Morocco's geospatial data

·NationalNew

Attractions, zoos, aquariums, theme parks, water parks, galleries, and artworks mapped in OpenStreetMap.

·NationalNew

Travel agencies in Morocco, including name and contact details when available

·National

Electric transmission pylons tagged OSM power=tower, with technical attributes when available.

·National

Electric poles tagged power=pole in OSM, mainly linked to distribution networks.

·National

Polygonal footprints of electrical substations mapped in OpenStreetMap.

·National

OSM electric substation points and representative points calculated inside substations mapped as areas.

·National

Segments of active power lines and cables mapped in OpenStreetMap, clipped to the continuous national extent.

·National

Polygon footprints of solar generators mapped in OpenStreetMap.

·National

Solar generators mapped in OpenStreetMap as points or representative points of photovoltaic surfaces.

·National

Individual wind generators mapped in OpenStreetMap, including a representative point for objects drawn as surfaces.

·National

OSM power plants represented by a point, either directly or derived from a polygon footprint, with energy source when available.

·National

Carte catégorielle des principaux types de forêts en 2020 à 10 m sur l’ensemble du Maroc, régions du Sud incluses. Le JRC combine plusieurs jeux mondiaux afin de distinguer les forêts naturellement régénérées, les forêts primaires et les forêts plantées ou plantations. Les pixels sans classe sont hors de l’emprise forestière GFC 2020 v3 ou ne disposent pas d’une classe exploitable. La couche sert à l’évaluation nationale et régionale; elle ne remplace pas un inventaire forestier de terrain.

·National

603 forest fires recorded in Morocco between 2012 and 2023, with the date and time of ignition, estimated duration, and forest type according to the Copernicus classification for each event.

·National

Land cover map of Morocco at approximately 30 m resolution, derived from the GLC_FCS30D series built using Landsat observations and continuous change detection. Each pixel contains one of 35 thematic codes detailing crops, forests, shrub and herbaceous formations, wetlands, built-up areas, bare land, water, snow or ice. The layer covers Morocco in a single continuous national extent and is prepared as a COG for web and GIS use

·National

Modeled estimate of the local average number of tree species per hectare on a 0.025° grid (about 2.5 km) covering a continuous national extent of Morocco, including southern regions. The global map was produced using around 1.3 million forest inventory plots and biophysical covariates. It depicts species richness, not species identity, abundance, or the definite presence of each species in each pixel

·National

Annual binary mask at approximately 30 m pixels where Hansen Global Forest Change v1.13 detects gross tree cover loss in Morocco. The layer covers Morocco in a single continuous extent and is derived without interpolation from the official lossyear 2001–2025 band.

·National

Annual 10 m map of land cover and land use in Morocco since 2017, produced by Impact Observatory and Esri from Sentinel-2 images. Each pixel contains one of nine thematic classes. The layer covers the national territory in a continuous extent, including southern regions, and is prepared in COG format for web viewing and GIS analysis.

·National

Slope aspect calculated from the GEDTM30 DEM on a 30 m metric grid covering all of Morocco, including southern regions. Values are expressed in degrees clockwise from north and should be combined with slope to identify flat areas.

·National

Local slope angle in degrees, calculated from the GEDTM30 DEM on a 30 m metric grid covering all of Morocco, including southern regions. This derived layer is suitable for regional analyses of terrain, erosion, runoff, and planning constraints.

·National

Estimated bare earth elevation at about 30 m resolution for all Morocco, including southern regions. GEDTM30 merges multiple elevation models and ICESat-2/GEDI observations using machine learning to reduce the influence of buildings, vegetation, and artifacts. This national COG is suited for web visualization and regional analyses, without replacing topographic or LiDAR surveys