 12 | var vis = {min:0,max:2,palette:["0050cb","d78956","21d911"]} 13 | Map.addLayer(gcep30.mosaic().clip(countries),vis,'GCEP30') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/GEESEBAL-ET-SOUTH-AMERICA: -------------------------------------------------------------------------------- 1 | var dataset = ee.ImageCollection('projects/et-brasil/assets/geesebal/myd11a2/sa/v0-02') 2 | .filter(ee.Filter.date('2018-05-01', '2018-10-01')); 3 | var et = dataset.select('ET_24h'); 4 | var etVis = { 5 | min: 0.0, 6 | max: 6000.0, 7 | palette: ['#650000','#b02323','#d35454','#cc9e99','#e6e2ac','#99accc','#5676d6','#2222ab','#380061'] 8 | 9 | }; 10 | Map.setCenter(-50, -25, 3); 11 | Map.addLayer( 12 | et, etVis, 13 | 'Daily Evapotranspiration (mm/day)'); 14 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/GFCC30TC-TREE-CANOPY-COVER: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var GFCC30TC = ee.ImageCollection("projects/sat-io/open-datasets/GFCC30TC"), 3 | countries = ee.FeatureCollection("FAO/GAUL/2015/level0"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | print(GFCC30TC) 6 | var vis = ['#CCFFCC','#99FF99','#66FF66','#33CC33','#009900','#006600','#003300','#000000']; 7 | 8 | Map.addLayer(GFCC30TC.filterDate('2000-01-01','2001-12-31').mosaic().clip(countries),{min:0,max:150,palette:vis},'GFCC30TC 2000') 9 | Map.addLayer(GFCC30TC.filterDate('2015-01-01','2015-12-31').mosaic().clip(countries),{min:0,max:150,palette:vis},'GFCC30TC 2015') 10 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/GLOBAL-10m-CANOPY-HEIGHT: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var canopy_vis = {"min":0,"max":50,"palette":["#010005","#150b37","#3b0964","#61136e","#85216b","#a92e5e","#cc4248","#e75e2e","#f78410","#fcae12","#f5db4c","#fcffa4"]}, 3 | sd_vis = {"min":0,"max":15,"palette":["#0d0406","#241628","#36274d","#403a76","#3d5296","#366da0","#3488a6","#36a2ab","#44bcad","#6dd3ad","#aee3c0","#def5e5"]}, 4 | canopy_height = ee.Image("users/nlang/ETH_GlobalCanopyHeight_2020_10m_v1"), 5 | standard_deviation = ee.Image("users/nlang/ETH_GlobalCanopyHeightSD_2020_10m_v1"); 6 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 7 | Map.addLayer(canopy_height, canopy_vis, 'Canopy top height'); 8 | Map.addLayer(standard_deviation, sd_vis, 'Standard deviation'); 9 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/GLOBAL-ESI-10KM: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get first image 2 | var esi_4wk_ic = ee.ImageCollection('projects/climate-engine/esi/4wk') 3 | var esi_4wk_i = esi_4wk_ic.filterDate('2020-08-01', '2020-08-10').first() 4 | var esi_12wk_ic = ee.ImageCollection('projects/climate-engine/esi/12wk') 5 | var esi_12wk_i = esi_12wk_ic.filterDate('2020-08-01', '2020-08-10').first() 6 | 7 | // Print first image to see bands 8 | print(esi_4wk_i) 9 | print(esi_12wk_i) 10 | 11 | // Visualize select bands from first image — additional bands are present in the Image Collection 12 | var esi_palette = ["#0000aa", "#0000ff", "#00aaff", "#00ffff", "#aaff55", "#ffffff", "#ffff00", "#fcd37f", "#ffaa00", "#e60000", "#730000"] 13 | Map.addLayer(esi_4wk_i.select('ESI'), {min: -2.5, max: 2.5, palette: esi_palette}, 'ESI_4wk') 14 | Map.addLayer(esi_12wk_i.select('ESI'), {min: -2.5, max: 2.5, palette: esi_palette}, 'ESI_12wk') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/GLOBAL-FOREST-CANOPY-HT-GEDI-LANDSAT: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gf = ee.ImageCollection("projects/sat-io/open-datasets/GLAD/GEDI_V27"), 3 | gbf = ee.ImageCollection("projects/sat-io/open-datasets/GLAD/GEDI_V25_Boreal"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var comb = gf.merge(gbf).mosaic() 6 | gf = gf.mosaic().unmask() 7 | gbf = gbf.mosaic().unmask() 8 | 9 | var visParam = {min:0,max:30,palette:'white, #006600'}; 10 | Map.addLayer(gf.updateMask(gf.gte(3)),visParam,'Forest Canopy Height'); 11 | var visParamB = {min:0,max:30,palette:'white, #004d4d'}; 12 | Map.addLayer(gbf.updateMask(gbf.gte(3)),visParamB,'Forest Canopy Height Boreal'); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/GLOBAL-IRRIGATION-AREAS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var vis_2001 = {"opacity":1,"bands":["constant"],"palette":["2d50ff"]}, 3 | vis_2010 = {"opacity":1,"bands":["constant"],"palette":["119708"]}; 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var irrigation_maps = ee.ImageCollection("users/deepakna/global_irrigation_maps") 6 | print(irrigation_maps) 7 | 8 | var highly_irrigated_areas_2010 = ee.Image(irrigation_maps.filter(ee.Filter.date('2010-01-01','2010-12-31')).first()) 9 | .expression("b(0) == 2 ? 1 : 0"); 10 | 11 | Map.addLayer(highly_irrigated_areas_2010.updateMask(highly_irrigated_areas_2010.neq(0)),vis_2010,'highly_irrigated_areas_2010') 12 | 13 | var highly_irrigated_areas_2001 = ee.Image("users/deepakna/global_irrigation_maps/2001") 14 | .expression("b(0) == 2 ? 1 : 0"); 15 | 16 | Map.addLayer(highly_irrigated_areas_2001.updateMask(highly_irrigated_areas_2001.neq(0)),vis_2001,'highly_irrigated_areas_2001') 17 | 18 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/GLOBAL-NATURAL-PLANTED-FORESTS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var global_forest_types = ee.ImageCollection("projects/sat-io/open-datasets/GLOBAL-NATURAL-PLANTED-FORESTS"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var image = global_forest_types.mosaic() 5 | 6 | var maskedImage = image.updateMask( 7 | image.select('b1').neq(127) 8 | .or(image.select('b2').neq(127)) 9 | .or(image.select('b3').neq(127)) 10 | ); 11 | Map.addLayer(maskedImage,{min:0,max:127},'Masked Global Natural(Green) & Planted(Yellow) Trees') 12 | 13 | var snazzy = require("users/aazuspan/snazzy:styles"); 14 | snazzy.addStyle("https://snazzymaps.com/style/71079/dark", "Dark"); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/LANDFIRE-DISTURBANCE: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var fdist = ee.ImageCollection("projects/sat-io/open-datasets/landfire/disturbance/FDIST"), 3 | hdist = ee.ImageCollection("projects/sat-io/open-datasets/landfire/disturbance/HDIST"), 4 | distyear = ee.ImageCollection("projects/sat-io/open-datasets/landfire/disturbance/DISTYEAR"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | //Import palette 7 | var palettes = require('users/gena/packages:palettes') 8 | 9 | //Disturbance Layers 10 | Map.addLayer(fdist.mosaic(),{'min':0,'max':260,palette: palettes.cmocean.Deep[7]},'Fuel Disturbance') 11 | Map.addLayer(hdist.mosaic(),{'min':0,'max':20078388,palette: palettes.cmocean.Haline[7]},'Historical Disturbance') 12 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/LANDFIRE-FIRE-REGIME: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var vcc = ee.ImageCollection("projects/sat-io/open-datasets/landfire/fire-regime/vcc"), 3 | vdep = ee.ImageCollection("projects/sat-io/open-datasets/landfire/fire-regime/vdep"), 4 | sclass = ee.ImageCollection("projects/sat-io/open-datasets/landfire/fire-regime/sclass"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | 7 | //Import palette 8 | var palettes = require('users/gena/packages:palettes') 9 | 10 | // Fire Regime 11 | Map.addLayer(vcc,{'min':2,'max':174,palette: palettes.colorbrewer.Dark2[7]},'Vegetation Condition Class') 12 | Map.addLayer(vdep,{'min':24,'max':175,palette: palettes.cmocean.Haline[7]},'Vegetation Departure Index') 13 | Map.addLayer(sclass,{'min':2,'max':174,palette: palettes.cmocean.Amp[7]},'Succession Classes') 14 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/LANDFIRE-TOPOGRAPHIC: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var asp = ee.ImageCollection("projects/sat-io/open-datasets/landfire/TOPOGRAPHY/ASP"), 3 | elev = ee.ImageCollection("projects/sat-io/open-datasets/landfire/TOPOGRAPHY/ELEV"), 4 | slpd = ee.ImageCollection("projects/sat-io/open-datasets/landfire/TOPOGRAPHY/SLPD"), 5 | slpp = ee.ImageCollection("projects/sat-io/open-datasets/landfire/TOPOGRAPHY/SLPP"); 6 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 7 | //Import palette 8 | var palettes = require('users/gena/packages:palettes') 9 | 10 | // Topographic 11 | Map.addLayer(elev.mosaic().mask(elev.mosaic().neq(32767)),{'min':2,'max':3500,palette: palettes.cmocean.Amp[7]},'Elevation') 12 | Map.addLayer(asp.mosaic().mask(asp.mosaic().neq(32767)),{'min':2,'max':174,palette: palettes.cmocean.Haline[7]},'Aspect') 13 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/LANDFIRE-TRANSPORTATION: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var imageCollection = ee.ImageCollection("projects/sat-io/open-datasets/landfire/transportation/ROADS"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | //Add roads Layer 5 | 6 | /* 7 | 20 Primary road 8 | 21 Secondary road 9 | 22 Tertiary road 10 | 23 Thinned road 11 | */ 12 | 13 | Map.addLayer(imageCollection.mosaic().updateMask(imageCollection.mosaic().neq(0)),{min:20,max:23,palette: ['#f7f7f7','#cccccc','#969696','#525252']},'Roads') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/LANDFIRE-VEGETATION: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var evc = ee.ImageCollection("projects/sat-io/open-datasets/landfire/VEGETATION/EVC"), 3 | evh = ee.ImageCollection("projects/sat-io/open-datasets/landfire/VEGETATION/EVH"), 4 | evt = ee.ImageCollection("projects/sat-io/open-datasets/landfire/VEGETATION/EVT"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | //Import palette 7 | var palettes = require('users/gena/packages:palettes'); 8 | 9 | Map.addLayer(evc.mosaic(),{'min':11,'max':356,palette: palettes.colorbrewer.YlGnBu[8]},'Existing Vegetation Cover') 10 | Map.addLayer(evh.mosaic(),{'min':11,'max':301,palette: palettes.colorbrewer.RdYlGn[8]},'Existing Vegetation Height') 11 | Map.addLayer(evt.mosaic(),{'min':7067,'max':9038,palette: palettes.cmocean.Matter[7]},'Existing Vegetation Type') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/LGRIP-30-CROPLAND: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var countries = ee.FeatureCollection("FAO/GAUL/2015/level0"), 3 | lgrip30 = ee.ImageCollection("projects/sat-io/open-datasets/GFSAD/LGRIP30"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | 6 | /* 7 | Class Label Name Description 8 | 0 Ocean Ocean and Water bodies 9 | 1 Non-croplands Land with other land use 10 | 2 Irrigated croplands Agricultural croplands that are irrigated 11 | 3 Rainfed croplands Agricultural croplands that are rainfed 12 | */ 13 | 14 | 15 | var vis = {min:0,max:3,palette:["#0050cb","#6e462c"," #cccc66","#306466"]} 16 | Map.addLayer(lgrip30.mosaic().clip(countries),vis,'LGRIP30') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/LGRIP-30-CROPLAND-EXTENT: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var countries = ee.FeatureCollection("FAO/GAUL/2015/level0"), 3 | lgrip30 = ee.ImageCollection("projects/sat-io/open-datasets/GFSAD/LGRIP30"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | 6 | /* 7 | Class Label Name Description 8 | 0 Ocean Ocean and Water bodies 9 | 1 Non-croplands Land with other land use 10 | 2 Irrigated croplands Agricultural croplands that are irrigated 11 | 3 Rainfed croplands Agricultural croplands that are rainfed 12 | */ 13 | 14 | 15 | var vis = {min:0,max:3,palette:["0050cb","d58855","c2d30c","379a4b"]} 16 | Map.addLayer(lgrip30.mosaic().clip(countries),vis,'LGRIP30') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/PS-AFRICA-TREECOVER: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var tree_cover = ee.Image("projects/sat-io/open-datasets/PS_AFRICA_TREECOVER_2019_100m_V10"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var palette= ["#00FF00", "#1AFF1A", "#33FF33", "#4DFF4D", "#66FF66", "#80FF80", "#99FF99", "#B3FFB3", "#CCFFCC", "#E6FFE6"] 5 | 6 | 7 | Map.addLayer(tree_cover,{min:0,max:100,palette:palette},'PS Tree Cover Percentage') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/QDANN-30M-YIELD-MAPS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var corn_soybean = ee.ImageCollection("projects/sat-io/open-datasets/lobell-lab/VAE_QDANN_YIELD_MAP/CORN_SOY_MAP"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | /** 5 | Modified GEE code to demostrate the QDANN Yield Map (corn and soybean) 6 | Band info: b1: corn; b2: soybean 7 | Unit: kg/ha 8 | Author: Samapriya Roy 9 | Date: 2024-09-22 10 | **/ 11 | 12 | var vis = {bands: ['b1'], min: 4000, max: 16000, palette: ['black', 'blue', 'green', 'yellow' ,'red']} 13 | 14 | 15 | var mosaic_2008 = corn_soybean.filterDate('2008-01-01','2008-12-31') 16 | .mosaic(); 17 | 18 | var mask = mosaic_2008.select('b1').neq(0) 19 | var masked = mosaic_2008.updateMask(mask) 20 | 21 | Map.setCenter(-89.0926, 41.18, 10) 22 | Map.addLayer(masked, vis, 'Corn Yield Map 2008 in kg/ha') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/US-FRET: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get single image 2 | var fret_ic = ee.ImageCollection('projects/climate-engine/fret/forecast/eto') 3 | var fret_i = fret_ic.first() 4 | 5 | // Print image to see bands 6 | print(fret_i) 7 | 8 | // Visualize a single image 9 | var fret_palette = ["#ffffb2", "#fed976", "#feb24c", "#fd8d3c", "#fc4e2a", "#e31a1c", "#b10026"] 10 | Map.addLayer(fret_i, {min:0, max:10, palette: fret_palette}, 'fret_i') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/US-TILE-DRAINED-CROPLANDS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var tile30m = ee.Image("projects/sat-io/open-datasets/agtile/AgTile-US"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.addLayer(tile30m) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/agriculture-vegetation-forestry/VODCA: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var cband = ee.ImageCollection("projects/sat-io/open-datasets/VODCA/C-BAND"), 3 | kband = ee.ImageCollection("projects/sat-io/open-datasets/VODCA/K-BAND"), 4 | xband = ee.ImageCollection("projects/sat-io/open-datasets/VODCA/X-BAND"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | var vis = ['#a50026','#d73027','#f46d43','#fdae61','#fee08b','#ffffbf','#d9ef8b','#a6d96a','#66bd63','#1a9850','#006837'] 7 | 8 | Map.addLayer(cband.filterDate('2005-01-01','2005-12-31').limit(10).median().select('b1'),{palette:vis},'C-Band 2005 subset') 9 | Map.addLayer(xband.filterDate('2005-01-01','2005-12-31').limit(10).median().select('b1'),{palette:vis},'X-Band 2005 subset') 10 | Map.addLayer(kband.filterDate('2005-01-01','2005-12-31').limit(10).median().select('b1'),{palette:vis},'K-Band 2005 subset') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/analysis-ready-data/HYSPECNET-11K: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var hyspecnet = ee.ImageCollection("projects/sat-io/open-datasets/HySpecNet/HYSPECNET-11K"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(hyspecnet.size()) 5 | 6 | //Remove invalid bands 7 | var invalid_bands = ['B126', 'B127', 'B128', 'B129', 'B130', 'B131', 'B132', 'B133', 'B134', 'B135', 'B136', 'B137', 'B138', 'B139', 'B140', 'B160', 'B161', 'B162', 'B163', 'B164', 'B165', 'B166'] 8 | 9 | //Select an image 10 | var image = hyspecnet.limit(500).sort('system:time_start',false).first() 11 | image = image.select(image.bandNames().removeAll(invalid_bands)) 12 | print('Resolution',image.select(['B1']).projection().nominalScale()) 13 | print('Band Names',image.bandNames()) 14 | 15 | //Add image as layer 16 | Map.centerObject(image,12) 17 | Map.addLayer(image,{"opacity":1,"bands":["B3","B2","B1"],"min":-154,"max":934,"gamma":1},'Sample HYSPECNET Image Chip') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/analysis-ready-data/OPEN-AERIAL-MAP: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var oam_subset = ee.ImageCollection("projects/sat-io/open-datasets/open-aerial-map"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | //Shuffle the image collection to fetch random data 5 | var collection = oam_subset.toList(oam_subset.size()) 6 | 7 | //Fetch any item from list 8 | var image = ee.Image(collection.get(8)) 9 | Map.centerObject(image,14) 10 | print('Image detail',image) 11 | 12 | // Add imagery 13 | Map.addLayer(ee.Image(image),{},image.get('platform').getInfo()+' data') 14 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/analysis-ready-data/SRER-HIGHRES-DRONE: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var full_ortho_srer_may_2019_1cm = ee.Image("users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_srer_may_2019_1cm"), 3 | full_ortho_srer_sept_2019_1cm = ee.Image("users/gponce/usda_ars/assets/images/aes/srer/suas/2019/full_ortho_srer_sept_2019_1cm"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.addLayer(full_ortho_srer_may_2019_1cm,{},'Ortho May 2019 @ SRER') 6 | Map.addLayer(full_ortho_srer_sept_2019_1cm,{},'Ortho Sept. 2019 @ SRER',false) 7 | 8 | Map.setCenter(-110.85754, 31.8068,13) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/analysis-ready-data/SWISSTOPO-S2-SR-HARMONIZED: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var vis = {"opacity":1,"bands":["B8","B3","B2"],"min":99,"max":6895,"gamma":1}; 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var collection = ee.ImageCollection("projects/satromo-prod/assets/col/S2_SR_HARMONIZED_SWISS") 5 | Map.centerObject(collection.first(),8) 6 | 7 | print(collection.aggregate_histogram('pixel_size_meter')) 8 | 9 | var collection_20m = collection.filter(ee.Filter.eq('pixel_size_meter',20)) 10 | var collection_10m = collection.filter(ee.Filter.neq('pixel_size_meter',20)) 11 | 12 | Map.addLayer(collection_10m.filterDate('2024-07-01','2024-07-30').median(),vis,'Collection Mosaic Month') 13 | 14 | var snazzy = require("users/aazuspan/snazzy:styles"); 15 | snazzy.addStyle("https://snazzymaps.com/style/38/shades-of-grey", "Greyscale"); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/analysis-ready-data/USGS-3DEP-CONSOLIDATED-SURVEY-CHECKPOINTS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var survey_checkpoints = ee.FeatureCollection("projects/sat-io/open-datasets/USGS/SURVEY_CHECKPOINTS_3DEP_2010_2017"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.addLayer(survey_checkpoints) 5 | 6 | var snazzy = require("users/aazuspan/snazzy:styles"); 7 | snazzy.addStyle("https://snazzymaps.com/style/65217/grey", "Greyscale"); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/analysis-ready-data/USGS-HISTORICAL-AERIAL-IMAGERY: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var conusWest_imagery = ee.ImageCollection("projects/wlfw-um/assets/historical-imagery/conus-west"), 3 | conusWest_metadata = ee.FeatureCollection("projects/wlfw-um/assets/historical-imagery/conus-west-seamlines"), 4 | conusWest_metadata_with_date = ee.FeatureCollection("projects/sat-io/open-datasets/wlfm-um-extra/wlfm-um-seamlines"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | Map.setCenter(-108.3563, 42.8912, 5) 7 | 8 | //Get count of features in each year 9 | print(conusWest_metadata_with_date.aggregate_histogram('year')) 10 | 11 | Map.addLayer(conusWest_imagery, {}, 'Historical Imagery', true) 12 | Map.addLayer(conusWest_metadata, {}, 'Historical Imagery - metadata', false) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/biodiversity-ecosystems-habitat/BIODIVERSITY-INTACTNESS-INDEX: -------------------------------------------------------------------------------- 1 | var bdi_ic = ee.ImageCollection("projects/ebx-data/assets/earthblox/IO/BIOINTACT") 2 | var bdi_2017_20_comp = bdi_ic.mean() 3 | var bdi_2017_comp = bdi_ic.filterDate('2017-01-01', '2017-12-31').mean() 4 | 5 | 6 | var visualization = { 7 | bands: ['BioIntactness'], 8 | min: 0, 9 | max: 1, 10 | palette: ['e5f5e0', 'a1d99b', '31a354'], 4: ['edf8e9', 'bae4b3', '74c476', '238b45'] 11 | }; 12 | 13 | Map.addLayer(bdi_2017_20_comp, visualization, "composite 2017-20") 14 | Map.addLayer(bdi_2017_comp, visualization, "composite 2017") -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/elevation-bathymetry/ASTWBD: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var ASTWBD = ee.Image("projects/sat-io/open-datasets/ASTER/ASTWBD_ATT"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | //Import module to set map style 5 | var style = require('users/gena/packages:style') 6 | 7 | //3 class only with land removed 8 | var palette = ["0d3ce2","4075e7","50B2C4"] 9 | 10 | //Mask out land and add water bodies only 11 | Map.addLayer(ASTWBD.updateMask(ASTWBD.gte(1)),{min:1,max:3,palette:palette},'ASTER Global Water Bodies Database (ASTWBD) Version 1') 12 | style.SetMapStyleGrey() -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/elevation-bathymetry/DELTA-DTM: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var delta_dtm = ee.Image("projects/sat-io/open-datasets/DELTARES/deltadtm_v1"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var elevation = delta_dtm.select('b1'); 5 | elevation = elevation.updateMask(elevation.neq(10)); 6 | 7 | //Setup basemaps 8 | var snazzy = require("users/aazuspan/snazzy:styles"); 9 | snazzy.addStyle("https://snazzymaps.com/style/132/light-gray", "Grayscale"); 10 | 11 | var elevationVis = { 12 | min: 0, 13 | max: 10.0, 14 | // cmocean deep 15 | palette: ["281a2c", "3f396c", "3e6495", "488e9e", "5dbaa4", "a5dfa7", "fdfecc"] 16 | }; 17 | 18 | Map.setCenter(103, 0, 7); // South East Asia 19 | Map.addLayer(elevation, elevationVis, 'DeltaDTM'); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/elevation-bathymetry/GEBCO: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gebco_grid = ee.ImageCollection("projects/sat-io/open-datasets/gebco/gebco_grid"), 3 | gebco_sub_ice_topo = ee.ImageCollection("projects/sat-io/open-datasets/gebco/gebco_sub-ice-topo"), 4 | gebco_tid_grid = ee.ImageCollection("projects/sat-io/open-datasets/gebco/gebco_tid_grid"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | //Import palette 7 | var palettes = require('users/gena/packages:palettes') 8 | 9 | var elevationVis = { 10 | min: -7000.0, 11 | max: 3000.0, 12 | palette: ['011de2', 'afafaf', '3603ff', 'fff477', 'b42109'], 13 | }; 14 | Map.setCenter(-37.62, 25.8, 2); 15 | Map.addLayer(gebco_grid.median(), elevationVis, 'Elevation'); 16 | Map.addLayer(gebco_sub_ice_topo.median(), {min:-7500,max:5000,palette: ['011de2', 'afafaf', '3603ff', 'fff477', 'b42109']},'Elevation Sub Ice Topo' ); 17 | Map.addLayer(gebco_tid_grid,{'min':0,'max':70,palette: palettes.colorbrewer.RdYlBu[11]},'Elevation TID',false) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/elevation-bathymetry/ICELAND-DEM-10m: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var DEM = ee.Image("projects/ee-landmaelingar/assets/IslandsDEMv1_10m_isn93"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(DEM); 5 | 6 | var elevationVis = { 7 | min: -30.0, 8 | max: 1200.0, 9 | palette: ["#317605","f5cf13","b45504","#ffffff"], 10 | }; 11 | 12 | Map.setCenter(-18.52, 64.81, 7); 13 | Map.addLayer(DEM, elevationVis, 'IslandsDEMv1 10m isn93'); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/elevation-bathymetry/NOAA-SLR-DEM: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var slrdem = ee.ImageCollection("projects/sat-io/open-datasets/NOAA/SLR_DEM"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(slrdem.size()) 5 | 6 | //Function to attach nominal scale to collection 7 | var scales = function(image){ 8 | var b1proj = image.select('b1').projection(); 9 | var b1scale = image.select('b1').projection().nominalScale(); 10 | return image.set('scale',ee.Number(b1scale).round()) 11 | } 12 | 13 | var ns = slrdem.map(scales) 14 | 15 | //print distribution of nominal scale across collection 16 | print(ns.aggregate_histogram('scale')) 17 | 18 | //filter by nominal scale 19 | var ns_3m =ns.filter(ee.Filter.eq('scale',3)) 20 | var image = ns_3m.mosaic().setDefaultProjection('EPSG:3857',null,3) 21 | Map.addLayer(image.updateMask(image.gt(-9999))) 22 | Map.addLayer(ee.Terrain.products(image)) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/elevation-bathymetry/TINITALY-DTM-10m: -------------------------------------------------------------------------------- 1 | var DEM = ee.ImageCollection("projects/sat-io/open-datasets/Tinitaly_DTM") 2 | print(DEM); 3 | 4 | var elevationVis = { 5 | min: -30.0, 6 | max: 1200.0, 7 | palette: ["#317605","f5cf13","b45504","#ffffff"], 8 | }; 9 | 10 | Map.setCenter(13.58, 42.386, 7); 11 | Map.addLayer(DEM, elevationVis, 'Tinitaly 10m'); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/fire-monitoring-analysis/GLOBAL-ANNUAL-BURNED-AREA-MAPS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gabam = ee.ImageCollection("projects/sat-io/open-datasets/GABAM"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var year_2018 = gabam.filterDate('2018-01-01','2018-12-31') 5 | print(year_2018.size()) 6 | 7 | Map.addLayer(year_2018.mosaic(),{min:1,max:1,palette:"f00c0c"},'GABAM Year 2018') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/fire-monitoring-analysis/GLOBAL-FIRE-WEATHER-DB: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get first image 2 | var merra2_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-merra2_fwi-daily') 3 | var merra2_i = merra2_ic.filterDate('2020-08-01', '2020-08-05').first() 4 | 5 | // Print first image to see bands 6 | print(merra2_i) 7 | 8 | // Visualize select bands from first image — additional bands are present in the Image Collection 9 | var fwi_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse() 10 | Map.addLayer(merra2_i.select('FWI'), {min: 0, max: 100, palette: fwi_palette}, 'FWI') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/fire-monitoring-analysis/MONITORING-TRENDS-BURN-SEVERITY: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var area_boundaries = ee.FeatureCollection("projects/sat-io/open-datasets/MTBS/burned_area_boundaries"), 3 | fire_occurrence = ee.FeatureCollection("projects/sat-io/open-datasets/MTBS/fire_occurrence"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.addLayer(fire_occurrence,{'color':'red'},'MTBS