Greenland GIS data
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satellite imagery by Cave Exploration and Research Initiative, acquired 2025-03-30 to 2025-03-31, 3.1 cm per pixel, published on OpenAerialMap.
EOxCloudless is a global satellite imagery dataset consisting of near-cloudless mosaics derived from Copernicus Sentinel-2 multispectral satellite data. The dataset is produced by EOX IT Services GmbH and is designed to provide consistent, high-resolution surface imagery for mapping, analysis, and visualization purposes. The mosaics are generated by combining a large number of Sentinel-2 Level-1C observations acquired over defined time periods. Advanced pixel selection algorithms and thematic masks (including clouds, cloud shadows, and water) are applied to reduce cloud contamination. For each pixel location, the most representative observation is selected based on statistical criteria and temporal analysis, resulting in near-cloudless composite images. The dataset provides global land coverage and is typically delivered at a spatial resolution of 10 metres for the utilised spectral bands (blue, green, red, and near-infrared). EOxCloudless is suitable for use as a basemap as well as for environmental monitoring, land analysis, spatial planning, and other geoscientific applications. Official metadata Purpose The dataset has been adapted and created specifically for display on the Greenland Portal. Lineage EOX Sentinel-2 cloudless is a purchased dataset from the company EOX that contains a mosaic of cloudless satellite imagery worldwide on a scale of 1:20.000. The original dataset consists of a GeoPackage with tiles of 512 x 512 pixels with LZW (lossless) compression where the individual tiles are stored as GeoTIFFs with 8-bit RGB bands, each in a non-projected geographical coordinate system (WGS 84, EPSG: 4326). The data set is cut to Greenland only and projected from WGS 84 to projected coordinate system WGS 84 / UTM 24N (EPSG: 32624).
uav imagery by Cave Exploration and Research Initiative , acquired 2025-03-30 to 2025-03-30, 6.1 cm per pixel, published on OpenAerialMap.
Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.
The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.
This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Greenland **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0025 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0001 million km (5.2146%) - Unpaved roads: 0.0009 million km (38.0451%) About 0.0014 million km (56.7402%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (0.1402%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org
This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long
Education facilities from OSM. Includes schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. OpenStreetMap is a free, collaborative world map built by volunteers. Completeness varies by region: urban areas are usually well-mapped, remote areas may be incomplete. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap - OSM tags: Key:amenity, Key:building - Part of many exports grid on HDX by the HOT
Health facilities from OSM. Includes hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. OpenStreetMap is a free, collaborative world map built by volunteers. Completeness varies by region: urban areas are usually well-mapped, remote areas may be incomplete. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap - OSM tags: Key:amenity, Key:healthcare - Part of many exports grid on HDX by the HOT
Financial services from OSM. Includes banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. OpenStreetMap is a free, collaborative world map built by volunteers. Completeness varies by region: urban areas are usually well-mapped, remote areas may be incomplete. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap - OSM tags: Key:amenity - Part of many exports grid on HDX by the HOT
Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT
Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT
Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT
Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT
Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT
Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT
Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).
Retirement Notice: This item is in mature support as of June 2024 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Greenland Country Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in May 2023. Other administrative boundaries for this country are also available:Kommuner
Retirement Notice: This item is in mature support as of June 2024 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Greenland Kommuner Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in May 2023. Other administrative boundaries for this country are also available:Country
GEUS' 1:250 000 Topographic Map of Greenland with semi-transparent hillshading effect based on the GIMP DEM.
The ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data are obtained from the USGS webpage https://earthexplorer.usgs.gov/ in the time period of 2000-2007. Scenes were also limited to summer and captured in daytime. Preprocessing of the Aster scenes encompasses atmospheric, radiometric and topographic corrections before masking non-outcrop pixels and generating the final mosaic. The calibrated radiance data is converted to apparent surface reflectance using a radiative transfer program, Atmospheric and Topographic Correction (ATCOR-3), in rugged terrain mode. The ATCOR rugged terrain mode utilizes a surface elevation model to adjust illumination levels. Calibration and adjusting the apparent surface reflectance values from the ATCOR-3 processing was not feasible due to lack of ground-based reflectance measurements.
uav imagery by Cave Exploration and Research Initiative , acquired 2025-03-24 to 2025-03-24, 2.8 cm per pixel, published on OpenAerialMap.
uav imagery by Cave Exploration and Research Initiative, acquired 2025-03-25 to 2025-03-26, 14.6 cm per pixel, published on OpenAerialMap.
uav imagery by Cave Exploration and Research Initiative, acquired 2025-03-31 to 2025-04-01, 1.6 cm per pixel, published on OpenAerialMap.
Skråbillederne er behandlet og optegnet i Flylaboratoriet i afdeling for petrologi og malmgeologi. Optagelsen af skråbillederne er foretaget i store målestoksforhold til senere fotogrammetrisk opmåling og 3D stereovisualisering af digital 3D kortdata.
uav imagery by Cave Exploration and Research Initiative, acquired 2025-03-25 to 2025-03-26, 14.6 cm per pixel, published on OpenAerialMap.
This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.
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This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source - name:gre - name:da This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT