Morocco GIS data

Rivers, streams and drainage network of Morocco showing the extent and hierarchy of surface water features. Use this layer for watershed analysis, flood modelling, irrigation planning, water resource mapping, and environmental impact assessment.

Second-level administrative divisions of Morocco such as districts or departments. Use this layer for local government analysis, service delivery planning, and community-level spatial work.

Point locations of airports, airstrips and airfields in Morocco. Each feature includes the facility name, type, elevation and runway information where available. Use this layer for aviation planning, accessibility analysis, emergency response logistics, and transport network mapping.

Natural Earth admin 0 countries mar at 1:10 million scale: public domain cartographic vector data built to make small-scale world, regional and country maps.

Road and transport network of Morocco showing the classification and extent of road infrastructure. Includes primary roads, secondary roads, tracks and footpaths where available. Use this layer for route planning, accessibility analysis, transport modelling, and logistics planning.

Administrative boundary lines of Morocco as linear features. Use this layer for cartographic production and boundary demarcation visualization.

Point locations of health facilities in Morocco including hospitals, health centres, clinics, dispensaries and health posts. Each feature records the facility name, type and ownership. Use this layer to map healthcare coverage, identify service gaps, plan facility distribution, and support health sector analysis.

The national boundary of Morocco, defining the outermost extent of the country's territory. Use this layer as a base for national-scale maps, spatial analysis, and geographic context in any GIS project covering Morocco.

Locations of seaports, harbours and maritime infrastructure in Morocco. Includes commercial ports, fishing harbours and ferry terminals. Use this layer for maritime transport planning, trade logistics analysis, coastal zone management, and shipping route mapping.

**UNOSAT code EQ20230909MAR, GDACS Id: 1390643** Cette carte illustre les structures/bâtiments potentiellement endommagés par le séisme de magnitude 6,8 d’ Adassil/Mjdid du 8 Septembre 2023 à 22h11 UTC (à 75 km au SO de Marrakech) tel qu'observé à partir d'une image satellite Pléiades acquise le 10 Septembre 2023. Dans ce secteur d’analyse, UNOSAT a identifié 216 bâtiments endommagés et/ou potentiellement endommagés. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT)

**UNOSAT code EQ20230909MAR, GDACS Id: 1390643** This map illustrates potentially damaged structures/buildings by the M6.8 Chichaoua earthquake (70 km SW Marrakesh) of 08 September 2023 (22:11 UTC) as detected by using a Pleiades very high-resolution satellite image acquired on 10 September 2023. Within map extent, UNOSAT identified 271 damaged/potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code EQ20230909MAR, GDACS Id: 1390643** Status: Power outage observed. Further action(s): continue monitoring No significant power outage observed in Marrakech (70 km NE mainshock epicentre), Taroudant (150 km SW Marrakech) and Chichaoua (75 km SW Marrakech). By 17 September, power supply in Tameslouht (20 km SW Marrakech) has recovered to the level observed before the earthquake of 08 September 2023. On 14 September, Southeast of Amizmiz likely to have power outage. By 17 September, power supply in Amizmiz (50 km SW Marrakech) was still  in recovery phase but not yet at the before earthquake level. On 14 September, no evidences of power outage is observed in rural areas such as Ait Iaaza (60 km SW mainshock epicentre) and Sidi Zouine (60 km NE mainshock epicentre).

**UNOSAT code EQ20230909MAR, GDACS Id: 1390643** Cette carte illustre les structures/bâtiments potentiellement endommagés par le séisme du 8 Septembre 2023 à Taroudant (à 155 km au SO de Marrakech) tel qu'observé à partir de l’analyse d’une image satellite Pléiades acquise le 10 Septembre 2023. Dans ce secteur d’analyse, UNOSAT a identifié 205 bâtiments endommagés et/ou potentiellement endommagés. L'observation d'un regroupement de tentes au NE de la ville indiquerait une zone de rassemblement temporaire de sinistrés. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT).

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.

**UNOSAT code EQ20230909MAR, GDACS Id: 1390643** This map illustrates potentially damaged structures/buildings by the M6.8 Adassil/Al Haouz earthquake (70 km SW Marrakesh) of 08 September 2023 (22:11 UTC) as detected by using a Pleiades very high-resolution satellite image acquired on 10 September 2023. Within map extent, UNOSAT identified 77 damaged/potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

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 Morocco **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.5282 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.1088 million km (20.6055%) - Unpaved roads: 0.0629 million km (11.9155%) About 0.3564 million km (67.479%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0008 million km (0.2314%) 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

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This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long