Fire Occurrence') 6 | Map.addLayer(area_boundaries,{'color':'orange'},'MTBS burned area boundaries') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/geophysical-biological-biogeochemical/BARE_EARTH_SPECTRA: -------------------------------------------------------------------------------- 1 | var bare_surface = ee.Image('users/geocis/BareSurfaces/BS_1980_2019'); 2 | var bare_frequency = ee.Image('users/geocis/BareSurfaces/BF_1980_2019'); 3 | 4 | //Import palette 5 | var palettes = require('users/gena/packages:palettes') 6 | 7 | Map.setCenter(0,0,3); 8 | Map.addLayer(bare_surface,{bands: ['red', 'green', 'blue'], min: 500, max: 3500, gamma: 1.25},'Bare Surface') 9 | Map.addLayer(bare_frequency.divide(ee.Number(100)),{min: 0, max:100, palette: palettes.matplotlib.magma[7]},'Bare Surface Frequency') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/geophysical-biological-biogeochemical/GEOMORPHO90-GEOMORPHOLOGICAL-FORMS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var geom = ee.ImageCollection("projects/sat-io/open-datasets/Geomorpho90m/geom"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | //Import palette 5 | var palettes = require('users/gena/packages:palettes') 6 | 7 | //Center Map 8 | Map.setCenter(91.1709, 29.3025,10) 9 | 10 | //Add Layers 11 | Map.addLayer(geom.median(),{min: 0.01, max: 273.18, palette: palettes.cmocean.Turbid[7]},'Geomorphon') 12 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/geophysical-biological-biogeochemical/GLOBAL-WATER-SALINITY: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var groundwater = ee.FeatureCollection("projects/sat-io/open-datasets/global_water_salinity/groundwaters_database"), 3 | rivers = ee.FeatureCollection("projects/sat-io/open-datasets/global_water_salinity/rivers_database"), 4 | lakes_reservoir = ee.FeatureCollection("projects/sat-io/open-datasets/global_water_salinity/lakes_reservoirs_database"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | print('Groundwater database size',groundwater.size()) 7 | print('Rivers database size',rivers.size()) 8 | print('Lakes database size',lakes_reservoir.size()) 9 | 10 | Map.addLayer(groundwater,{'color':'red','opacity':0.5},'Ground Database') 11 | Map.addLayer(rivers,{'color':'blue','opacity':0.5},'Rivers Database') 12 | Map.addLayer(lakes_reservoir,{'color':'brown','opacity':0.5},'Lakes and Reservoirs Database') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/geophysical-biological-biogeochemical/SOIL-NEMATODE-ABUNDANCE: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var nematode = ee.FeatureCollection("projects/sat-io/open-datasets/global-nematode"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.addLayer(nematode) 5 | print(nematode.size()) 6 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/geophysical-biological-biogeochemical/SOIL-ORGANIC-CARBON-SA: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var SOC30_mean = ee.ImageCollection("projects/sat-io/open-datasets/NINA/SOC30_SA_mean"), 3 | SOC30_trend = ee.ImageCollection("projects/sat-io/open-datasets/NINA/SOC30_SA_trend"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var palettes = require('users/gena/packages:palettes') 6 | 7 | Map.setCenter(25.654, -29.558,6) 8 | Map.addLayer(SOC30_mean.median().divide(100),{min:0.86,max:16,palette: palettes.cmocean.Algae[7]},'SOC median') 9 | Map.addLayer(SOC30_trend.median().divide(100),{min:-20,max:24,palette: palettes.cmocean.Curl[7]},'SOC trend') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-events-layers/GEOCODED-DISASTERS-DATASET: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gdis = ee.FeatureCollection("projects/sat-io/open-datasets/gdis_1960-2018"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(gdis.size()) 5 | Map.addLayer(gdis) 6 | 7 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-events-layers/GLOBAL-LANDSLIDE-CATALOG: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var glc = ee.FeatureCollection("projects/sat-io/open-datasets/events/global_landslide_1970-2019"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print('Global Landslide Catalog 1970-2019',glc.size()) 5 | Map.addLayer(glc,{},'Landslide Catalog Events 1985-2016') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-events-layers/GLOBAL-LARGE-FLOOD-EVENTS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var flood_events = ee.FeatureCollection("projects/sat-io/open-datasets/events/large_flood_events_1985-2016"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print('Total events 1985-2016',flood_events.size()) 5 | Map.addLayer(flood_events,{},'Flood Events 1985-2016') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-events-layers/SEA-OF-JAPAN-EQ-2024: -------------------------------------------------------------------------------- 1 | var notoPeninsula = ee.ImageCollection("projects/sat-io/open-datasets/disaster/japan-earthquake-2024_ALOS"); 2 | 3 | var dates = notoPeninsula.distinct('system:time_start').aggregate_array('system:time_start') 4 | .map(function(feature){return ee.Date(feature)}).aside(print); 5 | 6 | var before = notoPeninsula.filterDate('2024-01-01','2024-01-04'); 7 | var after = notoPeninsula.filterDate('2022-01-01','2024-01-01'); 8 | 9 | Map.addLayer(before,{min: 0.0,max: 10000.0},'before'); 10 | Map.addLayer(after,{min: 0.0,max: 10000.0},'after'); 11 | Map.setCenter(137.1901, 36.6159); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-events-layers/UMBRA-OPENDATA: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var umbra_open = ee.ImageCollection("projects/sat-io/open-datasets/UMBRA/open-data"), 3 | notoPeninsula = ee.ImageCollection("projects/sat-io/open-datasets/disaster/japan-earthquake-2024_UMBRA"), 4 | vis = {"opacity":1,"bands":["b1"],"min":12,"max":160,"gamma":1}; 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | Map.centerObject(notoPeninsula.first(),14) 7 | Map.addLayer(umbra_open.first(),vis,'Umbra sample',false) 8 | Map.addLayer(notoPeninsula.first(),vis,'Noto Peninsula sample') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-events-layers/URBAN-SKY-OPENDATA: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var HIGHRES_RGB = ee.Image("projects/sat-io/open-datasets/URBAN_SKY/USKY01_D00236_010_11S_20250119_20250125200840_A0_000_000_01"), 3 | LWIR_THERMAL = ee.Image("projects/sat-io/open-datasets/URBAN_SKY/USKY02_D00235_400_11S_20250119_20250121015227_A2_000_000_01"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.centerObject(LWIR_THERMAL,10) 6 | Map.addLayer(HIGHRES_RGB.mask(HIGHRES_RGB.gt(0)),{},'High Res RGB 10cm') 7 | Map.addLayer(LWIR_THERMAL, {min: 3, max: 215, palette: ['#313695', '#4575b4', '#74add1', '#abd9e9', '#fee090', '#fdae61', '#f46d43', '#d73027']}, 'LWIR Thermal 3m') 8 | 9 | var snazzy = require("users/aazuspan/snazzy:styles"); 10 | snazzy.addStyle("https://snazzymaps.com/style/15/subtle-grayscale", "Greyscale"); 11 | 12 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-landuse-landcover/CloudSEN12-FOOTPRINT: -------------------------------------------------------------------------------- 1 | var regions = ee.FeatureCollection("projects/sat-io/open-datasets/cloudsen12/footprint") 2 | 3 | var palette = ee.Dictionary({ 4 | 'high': 'red', 5 | 'scribble': 'blue', 6 | 'nolabel': 'green', 7 | }); 8 | 9 | var image = regions 10 | .map(function (feature) { 11 | // Add a style-dictionary property using our chosen palette 12 | return feature.set('myStyle', { 13 | 'color': palette.get(feature.get('lbl_typ')), 14 | 'pointSize': 1 15 | }); 16 | }) 17 | .style({ 18 | 'styleProperty': 'myStyle', 19 | }); 20 | 21 | Map.centerObject(image) 22 | Map.addLayer(image, {}); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-landuse-landcover/DAYLIGHT-LAND-WATER-POLY: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var water_polygons = ee.FeatureCollection("projects/sat-io/open-datasets/DAYLIGHTMAP/water_polygons"), 3 | land_polygons = ee.FeatureCollection("projects/sat-io/open-datasets/DAYLIGHTMAP/land_polygons"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.setCenter(-34.14,37.6,4) 6 | 7 | Map.addLayer( 8 | water_polygons.style({ 9 | fillColor: '00000000', 10 | color: 'blue', 11 | }), 12 | {}, 13 | 'Water Polygons' 14 | ); 15 | 16 | Map.addLayer( 17 | land_polygons.style({ 18 | fillColor: '00000000', 19 | color: 'brown', 20 | }), 21 | {}, 22 | 'Land Polygons' 23 | ); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-landuse-landcover/GLANCE-TRAINING: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var glance_training = ee.FeatureCollection("projects/sat-io/open-datasets/GLANCE/GLANCE_TRAINING_DATA_V1"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(glance_training.size()) 5 | 6 | print(glance_training.first()) 7 | 8 | Map.addLayer(glance_training) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-landuse-landcover/GLOBAL-MANGROVE-CANOPY-HT-TANDEMX: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var mangrove_tandemx_12 = ee.ImageCollection("projects/sat-io/open-datasets/GLOBAL_MANGROVE_HT_TANDEMX"), 3 | mangrove_gedi = ee.FeatureCollection("projects/space-geographer/assets/GEDI_MANGROVE_HT"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var palettes = require('users/gena/packages:palettes') 6 | var snazzy = require("users/aazuspan/snazzy:styles"); 7 | snazzy.addStyle("https://snazzymaps.com/style/38/shades-of-grey", "Greyscale"); 8 | 9 | Map.setCenter(103.4919, -0.4812,10) 10 | Map.addLayer(mangrove_tandemx_12.mosaic(),{'min':1,'max':22,'palette':palettes.colorbrewer.YlGn[9]},'Global Mangrove Height TANDEMX') 11 | Map.addLayer(mangrove_gedi.style({'color':'red'}),{},'Mangrove GEDI point') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-landuse-landcover/GLOBAL-PEATLAND-DATABASE: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var peatland_db = ee.Image("projects/sat-io/open-datasets/GLOBAL-PEATLAND-DATABASE"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | // Load administrative boundaries for Indonesia 5 | var admin1 = ee.FeatureCollection("projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1"); 6 | var geometry = admin1.filter(ee.Filter.eq('shapeGroup', 'IDN')); 7 | 8 | //Map.centerObject(geometry, 4); 9 | Map.setOptions("Hybrid"); 10 | 11 | var peat = peatland_db //.clip(geometry) 12 | 13 | //.clip(geometry) 14 | .unmask(); 15 | 16 | // Display the results 17 | Map.addLayer(peat.clip(geometry), 18 | {min: 0, max: 1, palette: ['#f7fcf5', '#c7e9c0', '#74c476', '#238b45', '#00441b']}, 19 | 'Peatland Distribution', true 20 | ); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-landuse-landcover/GLOBAL-PEATLAND-FRACTIONAL-COVER: -------------------------------------------------------------------------------- 1 | // Load administrative boundaries for Indonesia 2 | var admin1 = ee.FeatureCollection("projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1"); 3 | var geometry = admin1.filter(ee.Filter.eq('shapeGroup', 'IDN')); 4 | 5 | Map.centerObject(geometry, 4); 6 | Map.setOptions("Hybrid"); 7 | 8 | var peat = ee.Image("projects/sat-io/open-datasets/ML-GLOBAL-PEATLAND-EXTENT") 9 | .clip(geometry) 10 | .unmask(); 11 | 12 | // Display the results 13 | Map.addLayer(peat.clip(geometry), 14 | {min: 0, max: 100, palette: ['#f7fcf5', '#c7e9c0', '#74c476', '#238b45', '#00441b']}, 15 | 'Peatland Distribution', true 16 | ); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-landuse-landcover/RANDOLPH-GLACIER-INVENTORY: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var glacier_ft = ee.FeatureCollection("projects/sat-io/open-datasets/RGI/RGI_VECTOR_MERGED_V7"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.centerObject(glacier_ft.first()) 5 | 6 | // print a feature 7 | print('first glacier feature', glacier_ft.first()); 8 | 9 | // Make a raster image out of the land area attribute. 10 | var glacier_img = glacier_ft.reduceToImage({ 11 | properties: ['area_km2'], 12 | reducer: ee.Reducer.first() 13 | }); 14 | 15 | // Make a binary mask 16 | var glacier_binary = glacier_img.gt(0).unmask(); 17 | 18 | //Add layers 19 | Map.addLayer(glacier_binary, {min:0, max:1}, 'Glacier raster mask',false); 20 | Map.addLayer(glacier_ft,{color:'#368BC1'},'Glacier Features') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-landuse-landcover/WORLD-SETTLEMENT-FOOTPRINT-IDC: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var wsf_evo_idc = ee.ImageCollection("projects/sat-io/open-datasets/WSF/WSF_EVO_IDC"), 3 | vis = {"opacity":1,"bands":["b30"],"max":6,"gamma":1}; 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | print('Collection size',wsf_evo_idc.size()); 6 | 7 | print('Band size',wsf_evo_idc.first().bandNames().size()) 8 | print('Band list b1:1985 to b31:2015',wsf_evo_idc.first().bandNames()) 9 | 10 | var palette = ['#d73027','#fc8d59','#fee08b','#d9ef8b','#91cf60','#1a9850'] 11 | 12 | Map.addLayer(wsf_evo_idc.mosaic().select('b30'),{min:1,max:6,'palette':palette},'B30 IDC') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/FACEBOOK-ELECTRICAL-DIST-GRID-MAPS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gmv_raster = ee.ImageCollection("projects/sat-io/open-datasets/facebook/global_medium_voltage_grid"), 3 | gmv_vector = ee.FeatureCollection("projects/sat-io/open-datasets/facebook/gmv_grid"), 4 | vis = {"opacity":1,"bands":["b1"],"min":1,"palette":["be2dac"]}; 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | Map.centerObject(gmv_raster.first(),10) 7 | 8 | // Fire Regime 9 | Map.addLayer(gmv_raster,vis,'Medium Voltage Grid') 10 | Map.addLayer(gmv_vector,{},'Medium Voltage Grid: Vector') 11 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-CARBON-OFFSET-PROJECTS: -------------------------------------------------------------------------------- 1 | var carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL'); 2 | 3 | var visParams = { 4 | palette: ['#9ab555'], 5 | min: 0.0, 6 | max: 1550000.0, 7 | opacity: 0.8, 8 | }; 9 | var carbonoffsets = ee.Image().float().paint(carbonoffsetscol, 'REP_AREA'); 10 | 11 | Map.setCenter(-52.692,-2.628,6) 12 | Map.addLayer(carbonoffsets, visParams, 'Existing carbon projects area'); 13 | 14 | var snazzy = require("users/aazuspan/snazzy:styles"); 15 | snazzy.addStyle("https://snazzymaps.com/style/15/subtle-grayscale", "Greyscale"); 16 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-CEMENT-PRODUCTION-ASSETS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var global_cement = ee.FeatureCollection("projects/sat-io/open-datasets/SFI/global_cement_database_20210701"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(global_cement.first()) 5 | 6 | Map.addLayer(global_cement,{'color':'red'},'Global Cement Database') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-CEMENT-SUPP-PROD-DB: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var assets_db = ee.FeatureCollection("projects/sat-io/open-datasets/SFI/global_cement_db_assets_20231004"), 3 | suppliers_producers_db = ee.FeatureCollection("projects/sat-io/open-datasets/SFI/global_cement_db_suppliers_20231004"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | //Let's get property names for a single asset in the databases 6 | print(assets_db.first().propertyNames()) 7 | print(suppliers_producers_db.first().propertyNames()) 8 | 9 | //Size estimates are helpful too 10 | print('Assets DB size',assets_db.size()) 11 | print('Suppliers& Producers DB size',suppliers_producers_db.size()) 12 | 13 | //Let's add these to the code editor map 14 | Map.addLayer(assets_db,{'color':'red'},'Assets Database') 15 | Map.addLayer(suppliers_producers_db,{'color':'blue'},'Supplier & Producers Database') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-COMBINED-BUILDING-FOOTPRINTS-VIDA: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var ind = ee.FeatureCollection("projects/sat-io/open-datasets/VIDA_COMBINED/IND"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.setCenter(79.088,21.145,14) 5 | 6 | Map.addLayer( 7 | ee.FeatureCollection(ind).style({ 8 | fillColor: '00000000', 9 | color: '#964B00', 10 | }),{},'Combined Buildings India' 11 | ); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-FIXED-MOBILE-NETWORK-PERFORMANCE: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var mobile_20200101 = ee.FeatureCollection("projects/sat-io/open-datasets/network/mobile_tiles/2020-01-01_performance_mobile_tiles"), 3 | fixed_20200101 = ee.FeatureCollection("projects/sat-io/open-datasets/network/fixed_tiles/2020-01-01_performance_fixed_tiles"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | print('Fixed Performance Metrics',fixed_20200101.size()) 6 | print('Mobile Performance Metrics',mobile_20200101.size()) 7 | Map.addLayer(fixed_20200101,{'color':'blue'},'Fixed 2020-01-01') 8 | Map.addLayer(mobile_20200101,{'color':'red'},'Mobile 2020-01-01') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-HEALTHSITES-MAPPING-PROJECT: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var node = ee.FeatureCollection("projects/sat-io/open-datasets/health-site-node"), 3 | way = ee.FeatureCollection("projects/sat-io/open-datasets/health-site-way"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | print('Total Sites Nodes: Global Healthsites Mapping Project',node.size()) 6 | print('Total Sites Ways: Global Healthsites Mapping Project',way.size()) 7 | 8 | Map.addLayer(node,{},'Global Healthsites Mapping Project: Nodes') 9 | Map.addLayer(way,{},'Global Healthsites Mapping Project: Ways') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-IRON-STEEL-PRODUCTION-ASSETS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var global_steel = ee.FeatureCollection("projects/sat-io/open-datasets/SFI/global_steel_database_20210701"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(global_steel.first()) 5 | 6 | Map.addLayer(global_steel,{'color':'red'},'Global Iron and Steel Database') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-MINING-AND-VALIDATION: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var mining = ee.FeatureCollection("projects/sat-io/open-datasets/global-mining/global_mining_polygons"), 3 | validation = ee.FeatureCollection("projects/sat-io/open-datasets/global-mining/global_mining_validation"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.addLayer(ee.FeatureCollection(mining).style({fillColor: '00000000',color: 'af8dc3',width:3}),{},'Mining Polygons') 6 | Map.addLayer(ee.FeatureCollection(validation).style({color: 'FC8D59',width:4}),{},'Mining Validation Points') 7 | 8 | Map.setOptions('SATELLITE') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-MINING-FOOTPRINTS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var mining_footprints = ee.FeatureCollection("projects/sat-io/open-datasets/global-mining/global_mining_footprints"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.centerObject(mining_footprints.first(),9) 5 | Map.addLayer(ee.Image().paint(mining_footprints,0,3), {"palette":["#d73027"]},'Global Mining Footprints') 6 | Map.setOptions('SATELLITE') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-NPP-VIIRS-LIKE-NTL: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var npp_viirs_ntl = ee.ImageCollection("projects/sat-io/open-datasets/npp-viirs-ntl"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var image = npp_viirs_ntl.sort('system:time_start').first() 5 | var vis = {"opacity":1,"bands":["b1"],min: 1, max: 32,"palette":["584d9f","9c79c1","c98cbe","f2d192","e2ee82"]} 6 | var formattedDate = ee.Date(image.get('system:time_start')).format('YYYY-MM-dd'); 7 | Map.addLayer(image.mask(image.neq(0)),vis,'NTL VIIRS '+formattedDate.getInfo()) 8 | 9 | var image = npp_viirs_ntl.sort('system:time_start',false).first() 10 | var formattedDate = ee.Date(image.get('system:time_start')).format('YYYY-MM-dd'); 11 | Map.addLayer(image.mask(image.neq(0)),vis,'NTL VIIRS '+formattedDate.getInfo()) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/GLOBAL-OFFSHORE-WIND-TURBINES: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gowt = ee.FeatureCollection("projects/sat-io/open-datasets/global_offshore_wind_turbine_v1-3"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | 5 | Map.setCenter(30.290751107548992,26.158890852053972,3) 6 | Map.addLayer(gowt, {"palette":"FF0000"}, 'Global Offshore Wind Turbines') 7 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/HARMONIZED-WIND-SOLAR-FARMS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var wind_farms = ee.FeatureCollection("projects/sat-io/open-datasets/global_wind_farms_2020"), 3 | solar_farms = ee.FeatureCollection("projects/sat-io/open-datasets/global_solar_farms_2020"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | print('Total Harmonized Global Windfarms 2020',wind_farms.size()) 6 | print('Total Harmonized Global solarfarms 2020',solar_farms.size()) 7 | 8 | Map.addLayer(wind_farms,{'color':'blue'},'Global Harmonized Dataset: Wind farms') 9 | Map.addLayer(solar_farms,{'color':'red'},'Global Harmonized Dataset: Solar farms') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/MS-GLOBAL-ROADS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var oceania = ee.FeatureCollection("projects/sat-io/open-datasets/MSRoads/Oceania"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.centerObject(oceania.first(),9) 5 | 6 | Map.addLayer(oceania.style({color: '800080',width:0.75}),{},'Oceania Microsoft Roads') 7 | var style = require('users/gena/packages:style') 8 | style.SetMapStyleGrey() -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/NSI: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var buildings = ee.FeatureCollection("projects/sat-io/open-datasets/OVERTURE/BUILDINGS/CONUS-EXTRACT"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.addLayer(buildings.style({color:'blue'}), {}, 'Buildings CONUS Extract'); 5 | 6 | var nsi_wy = ee.FeatureCollection('projects/sat-io/open-datasets/NSI/nsi_2022_WY'); 7 | Map.centerObject(nsi_wy.first(),16) 8 | Map.addLayer(nsi_wy.style({color:'red'}), {}, 'NSI'); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/OVERTURE-BUILDINGS-EXTRACT: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var buildings = ee.FeatureCollection("projects/sat-io/open-datasets/OVERTURE/BUILDINGS/CONUS-EXTRACT"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.setCenter(-90.1887, 38.6255,15) 5 | Map.addLayer(buildings, {color:'blue'}, 'Buildings CONUS Extract'); 6 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/global-utilities-assets-amenities/PREDICTED-GLOBAL-POWER-SYSTEMS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var transmission = ee.FeatureCollection("projects/sat-io/open-datasets/predictive-global-power-system/distribution-transmission-lines"), 3 | lv = ee.Image("projects/sat-io/open-datasets/predictive-global-power-system/lv"), 4 | targets = ee.Image("projects/sat-io/open-datasets/predictive-global-power-system/targets"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | Map.addLayer(ee.FeatureCollection(transmission).style({color: 'FC8D59',width:1}),{},'Predicted Distribution and Transmission Lines') 7 | Map.addLayer(lv,{},'Predicted low-voltage infrastructure in kilometres per cell') 8 | Map.addLayer(targets,{},'Locations predicted to be connected to distribution grid') 9 | 10 | Map.setOptions('TERRAIN') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/GLOBAL-CHANNEL-BELT: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gcb = ee.Image("projects/sat-io/open-datasets/GCB/GRMM"), 3 | env = ee.Image("projects/sat-io/open-datasets/GCB/Env"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var visParams = { 6 | bands: ['Braided', 'Braided', 'Meander'], 7 | min: 0, 8 | max: 100, 9 | gamma: 2.0, 10 | }; 11 | 12 | var colors = {min:1,max:6, palette:['#d4a334','#1bff00','#fff700','#1b00ff','#10fff4','#50c7ff']}; 13 | 14 | var mask = env.gt(1);// Do not map background value 15 | 16 | Map.addLayer(gcb.unmask(0),visParams,'ML Prediction',true); 17 | Map.addLayer(env.mask(mask),colors,'Environments',false); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/GLOBAL-COASTAL-RIVERS-ENV-VARIABLES: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var global_costal_rivers = ee.FeatureCollection("projects/sat-io/open-datasets/delta/global-costal-rivers-points"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | function buffer_collection(ft) { 5 | var buffered = ft.buffer(2000);//buffer distance in meter 6 | return buffered; 7 | } 8 | print(global_costal_rivers.size()); 9 | print(global_costal_rivers.first()) 10 | Map.addLayer( 11 | ee.FeatureCollection(global_costal_rivers.map(buffer_collection)).style({ 12 | fillColor: '00000000', // transparent 13 | color: '#191919', 14 | }),{},'Global coastal rivers and environmental variables' 15 | ); 16 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/GLOBAL-GEOREF-DATABASE-DAMS: -------------------------------------------------------------------------------- 1 | var catchments = ee.FeatureCollection("projects/sat-io/open-datasets/GOODD/GOOD2_catchments"); 2 | var dams = ee.FeatureCollection("projects/sat-io/open-datasets/GOODD/GOOD2_dams"); 3 | 4 | Map.addLayer(ee.Image().paint(catchments,0,3), {"palette":["008000"]}, 'GOODD Catchments') 5 | Map.addLayer(dams, {"palette":"FF0000"}, 'GOODD Dams') 6 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/GLOBAL-HIGHRES-FLOODPLAINS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gfplain250 = ee.ImageCollection("projects/sat-io/open-datasets/GFPLAIN250"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(gfplain250); 5 | 6 | var pal = ["#000080", "#0000ff", "#0063ff", "#00d4ff", "#4effa9", "#a9ff4e", "#ffe600", "#ff7d00", "#ff1400", "#800000"] 7 | 8 | Map.addLayer(gfplain250.mosaic(),{palette:"#002B4D"},'Global Flood Plain 250m') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/GLOBAL-HYDROLOGIC-CURVE-NUMBER: -------------------------------------------------------------------------------- 1 | 2 | // Import the GCN250 Images and rename bands 3 | var GCN250_Average = ee.Image("users/jaafarhadi/GCN250/GCN250Average").select('b1').rename('average'); 4 | var GCN250_Dry = ee.Image("users/jaafarhadi/GCN250/GCN250Dry").select('b1').rename('dry'); 5 | var GCN250_Wet = ee.Image("users/jaafarhadi/GCN250/GCN250Wet").select('b1').rename('wet'); 6 | 7 | // visualize the Dry GCN dataset 8 | var vis = { 9 | min:40, 10 | max:75, 11 | palette: ['Red','SandyBrown','Yellow','LimeGreen', 'Blue','DarkBlue'] 12 | 13 | }; 14 | 15 | Map.addLayer(GCN250_Dry, vis, 'CN Dry'); 16 | 17 | Map.setCenter(17.93, 7.71, 2); 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/GLOBAL-RIVER-CLASSIFICATION(GLORIC): -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gloric = ee.FeatureCollection("projects/sat-io/open-datasets/GloRiC/GloRiC_v10"), 3 | gloric_canada = ee.FeatureCollection("projects/sat-io/open-datasets/GloRiC/GloRiC_Canada_v10"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | print(gloric.first()); 6 | Map.addLayer(gloric,{color: 'blue'}, 'GloRiC global') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/GLOBAL-RIVER-DELTAS-VULNERABILITY: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var convex_hull = ee.FeatureCollection("projects/sat-io/open-datasets/delta/delta-convex-hull"), 3 | convex_hull_bound = ee.FeatureCollection("projects/sat-io/open-datasets/delta/delta-convex-bounds"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.addLayer(ee.FeatureCollection(convex_hull),{},'Delta Convex Hull'); 6 | Map.addLayer(ee.FeatureCollection(convex_hull_bound),{},'Delta Convex Hull Bounds'); 7 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/GLOBathy: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var globathy = ee.Image("projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry"), 3 | globathy_param = ee.FeatureCollection("projects/sat-io/open-datasets/GLOBathy/GLOBathy_basic_parameters"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var palettes = require('users/samapriya/utils:palettes'); 6 | 7 | // Use these visualization parameters, customized by location. 8 | var visParams = {min: 1, max: 700, palette: palettes.extra.blkred}; 9 | 10 | // Note that the visualization image doesn't require visualization parameters. 11 | Map.addLayer(globathy, visParams, 'Globathy Bathymetry (m)'); 12 | Map.addLayer(globathy_param,{},'GLOBathy Basic Param',false) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/HRES-INLAND-WB-NA: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var wbd = ee.FeatureCollection("projects/sat-io/open-datasets/HYDRO/wbd_fixed_geoms"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.setCenter(-107.0832, 59.341, 10) 5 | Map.addLayer(wbd, {"color":"#003366"}, "N. American boreal Water Body Dataset") -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/HYDROGRAPHY90-FLOW-INDEX: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var cti = ee.ImageCollection("projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/cti"), 3 | spi = ee.ImageCollection("projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/spi"), 4 | sti = ee.ImageCollection("projects/sat-io/open-datasets/HYDROGRAPHY90/flow_index/sti"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | var style = require('users/gena/packages:style') 7 | var palettes = require('users/samapriya/utils:palettes'); 8 | 9 | Map.setCenter(-16.786, 19.082,3); 10 | 11 | style.SetMapStyleGrey() 12 | 13 | //Add with scaling applied 14 | Map.addLayer(cti.mosaic().divide(1e8),{min:-3.554,max:3.161,palette: palettes.extra.blkred}, 'CTI'); 15 | Map.addLayer(spi.mosaic().divide(1e3),{min:-78.418,max:83.271,palette: palettes.extra.blkredwht}, 'SPI'); 16 | Map.addLayer(sti.mosaic().divide(1e3),{min:-0.552,max:0.792,palette: palettes.extra.orngblue},'STI') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/HYDROLAKES: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var lake_poly = ee.FeatureCollection("projects/sat-io/open-datasets/HydroLakes/lake_poly_v10"), 3 | lake_points = ee.FeatureCollection("projects/sat-io/open-datasets/HydroLakes/lake_points_v10"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.addLayer(lake_poly,{'color':'#00008B'},'HydroLakes Polygons v1.0'); 6 | Map.addLayer(lake_points,{'color':'red','opacity':0.1},'HydroLakes Points v1.0',false) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/HYDROWASTE: -------------------------------------------------------------------------------- 1 | // Visualization for HydroWASTE 2 | var table = ee.FeatureCollection("projects/sat-io/open-datasets/HydroWaste/HydroWASTE_v10"); 3 | 4 | 5 | var typeColor = ee.Dictionary({ 6 | 'Primary': 'fc8d59', 7 | 'Secondary': '542788', 8 | 'Advanced': 'fdb863', 9 | }); 10 | 11 | 12 | var type = ['Primary', 'Secondary', 'Advanced']; 13 | 14 | function addStyle(pt) { 15 | var size = ee.Number(pt.get('WASTE_DIS')).sqrt().divide(100); 16 | var color = typeColor.get(pt.get('LEVEL')); 17 | return pt.set('styleProperty', ee.Dictionary({'pointSize': size, 'color': color})); 18 | } 19 | 20 | var pp = ee.FeatureCollection(table).map(addStyle); 21 | Map.addLayer(pp.filter(ee.Filter.eq('LEVEL', 'Primary')).style({styleProperty: 'styleProperty'}), {}, 'Primary', true,0.65); 22 | Map.addLayer(pp.filter(ee.Filter.eq('LEVEL', 'Secondary')).style({styleProperty: 'styleProperty'}), {}, 'Secondary', true,0.65); 23 | Map.addLayer(pp.filter(ee.Filter.eq('LEVEL', 'Advanced')).style({styleProperty: 'styleProperty'}), {}, 'Advanced', true,0.65); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/OSM-WATER-SURFACE: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var osm_water = ee.ImageCollection("projects/sat-io/open-datasets/OSM_waterLayer"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var vis={min: 1, max: 5, palette: ["08306b","08519c","2171b5","4292c6","6baed6"]}; 5 | Map.addLayer(osm_water.median(),vis,'OSM Water Global') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/RealSAT-GLOBAL-RESERVOIRS-LAKES: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var realsat = ee.FeatureCollection("projects/sat-io/open-datasets/ReaLSAT/ReaLSAT-1_4"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.addLayer( 5 | realsat.style({ 6 | fillColor: '00000000', 7 | color: '008D97', 8 | }),{},'RealSAT' 9 | ); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/SWORD-NODES-REACHES: -------------------------------------------------------------------------------- 1 | var ee_nodes = ee.data.listAssets("projects/sat-io/open-datasets/SWORD/nodes"); 2 | var ee_reaches = ee.data.listAssets("projects/sat-io/open-datasets/SWORD/reaches"); 3 | 4 | print('Total of '+ee.List(ee_nodes.assets).size().getInfo()+ ' assets in nodes',ee_nodes.assets); 5 | print('Total of '+ee.List(ee_reaches.assets).size().getInfo()+ ' reaches in nodes',ee_reaches.assets); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/hydrology/SWORD-NODES-REACHES-MERGED: -------------------------------------------------------------------------------- 1 | var nodes_merged = ee.FeatureCollection("projects/sat-io/open-datasets/SWORD/nodes_merged"); 2 | var reaches_merged = ee.FeatureCollection("projects/sat-io/open-datasets/SWORD/reaches_merged"); 3 | 4 | // Define different styles for each FeatureCollection 5 | var nodeStyle = { 6 | color: 'red', 7 | width: 2 8 | }; 9 | 10 | var reachStyle = { 11 | color: 'blue', 12 | width: 1 13 | }; 14 | 15 | // Add each FeatureCollection with its own style 16 | Map.addLayer(nodes_merged, nodeStyle, 'Nodes'); 17 | Map.addLayer(reaches_merged, reachStyle, 'Reaches'); 18 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/oceans-shorelines/ARGOFLOAT-SUBSET: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var argo = ee.FeatureCollection("projects/sat-io/open-datasets/argo-subset"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print('Total Features',argo.size()) 5 | print('Distinct Platform Id: ',argo.aggregate_count_distinct('pid')) 6 | 7 | Map.addLayer(argo,{},'Argo Float Subset') 8 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/oceans-shorelines/GLOBAL-GRIDDED-SST: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var sstg = ee.ImageCollection("projects/sat-io/open-datasets/sstg"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(sstg.size()) 5 | Map.setCenter(-28.54, 42.92,3) 6 | 7 | var cmap = ["#000080", "#0000ff", "#0063ff", "#00d4ff", "#4effa9", "#a9ff4e", "#ffe600", "#ff7d00", "#ff1400", "#800000"] 8 | 9 | var addyear = function(image){ 10 | return image.set('year',image.date().get('year')).copyProperties(image) 11 | } 12 | 13 | print(sstg.map(addyear).aggregate_histogram('year')) 14 | Map.addLayer(sstg.sort('system:index',false).first(),{min:-2,max:34,palette:cmap},'SSTG Sample') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/oceans-shorelines/GLOBAL_SHORELINES: -------------------------------------------------------------------------------- 1 | var mainlands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/mainlands'); 2 | var big_islands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/big_islands'); 3 | var small_islands = ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/small_islands'); 4 | 5 | Map.setCenter(-4.843, 51.392,6) 6 | Map.addLayer(ee.Image().paint(mainlands,0,3), {"palette":["008000"]}, 'Shoreline Mainlands') 7 | Map.addLayer(ee.Image().paint(big_islands,0,3), {"palette":["0000FF"]}, 'Shoreline Big Islands') 8 | Map.addLayer(ee.Image().paint(small_islands,0,3), {"palette":["FF0000"]}, 'Shoreline Small Islands') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/oceans-shorelines/PLASTIC-INPUT-RIVERS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var plastic = ee.FeatureCollection("projects/sat-io/open-datasets/open-ocean/river_plastic_emissions"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(plastic.toList(20)) 5 | 6 | function addStyle(pt) { 7 | var size = ee.Number(pt.get('mpw')).sqrt().divide(2000).add(1); 8 | return pt.set('styleProperty', ee.Dictionary({'pointSize': size, 'color': '#0EA7A5','opacity':0.4})) 9 | } 10 | var sampler = plastic.map(addStyle) 11 | print(sampler.first()) 12 | Map.addLayer(sampler.style({styleProperty: 'styleProperty'}),{},'River Plastic Emissions',true,0.65) 13 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/AMD0-EDGEMATCHED: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var osm = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/OSM_adm0_polygons"), 3 | usgs = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/USGS_adm0_polygons"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.addLayer(ee.Image().paint(osm,0,3), {"palette":["#d73027"]}, 'OSM boundaries') 6 | Map.addLayer(ee.Image().paint(usgs,0,3), {"palette":["#fc8d59"]}, 'USGS boundaries') 7 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/COD-EDGEMATCHED: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var adm1_cod = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm1_cod"), 3 | adm2_cod = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm2_cod"), 4 | adm3_cod = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm3_cod"), 5 | adm4_cod = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm4_cod"); 6 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 7 | Map.addLayer(ee.Image().paint(adm1_cod,0,3), {"palette":["#d73027"]}, 'ADM1 COD') 8 | Map.addLayer(ee.Image().paint(adm2_cod,0,3), {"palette":["#fc8d59"]}, 'ADM2 COD',false) 9 | Map.addLayer(ee.Image().paint(adm3_cod,0,3), {"palette":["#b35806"]}, 'ADM3 COD',false) 10 | Map.addLayer(ee.Image().paint(adm4_cod,0,3), {"palette":["#01665e"]}, 'ADM4 COD',false) 11 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/CRITICAL-INF-SPATIAL-INDEX(CISI): -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var global_CISI = ee.Image("projects/sat-io/open-datasets/CISI/global_CISI"), 3 | infrastructure = ee.ImageCollection("projects/sat-io/open-datasets/CISI/amount_infrastructure"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | //Import palette 6 | var palettes = require('users/samapriya/utils:palettes'); 7 | 8 | print(infrastructure.aggregate_array('id_no')) 9 | 10 | Map.addLayer(global_CISI,{min:0,max:0.2,palette: palettes.extra.greens},'Global CISI') 11 | Map.addLayer(infrastructure.filter(ee.Filter.eq('id_no','hospital')),{min:0,max:50,palette:palettes.extra.orngblue},'Hospitals') 12 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/GAUL-2024: -------------------------------------------------------------------------------- 1 | var GAUL_2024_L1 = ee.FeatureCollection("projects/sat-io/open-datasets/FAO/GAUL/GAUL_2024_L1"); 2 | var GAUL_2024_L2 = ee.FeatureCollection("projects/sat-io/open-datasets/FAO/GAUL/GAUL_2024_L2"); 3 | 4 | var level1Style = { 5 | fillColor: '00000000', 6 | color: '000000', 7 | width: 1.5 8 | }; 9 | 10 | var level2Style = { 11 | fillColor: '00000000', 12 | color: '000000', 13 | width: 1 14 | }; 15 | 16 | Map.addLayer(GAUL_2024_L1.style(level1Style), {}, 'GAUL 2024 Level 1'); 17 | Map.addLayer(GAUL_2024_L2.style(level2Style), {}, 'GAUL 2024 Level 2'); 18 | 19 | Map.setCenter(10.535, 39.465, 6); 20 | 21 | var snazzy = require("users/aazuspan/snazzy:styles"); 22 | snazzy.addStyle("https://snazzymaps.com/style/132/light-gray", "Grayscale"); 23 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/GLOBAL-ML-BUILDINGS: -------------------------------------------------------------------------------- 1 | var objects = ee.data.listAssets('projects/sat-io/open-datasets/MSBuildings') 2 | print('Assets in MS Global Buildings Footprint Folder', objects['assets']) 3 | 4 | print(ee.FeatureCollection('projects/sat-io/open-datasets/MSBuildings/Australia').size()) 5 | 6 | var feature = ee.FeatureCollection('projects/sat-io/open-datasets/MSBuildings/Australia') 7 | Map.centerObject(feature.first(),6) 8 | Map.addLayer(feature.style({fillColor: '00000000',color: 'FF5500'})),{},'Australia' -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/GPW-v4: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gpw = ee.FeatureCollection("projects/sat-io/open-datasets/sedac/gpw-v4-admin-unit-center-points-population-estimates-rev11"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.addLayer(ee.FeatureCollection(gpw),{},'gpw-v4-admin-center-points-rev11') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/GRIDDED-ELECTRICITY-CONSUMPTION: -------------------------------------------------------------------------------- 1 | var imageCollection = ee.ImageCollection("projects/sat-io/open-datasets/GRIDDED_EC") 2 | 3 | var time_start = imageCollection 4 | .reduceColumns(ee.Reducer.toList(), ['system:time_start']) 5 | .get('list'); 6 | 7 | time_start.evaluate( 8 | 9 | function (time_start) { 10 | 11 | time_start.forEach( 12 | 13 | function (date) { 14 | 15 | var images = imageCollection.filterMetadata('system:time_start', 'equals', date); 16 | 17 | var year = new Date(date).getFullYear(); 18 | 19 | Map.addLayer(images, { 20 | min:0, 21 | max: 10e6, 22 | palette:["black","white","orange","yellow","gold","red",] 23 | }, 'Electricity Consumption(kwh) '+String(year),false); 24 | }); 25 | } 26 | ); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/GRIDDED-ELECTRICITY-CONSUMPTION-GDP: -------------------------------------------------------------------------------- 1 | var imageCollection = ee.ImageCollection("projects/sat-io/open-datasets/GRIDDED_EC-GDP") 2 | 3 | var time_start = imageCollection 4 | .reduceColumns(ee.Reducer.toList(), ['system:time_start']) 5 | .get('list'); 6 | 7 | time_start.evaluate( 8 | 9 | function (time_start) { 10 | 11 | time_start.forEach( 12 | 13 | function (date) { 14 | 15 | var images = imageCollection.filterMetadata('system:time_start', 'equals', date); 16 | 17 | var year = new Date(date).getFullYear(); 18 | 19 | Map.addLayer(images, { 20 | min:0, 21 | max: 34, 22 | palette:["black","white","orange","yellow","gold","red",] 23 | }, 'GDP US Dollars Millions (2017) '+String(year),false); 24 | }); 25 | } 26 | ); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/HUMANITARIAN-EDGEMATCHED: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var adm1 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm1_polygons"), 3 | adm2 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm2_polygons"), 4 | adm3 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm3_polygons"), 5 | adm4 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm4_polygons"); 6 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 7 | Map.addLayer(ee.Image().paint(adm1,0,3), {"palette":["#d73027"]}, 'ADM1 Humanitarian') 8 | Map.addLayer(ee.Image().paint(adm2,0,3), {"palette":["#fc8d59"]}, 'ADM2 Humanitarian',false) 9 | Map.addLayer(ee.Image().paint(adm3,0,3), {"palette":["#b35806"]}, 'ADM3 Humanitarian',false) 10 | Map.addLayer(ee.Image().paint(adm4,0,3), {"palette":["#01665e"]}, 'ADM4 Humanitarian',false) 11 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/JRC-GHSL-2023: -------------------------------------------------------------------------------- 1 | var smod_vis = {min: 10,max: 26,palette: ['000000', '448564', '70daa4', 'ffffff']}; 2 | var pop_vis = {min: 0.0,max: 125.0,palette: ['060606', '337663', '337663', 'ffffff']}; 3 | Map.setCenter(114.96, 31.13, 4); 4 | for (var i=1975;i<2035;i+=5) { 5 | var GHS_SMOD = ee.Image("projects/sat-io/open-datasets/GHS/GHS_SMOD/GHS_SMOD_E" + i + "_GLOBE_R2023A_54009_1000_V1_0"); 6 | var GHS_POP = ee.Image("projects/sat-io/open-datasets/GHS/GHS_POP/GHS_POP_E" + i + "_GLOBE_R2023A_54009_100_V1_0"); 7 | Map.addLayer(GHS_SMOD.mask(GHS_SMOD.neq(10)),smod_vis,'GHS_SMOD: Degree of Urbanization '+ i,false) 8 | Map.addLayer(GHS_POP,pop_vis,'GHS_POP: Population Count '+ i,false) 9 | } 10 | 11 | 12 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/OPEN-EDGEMATCHED: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var adm1 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-open/adm1_polygons"), 3 | adm2 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-open/adm2_polygons"), 4 | adm3 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-open/adm3_polygons"), 5 | adm4 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-open/adm4_polygons"); 6 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 7 | Map.addLayer(ee.Image().paint(adm1,0,3), {"palette":["#d73027"]}, 'ADM1 Open') 8 | Map.addLayer(ee.Image().paint(adm2,0,3), {"palette":["#fc8d59"]}, 'ADM2 Open',false) 9 | Map.addLayer(ee.Image().paint(adm3,0,3), {"palette":["#b35806"]}, 'ADM3 Open',false) 10 | Map.addLayer(ee.Image().paint(adm4,0,3), {"palette":["#01665e"]}, 'ADM4 Open',false) 11 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/ORNL-US-STRUCTURES: -------------------------------------------------------------------------------- 1 | var objects = ee.data.listAssets('projects/sat-io/open-datasets/ORNL/USA-STRUCTURES') 2 | print('Assets in US Strucutres Folder', objects['assets']) 3 | 4 | 5 | //Selecting DC from the folder list 6 | var feature = ee.FeatureCollection('projects/sat-io/open-datasets/ORNL/USA-STRUCTURES/USA_ST_DC') 7 | Map.centerObject(feature.first(),16) 8 | Map.addLayer(feature.style({fillColor: '00000000',color: 'FF5500'})),{},'DC' 9 | Map.addLayer(feature,{},'DC values only',false) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/POMELO-POP-DENSITY: -------------------------------------------------------------------------------- 1 | // load the population density 2 | var popDensity = ee.Image("projects/sat-io/open-datasets/POMELO/POMELOv1"); 3 | 4 | // Define the inferno color palette 5 | var infernoPalette = [ 6 | '#000004', '#1b0c41', '#4a0c6b', '#781c81', '#a52c7a', '#cf4446', 7 | '#ed721c', '#fb9b06', '#f7d03c', '#fcffa4' 8 | ]; 9 | 10 | // Define visualization parameters. 11 | var visParams = { 12 | min: 0, 13 | max: 450, 14 | palette: infernoPalette, 15 | opacity: 0.7 // 70% transparent 16 | }; 17 | 18 | var snazzy = require("users/aazuspan/snazzy:styles"); 19 | snazzy.addStyle("https://snazzymaps.com/style/132/light-gray", "Grayscale"); 20 | 21 | // Add the population density layer to the map. 22 | Map.addLayer(popDensity, visParams, 'Population Density'); 23 | 24 | // Center map 25 | Map.setCenter(39.2026, -6.1659, 12); 26 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/RELATIVE-WEALTH-INDEX(RWI): -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var rwi = ee.FeatureCollection("projects/sat-io/open-datasets/facebook/relative_wealth_index"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.addLayer( 5 | ee.FeatureCollection(rwi),{},'Relative Wealth Index' 6 | ); 7 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/RURAL-ACCESS-INDEX: -------------------------------------------------------------------------------- 1 | //Use the inaccessibility index to multiply your gridded rural population dataset to obtain the 2 | //distribution of rural population with access to all-season roads 3 | var inaccessibilityindex = ee.Image('projects/sat-io/open-datasets/RAI/raimultiplier'); 4 | Map.addLayer(inaccessibilityindex,{min:0, max:1, 'palette': ['EFC2B3','ECB176','E9BD3A','E6E600','63C600','00A600']}, 'Inaccessibility index'); 5 | 6 | //In order to get the Rural Access Index for any given set of boundaries, get zonal statistics 7 | //for the total rural population and the rural population with access to all-season roads 8 | 9 | var ruralpopulation = ee.Image('projects/sat-io/open-datasets/RAI/ruralpop'); 10 | Map.addLayer(ruralpopulation, {min:0, max:100,'palette': ['FFFFFF', 'ff0000']},'Rural Population'); 11 | 12 | var ruralpopulationwithaccess = ee.Image('projects/sat-io/open-datasets/RAI/ruralpopaccess'); 13 | Map.addLayer(ruralpopulationwithaccess,{min:0, max:100,'palette': ['00A600','63C600','E6E600','E9BD3A','ECB176','EFC2B3']},'Rural Pop w/ Access'); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/SOCIAL-CONNECTEDNESS-INDEX(SCI): -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var sci_user_loc = ee.FeatureCollection("projects/sat-io/open-datasets/facebook/sci_user_loc"), 3 | sci_fr_loc = ee.FeatureCollection("projects/sat-io/open-datasets/facebook/sci_fr_loc"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | Map.addLayer(sci_user_loc, {}, 'SCI User Loc') 6 | Map.addLayer(sci_fr_loc, {}, 'SCI FR Loc') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/population-socioeconomics/WEST_AFRICA-COASTAL-VULN: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var wacvm_pei = ee.Image("projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-pei"), 3 | wacvm_paci = ee.Image("projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-paci"), 4 | wacvm_svi = ee.Image("projects/sat-io/open-datasets/sedac/wacvm-social-vulnerability-indices-svi"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | //Import palette 7 | var palettes = require('users/gena/packages:palettes') 8 | 9 | Map.addLayer(wacvm_svi,{min: 2.31, max: 85.156, palette: palettes.cmocean.Turbid[7]},'WACVM SVM') 10 | Map.addLayer(wacvm_paci,{min: 10.24, max: 82.370, palette: palettes.cmocean.Amp[7]},'WACVM PACI') 11 | Map.addLayer(wacvm_pei,{min: 0.24, max: 100, palette: palettes.cmocean.Matter[7]},'WACVM PEI') 12 | 13 | Map.setOptions('HYBRID') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/regional-landuse-landcover/CCAP-IMPERVIOUS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var CCAP_IMP30 = ee.ImageCollection("projects/sat-io/open-datasets/NOAA/ccap_30m_impervious"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | Map.setCenter(-95.3592289208674,29.744084242227128,11) 5 | 6 | //Import palette 7 | var palettes = require('users/samapriya/utils:palettes'); 8 | 9 | Map.addLayer(CCAP_IMP30,{min:-12,max:60,palette:palettes.extra.blue_silver},'CCAP 30m Impervious') 10 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/regional-landuse-landcover/CCAP-WETLAND-POTENTIAL: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var ccap_wetland_potential = ee.Image("projects/sat-io/open-datasets/NOAA/conus_ccap_wetland_potential"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | 5 | Map.setCenter(-95.3592289208674,29.744084242227128,11) 6 | 7 | //Import palette 8 | var palettes = require('users/samapriya/utils:palettes'); 9 | 10 | Map.addLayer(ccap_wetland_potential.mask(ccap_wetland_potential.gt(0)),{min:1,max:10,palette:palettes.extra.marine},'CCAP Wetland Potential') 11 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/regional-landuse-landcover/CCI-LC-20M-AFRICA: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var esa_cci = ee.Image("projects/sat-io/open-datasets/ESA/ESACCI-LC-L4-LC10-Map-20m-P1Y-2016-v10"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | // Prototype Land Cover Classification at 20m https://2016africalandcover20m.esrin.esa.int/ 5 | 6 | Map.centerObject(esa_cci,4) 7 | 8 | var vis = {min:0,max:10,palette:["#000000","#00a000","#966400","#ffb400","#ffff64","#00dc82","#ffebaf","#fff5d7","#c31400","#ffffff","#0046c8"]} 9 | 10 | Map.addLayer(esa_cci,vis,'CCI LAND COVER-S2 PROTOTYPE LC 20M') 11 | 12 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/regional-landuse-landcover/GLOBAL-INDUSTRIAL-SMALLHOLDER-OIL-PALM: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var oil_palm_2019 = ee.ImageCollection("projects/sat-io/open-datasets/landcover/oil_palm_industrial_smallholder_2019"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | /* 5 | Classes 6 | [1] Industrial closed-canopy oil palm plantations 7 | [2] Smallholder closed-canopy oil palm plantations, and 8 | [3] other land covers/uses that are not closed canopy oil palm. 9 | */ 10 | 11 | //Zoom to a cluster 12 | Map.setCenter(107.1307996323747,1.5664239997609994,7) 13 | 14 | //Import module to set Style 15 | var style = require('users/gena/packages:style') 16 | 17 | //Mask out Class 3 other classes that are not closed canopy oil plam 18 | var oil_palm = oil_palm_2019.mosaic().updateMask(oil_palm_2019.mosaic().neq(3)) 19 | 20 | Map.addLayer(oil_palm,{min:1,max:2,"palette":["ff6218","ff3ba7"]},'Industrial and Small Holder Closed Canopy oil palm plantations') 21 | 22 | style.SetMapStyleDark() -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/regional-landuse-landcover/LCMAP-REFERENCE: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var reference = ee.FeatureCollection("projects/sat-io/open-datasets/LCMAP/LCMAP_CU_20200414_V01_REF"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(reference.size()) 5 | 6 | var buff = function(ft) { 7 | var buffered = ft.buffer(1000) 8 | return buffered 9 | }; 10 | 11 | Map.addLayer(reference.map(buff),{},'LCMAP reference') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/regional-landuse-landcover/OIL-PALM-PLANTATION-LAYERS: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var oil_palm = ee.ImageCollection("projects/sat-io/open-datasets/landcover/oil-palm-plantation-1984_2017"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | /*Citation 5 | Danylo, O., Pirker, J., Lemoine, G. et al. A map of the extent and year of detection of oil palm plantations in Indonesia 6 | Malaysia and Thailand. Sci Data 8, 96 (2021). https://doi.org/10.1038/s41597-021-00867-1 7 | */ 8 | 9 | var palettes = require('users/gena/packages:palettes') 10 | Map.setCenter(108.871, -2.401,7) 11 | 12 | /* 13 | 4 corresponds to the year 1984,the first year oil palm was detected 14 | and each consecutive number represents the next year, i.e., 5 is 1985, 15 | while the maximum value of 37 corresponds to 2017 16 | */ 17 | Map.addLayer(oil_palm.mode(),{min:4,max:37,palette: palettes.cmocean.Turbid[7]},'Oil Palm Plantation 1984-2017') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/regional-landuse-landcover/URBAN-WATCH-CITIES: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var imageCollection = ee.ImageCollection("projects/sat-io/open-datasets/HRLC/urban-watch-cities"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | print(imageCollection.size()) 5 | print('City List',imageCollection.aggregate_histogram('city')) 6 | 7 | //multiband mask 8 | var masked = function(image) { 9 | var b1 = image.select(['b1']); 10 | var b2 = image.select(['b2']); 11 | var b3 = image.select(['b3']); 12 | 13 | var maskb1 = b1.eq(0); 14 | var maskb2 = b2.eq(0); 15 | var maskb3 = b3.eq(0); 16 | 17 | var mask = maskb1.and(maskb2) 18 | .and(maskb3) 19 | .rename('cmask'); 20 | 21 | //Invert mask 22 | return image.addBands(mask) 23 | .updateMask(mask.unmask().not()); 24 | }; 25 | 26 | Map.setCenter(-117.18910446862611,32.76908678504577,12) 27 | Map.addLayer(imageCollection.map(masked).mosaic()) 28 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/regional-landuse-landcover/WEST-AFRICA-LULC: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var wa1975 = ee.Image("projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_1975"), 3 | wa2000 = ee.Image("projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_2000"), 4 | wa2013 = ee.Image("projects/sat-io/open-datasets/wa-datasets/wa_lc_usgs_2013"); 5 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 6 | var vis_wa = {"opacity":1,"min":1,"max":99,"palette":["8400A8","8BAD8B","000080","FFCC99","808000","33CCCC","FFFF96","3366FF","FF99CC","969696","A87000","FF0000","CCFF66","A95CE6","D296E6","A83800","F5A27A","EBC961","28734B","EBDF73","BEFFA6","A6C28C","0A9696","749373","505050","FFFFFF"]}; 7 | Map.addLayer(wa1975,vis_wa,'West Africa Land Cover USGS 1975') 8 | Map.addLayer(wa2000,vis_wa,'West Africa Land Cover USGS 2000') 9 | Map.addLayer(wa2013,vis_wa,'West Africa Land Cover USGS 2013'); -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/soil-properties/ISRIC-SOIL-GRID-250: -------------------------------------------------------------------------------- 1 | var sand_mean = ee.Image('projects/soilgrids-isric/sand_mean') 2 | //print example image metadata and description 3 | print(sand_mean) 4 | 5 | //Add all layer to Map 6 | Map.addLayer(sand_mean.select('sand_0-5cm_mean'),{min: 50, max: 1000,palette: ['5d5851','635a4b','6a5b44','715c3d','785e36','7e5f30','856129','8c6222','92641c','996515','a0660e','a66808','ad6901']},'SoilGrids250m 2.0 - Sand content ISRIC_0_5cm') 7 | 8 | //Set basemap to Hybrid view 9 | Map.setOptions('HYBRID') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/soil-properties/SOIL-BIOCLIM: -------------------------------------------------------------------------------- 1 | // Load image 2 | var SBIO_0_5cm = ee.Image('projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm') 3 | 4 | // Print bandNames 5 | print(SBIO_0_5cm.bandNames()) 6 | 7 | // Add to map 8 | Map.addLayer(SBIO_0_5cm.select('SBIO1_Annual_Mean_Temperature'), 9 | {min: -10, max: 30, palette: ["2166AC", "4393C3", "92C5DE", "D1E5F0", "FDDBC7", "F4A582", "D6604D", "B2182B"]}, 10 | 'SBIO1_Annual_Mean_Temperature') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/CANADA-DROUGHT-OUTLOOK: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get single image 2 | var cdo_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-aafc-cdo-monthly') 3 | var cdo_i = cdo_ic.sort('system:start',false).first() 4 | 5 | // Print image to see bands 6 | print(cdo_i) 7 | 8 | // Visualize a single image 9 | var cdo_palette = ["#ffffff", "#4a7733", "#dfb73d", "#b6a083", "#775412", "#c24d1b"] 10 | Map.addLayer(cdo_i, {min:0, max:4, palette: cdo_palette}, 'cdo_i') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/CE-HRDPA-DAILY: -------------------------------------------------------------------------------- 1 | // Read in Image Collections and get single image 2 | var hrdpa_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-hrdpa-daily') 3 | var hrdpa_i = hrdpa_ic.first() 4 | 5 | // Print single image to see bands 6 | print(hrdpa_i) 7 | 8 | // Visualize precipitation for single image 9 | var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"] 10 | Map.addLayer(hrdpa_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/CE-HRDPS-DAILY: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get first image 2 | var hrdps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-hrdps-daily') 3 | var hrdps_i = hrdps_ic.first() 4 | 5 | // Print first image to see bands 6 | print(hrdps_i) 7 | 8 | // Visualize temperature from first image 9 | var temp_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse() 10 | Map.addLayer(hrdps_i.select('Tavg'), {min: -10, max: 20, palette: temp_palette}, 'Tavg') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/CE-RDPA-DATASETS: -------------------------------------------------------------------------------- 1 | // Read in Image Collections and get single image 2 | var rdpa_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-rdpa-daily') 3 | var rdpa_i = rdpa_ic.first() 4 | 5 | // Print single image to see bands 6 | print(rdpa_i) 7 | 8 | // Visualize precipitation for single image 9 | var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"] 10 | Map.addLayer(rdpa_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/CE-RDPS-DAILY: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get first image 2 | var rdps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-rdps-daily') 3 | var rdps_i = rdps_ic.first() 4 | 5 | // Print first image to see bands 6 | print(rdps_i) 7 | 8 | // Visualize temperature from first image 9 | var temp_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse() 10 | Map.addLayer(rdps_i.select('Tavg'), {min: -10, max: 20, palette: temp_palette}, 'Tavg') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/CHIRPS-PRELIM: -------------------------------------------------------------------------------- 1 | // Read in Image Collections and get single image 2 | var chirps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-chirps-prelim-pentad') 3 | var chirps_i = chirps_ic.first() 4 | 5 | // Print single image to see bands 6 | print(chirps_i) 7 | 8 | // Visualize precipitation for single image 9 | var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"] 10 | Map.addLayer(chirps_i.select('precipitation'), {min: 0, max: 200, palette: prec_palette}, 'precipitation') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/CPC-MORPH: -------------------------------------------------------------------------------- 1 | // Read in Image Collections and get single image 2 | var cmorph_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-cpc-cmorph/daily') 3 | var cmorph_i = cmorph_ic.first() 4 | 5 | // Print single image to see bands 6 | print(cmorph_i) 7 | 8 | // Visualize precipitation for single image 9 | var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"] 10 | Map.addLayer(cmorph_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/ERA5-HEAT: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get first image 2 | var era5_heat_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-era5-heat') 3 | var era5_heat_i = era5_heat_ic.first() 4 | 5 | // Print first image to see bands 6 | print(era5_heat_i) 7 | 8 | // Visualize select bands from first image — additional bands are present in the Image Collection 9 | var temp_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse() 10 | Map.addLayer(era5_heat_i.select('mrt_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Mean Radiant Temperature, Daily Mean') 11 | Map.addLayer(era5_heat_i.select('utci_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Universal Thermal Climate Index, Daily Mean') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/GLOBAL-ET0: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var et_yearly = ee.Image("projects/sat-io/open-datasets/global_et0/global_et0_yearly"), 3 | et_yearly_sd = ee.Image("projects/sat-io/open-datasets/global_et0/global_et0_yearly_sd"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var palettes = require('users/samapriya/utils:palettes'); 6 | 7 | 8 | // Note that the visualization image doesn't require visualization parameters. 9 | Map.addLayer(et_yearly, {min: 1, max: 3000, palette: palettes.extra.blkred}, 'ET Yearly'); 10 | Map.addLayer(et_yearly_sd,{min: 1, max: 100, palette: palettes.MET.Monet},'ET Yearly SD',false) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/GLOBAL-EXTREME-HEAT-HAZARD: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var global_extreme_heat_hazard = ee.ImageCollection("projects/sat-io/open-datasets/WORLD-BANK/global-ext-heat-hazard"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var palette = [ 5 | "#FF0000", 6 | "#FF4000", 7 | "#FF8000", 8 | "#FFBF00", 9 | "#FFFF00", 10 | "#80FF00", 11 | "#00FF00" 12 | ] 13 | 14 | Map.addLayer(global_extreme_heat_hazard.first().updateMask(global_extreme_heat_hazard.first().gt(0)),{min:10,max:40,palette:palette},'Global Extreme Heat Hazard') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/GLOBAL-MOD10A261-Snow-Cover-8-Day: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var mod10a2 = ee.ImageCollection("projects/sat-io/open-datasets/MODIS/MOD10A261"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var mod10a2_2001=mod10a2.filterDate("2001-1-1","2001-12-13") 5 | 6 | var eq200=function(i){ 7 | return i.eq(200) 8 | } 9 | 10 | var mod10a2_2001_sum=mod10a2.select(["snowmax"]).map(eq200). 11 | reduce(ee.Reducer.sum()) 12 | 13 | var palette=['f7fbff','deebf7','c6dbef','9ecae1','6baed6', 14 | '4292c6','2171b5','08519c','08306b'] 15 | 16 | /* 17 | NOTE: This code only produces the number of days with 18 | detected snow. To produce an index of the fraction 19 | of observations that are detected snow, one must 20 | calculate the number of days with valid non-snow 21 | observations. See User Guide for the MOD10A2 Product 22 | */ 23 | 24 | Map.addLayer(mod10a2_2001_sum.updateMask(mod10a2_2001_sum.gt(0)),{min:0,max:42,palette:palette}) -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/GLOBAL-PRECIP-MEASUREMENT: -------------------------------------------------------------------------------- 1 | // Read in Image Collections and get single image 2 | var gpm_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-gpm-imerg-daily') 3 | var gpm_i = gpm_ic.first() 4 | 5 | // Print single image to see bands 6 | print(gpm_i) 7 | 8 | // Visualize precipitation for single image 9 | var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"] 10 | Map.addLayer(gpm_i.select('precipitationCal'), {min: 0, max: 200, palette: prec_palette}, 'precipitationCal') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/GLOBAL-SATELLITE-PM25: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var pm25_monthly = ee.ImageCollection("projects/sat-io/open-datasets/GLOBAL-SATELLITE-PM25/MONTHLY"), 3 | pm25_yearly = ee.ImageCollection("projects/sat-io/open-datasets/GLOBAL-SATELLITE-PM25/ANNUAL"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var scale = function(image){ 6 | return image.multiply(0.1).copyProperties(image, ['system:time_start', 'system:time_end']); 7 | } 8 | 9 | pm25_monthly=pm25_monthly.map(scale) 10 | pm25_yearly=pm25_yearly.map(scale) 11 | 12 | var vis = {bands: ['b1'],min: 0.5, max: 5,palette: ['#a50026','#d73027','#f46d43','#fdae61','#fee090','#ffffbf','#e0f3f8','#abd9e9','#74add1','#4575b4','#313695'].reverse()} 13 | 14 | Map.addLayer(pm25_monthly.first(),vis,'Monthly PM25') 15 | Map.addLayer(pm25_yearly.first(),vis,'Yearly PM25') 16 | 17 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/HXG-CLOUD-COVER: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var hxg = ee.ImageCollection("projects/sat-io/open-datasets/isccp/hxg"); 3 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 4 | var jan = (hxg.filter(ee.Filter.equals('month',1)).filter(ee.Filter.equals('hour',0)).reduce(ee.Reducer.sum()).rename('b1_jan')) 5 | var dec = (hxg.filter(ee.Filter.equals('month',12)).filter(ee.Filter.equals('hour',0)).reduce(ee.Reducer.sum()).rename('b1_dec')) 6 | 7 | //Import palette 8 | var palettes = require('users/gena/packages:palettes') 9 | 10 | Map.addLayer(jan, {min: [150], max: [1000], palette: palettes.cmocean.Curl[7]}, 'HXG January Hour 00'); 11 | Map.addLayer(dec, {min: [150], max: [1000], palette: palettes.cmocean.Balance[7]}, 'HXG December Hour 00'); 12 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/MODIS-GAPFILLED-LST-DAILY: -------------------------------------------------------------------------------- 1 | /**** Start of imports. If edited, may not auto-convert in the playground. ****/ 2 | var gf_day_1km = ee.ImageCollection("projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km"), 3 | gf_night_1km = ee.ImageCollection("projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km"); 4 | /***** End of imports. If edited, may not auto-convert in the playground. *****/ 5 | var palettes = require('users/gena/packages:palettes'); 6 | 7 | //gf indicates the gap-filled LST in the unit of 0.1 Celsius temperature (0.1 degree C) 8 | Map.addLayer(gf_day_1km.first().multiply(0.1),{min:-50,max:50,palette:palettes.cmocean.Balance[7]},' Daytime 1km LST') 9 | Map.addLayer(gf_night_1km.first().multiply(0.1),{min:-50,max:30,palette:palettes.cmocean.Balance[7]},' Nighttime 1km LST') 10 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/NOAA-NRCC-ACIS: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get first image 2 | var acis_nrcc_nn_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-nrcc-acis-nn/daily') 3 | var acis_nrcc_nn_i = acis_nrcc_nn_ic.first() 4 | 5 | // Print first image to see bands 6 | print(acis_nrcc_nn_i) 7 | 8 | // Visualize each band from first image 9 | var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"] 10 | var temp_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse() 11 | Map.addLayer(acis_nrcc_nn_i.select('precip'), {min: 0, max: 0.5, palette: prec_palette}, 'precip') 12 | Map.addLayer(acis_nrcc_nn_i.select('tmin'), {min: -10, max: 50, palette: temp_palette}, 'tmin') 13 | Map.addLayer(acis_nrcc_nn_i.select('tmax'), {min: -10, max: 50, palette: temp_palette}, 'tmax') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/SNODAS-DAILY: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get image 2 | var snodas_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/climate-engine/snodas/daily') 3 | var snodas_i = snodas_ic.filterDate('2022-01-01', '2022-01-05').first() 4 | 5 | // Print first image to see bands 6 | print(snodas_i) 7 | 8 | // Visualize select bands from first image 9 | var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"] 10 | Map.addLayer(snodas_i.select('Snow_Depth'), {min: 0, max: 1, palette: prec_palette}, 'Snow_Depth') 11 | Map.addLayer(snodas_i.select('SWE'), {min: 0, max: 1, palette: prec_palette}, 'SWE') 12 | -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/US-DROUGHT-OUTLOOK: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get single image 2 | var usdo_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-cpc-usdo-monthly') 3 | var usdo_i = usdo_ic.first() 4 | 5 | // Print image to see bands 6 | print(usdo_i) 7 | 8 | // Visualize a single image 9 | 10 | var usdo_palette = ["#ffffff", "#ABA362", "#DACBB5", "#FFD861", "#935743"] 11 | Map.addLayer(usdo_i, {min:0, max:4, palette: usdo_palette}, 'usdo_i') -------------------------------------------------------------------------------- /awesome-gee-catalog-examples/weather-climate/US-SEASONAL-DROUGHT-OUTLOOK: -------------------------------------------------------------------------------- 1 | // Read in Image Collection and get single image 2 | var usdo_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-cpc-usdo-monthly') 3 | var usdo_i = usdo_ic.first() 4 | 5 | // Print image to see bands 6 | print(usdo_i) 7 | 8 | // Visualize a single image 9 | 10 | var usdo_palette = ["#ffffff", "#ABA362", "#DACBB5", "#FFD861", "#935743"] 11 | Map.addLayer(usdo_i, {min:0, max:4, palette: usdo_palette}, 'usdo_i') -------------------------------------------------------------------------------- /docs/CNAME: -------------------------------------------------------------------------------- 1 | gee-community-catalog.org -------------------------------------------------------------------------------- /docs/blog/.authors.yml: -------------------------------------------------------------------------------- 1 | authors: 2 | squidfunk: 3 | name: Martin Donath 4 | description: Creator 5 | avatar: https://avatars.githubusercontent.com/u/932156 6 | url: https://github.com/squidfunk 7 | alexvoss: 8 | name: Alex Voss 9 | description: Community support 10 | avatar: https://avatars.githubusercontent.com/u/4134224 11 | url: https://github.com/alexvoss 12 | -------------------------------------------------------------------------------- /docs/blog/.meta.yml: -------------------------------------------------------------------------------- 1 | comments: true 2 | hide: 3 | - feedback 4 | -------------------------------------------------------------------------------- /docs/blog/index.md: -------------------------------------------------------------------------------- 1 | # Blog 2 | -------------------------------------------------------------------------------- /docs/contributing/bug.md: -------------------------------------------------------------------------------- 1 | # Bug report for dataset in community catalog 2 | 3 | Bug reports are useful information for the catalog. This can range for anything from a spelling mistake that breaks integration to change in asset path that may not have been updated in the documentation for example, incorrect doucentation or citation reference and many more. These are different from dataset updates as they do not pertain to availability of updated data or release information. 4 | 5 | ![bug_issue](https://github.com/samapriya/awesome-gee-community-datasets/assets/6677629/714d6458-a0b0-4792-aa42-1d8b9c9a4705) 6 | 7 | [To submit a bug report for an existing awesome-gee-catalog dataset use this link](https://github.com/samapriya/awesome-gee-community-datasets/issues/new?assignees=samapriya&labels=bug%2Ctriage&projects=&template=bug.yml&title=%5BBug%5D%3A+) 8 | -------------------------------------------------------------------------------- /docs/contributing/example.md: -------------------------------------------------------------------------------- 1 | # Submit example for dataset in community catalog 2 | 3 | Examples are helpful in understanding different use cases for datasets as well as enabling rich visualization of an existing dataset from domain experts. The template allows you to point to an existing dataset and submit an example code link via code editor/colab link or otherwise for others to use. All example/code contributors get attribution in the code apart from dataset attributions which are already included. 4 | 5 | ![example_issue](https://github.com/samapriya/awesome-gee-community-datasets/assets/6677629/c30caa5f-5d6f-4101-b6ca-659ce84d9443) 6 | 7 | [To submit an example for an existing awesome-gee-catalog dataset use this link](https://github.com/samapriya/awesome-gee-community-datasets/issues/new?assignees=samapriya&labels=data%2Cexample&projects=&template=bexm.yml&title=%5BDataset+Title%2FName%5D%3A) 8 | 9 | 10 | -------------------------------------------------------------------------------- /docs/contributing/submit.md: -------------------------------------------------------------------------------- 1 | # Submit or bring your data request to community catalog 2 | 3 | The submit data request templates are further subdivded into two templates one for datasets that you might have created vs any dataset that might be valuable to the community catalog and you would like to submit for consideration. For both templates modify the markdown text as needed and fill in the pieces of information that is available to you as in the example below. 4 | 5 | ![submit_issue](https://github.com/samapriya/awesome-gee-community-datasets/assets/6677629/903ec378-abd2-4de1-ad32-5c326bd6b960) 6 | 7 | * [To submit a new dataset for the community catalog use this link](https://github.com/samapriya/awesome-gee-community-datasets/issues/new?assignees=samapriya&labels=Dataset&projects=&template=bnd.yml&title=%5BDataset+Title%2FName%5D%3A+) 8 | 9 | * [To bring your own dataset for the community catalog use this link](https://github.com/samapriya/awesome-gee-community-datasets/issues/new?assignees=samapriya&labels=BYOD&projects=&template=byod.yml&title=%5BDataset+Title%2FName%5D%3A+) 10 | -------------------------------------------------------------------------------- /docs/contributing/update.md: -------------------------------------------------------------------------------- 1 | # Submit update request for dataset in community catalog 2 | 3 | The submit updated data request templates is designed for requesting update to an existing data in the community catalog. This can range from new releases to continuous updates. Modify the markdown text in the template as needed and fill in the pieces of information that is available to you as in the example below. 4 | 5 | ![update_issue](https://github.com/samapriya/awesome-gee-community-datasets/assets/6677629/5daa3fa4-0ec6-43ef-a50d-6663dfdfdb28) 6 | 7 | [To submit a suggested update to an existing awesome-gee-catalog dataset use this link](https://github.com/samapriya/awesome-gee-community-datasets/issues/new?assignees=samapriya&labels=update&projects=&template=bup.yml&title=%5BDataset%2FTitle%5D%3A+) 8 | -------------------------------------------------------------------------------- /docs/images/logo_cropped.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/samapriya/awesome-gee-community-datasets/edb5998d6c17f76974d25945b01eb52f82614a46/docs/images/logo_cropped.jpg -------------------------------------------------------------------------------- /docs/images/tinitaly.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/samapriya/awesome-gee-community-datasets/edb5998d6c17f76974d25945b01eb52f82614a46/docs/images/tinitaly.gif -------------------------------------------------------------------------------- /docs/startup/catalog-examples.md: -------------------------------------------------------------------------------- 1 | # Access awesome-gee-catalog-examples repo 2 | 3 | The awesome GEE catalog dataset examples are now part of a repo. Add this to your code editor space for easy access and updates to datasets and examples. 4 | 5 | ![repository_add](https://i.imgur.com/hcbHHM2.gif) 6 | 7 | 8 |
9 | 10 |
11 | 12 | [  Add examples repo to your GEE reader repository list :fontawesome-solid-circle-plus:][accept-repo]{ .md-button .md-button--primary} 13 | 14 |
15 | 16 | **OR** 17 | 18 |
19 | 20 | 21 | [accept-repo]: https://code.earthengine.google.com/?accept_repo=users/sat-io/awesome-gee-catalog-examples 22 | 23 | 24 | 25 |
26 | 27 |
28 | 29 | [  Download GEE Community Catalog Examples Folder :fontawesome-solid-download:][download-examples]{ .md-button .md-button--primary} 30 | 31 |
32 | 33 |
34 | 35 | [download-examples]: https://github.com/samapriya/awesome-gee-community-datasets/raw/master/awesome-gee-catalog-examples.zip 36 | -------------------------------------------------------------------------------- /docs/stats.md: -------------------------------------------------------------------------------- 1 | # Catalog Stats 2 | 3 | ![GEE Community Datasets](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/samapriya/34bc0c1280d475d3a69e3b60a706226e/raw/community.json) 4 | ![GitHub Release](https://img.shields.io/github/v/release/samapriya/awesome-gee-community-datasets) 5 | 6 | ## Daily Stats 7 | 8 | 9 | * **Total Size of catalog**: 533.08 TB 10 | * **Total images in catalog**: 1,552,904 11 | * **Total image collections in catalog**: 736 12 | * **Total Feature collections in catalog**: 2,834 13 | * **Last Run Date**: 2025-06-02 14 | 15 | -------------------------------------------------------------------------------- /docs/thumbnails/GPWv4.png: 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