Nigeria GIS data

The zipped geometrical dataset contains a shapefile on the reach of mine risk education services to children in Borno State, in the first quarter of 2019. The provision of Mine Risk Education is part of the overall Child Protection in Emergencies support in response to the humanitarian crisis in north east Nigeria.

The zipped geometrical dataset contains a shapefile on the reach of psycho-social support services to children in Borno and Yobe States, in the first quarter of 2019. The provision of psycho-social support to children is part of the overall Child Protection in Emergencies support services in response to the humanitarian crisis in north east Nigeria.

The zipped geometrical dataset contains a shapefile indicating the Child Protection Sub-sector overall response on all four indicators in all three crisis-affected states of north east Nigeria, that is, Borno, Adamawa and Yobe, during the first quarter of 2019.

Nigeria administrative level 0-3 edge-matched gazetteer, shapefiles, geodatabase, and geoservice. These layers are suitable for database or GIS linkage to the Nigeria - Subnational Population Statistics tables. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

**UNOSAT code FL20221019NGA** This map illustrates cumulative satellite-detected water using VIIRS in Nigeria between 13 to 17 October 2022 compared with the period from 08 to 12 October 2022. Within the cloud free analysed areas of about 890,000 km², a total of about 30,000 km² of land appears to be affected. In the analysed areas, 738 health facilities and 1,424 schools are identified to be within or close to a flooded zone. In comparison with the period between 08 to 12 October 2022, water extent appears to have decreased of about 1,000 km². Based on Worldpop population data and the maximal flood water extent ~6,660,000 people are potentially exposed or living close to flooded areas amongst the 204 million people living in the analysed zone. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

This map illustrates satellite-detected displaced persons shelters in the Muna settlement near Dalakaleri, Nigeria, 6 km east of Maidugur's city on the main road that connect Maidugur to Dikwa. UNITAR-UNOSAT analysis of satellite imagery acquired 5 September 2016 revealed a total of 2,597 shelters and 22 infrastructure and support buildings within the Muna compound. In addition there are 203 detected shelters outside the settlement, grouped across the road. Finally, as of 5 September 2016 there are 120 newly constructed shelters arranged in five rows in the norther part of the Muna compound. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected areas of displaced persons shelters in the Sangaya settlement, Borno state, Nigeria, and in the surrounding town of Dikwa. UNITAR-UNOSAT analysis of satellite imagery acquired 29 September 2016 revealed a total of 433 shelters and 54 infrastructure and support buildings within the Sangaya compound and a total of 2,259 shelters scattered in the surrounding town. A density analysis has been performed to highlight the most dense shelters areas (Sangaya settlement included), ranging from 400 to 10,500 shelters per square kilometer. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

**UNOSAT code FL20221019NGA** This map illustrates cumulative satellite-detected water using VIIRS in Nigeria between 1 to 25 October 2022 compared with the period from 1 to 30 September 2022. Within the cloud free analysed areas of about 912,000 km², a total of about 48,300 km² of land appears to be affected. In the analysed areas, 1,370 health facilities and 2,730 schools are identified to be within or close to a flooded zone. In comparison with the period between 1 to 30 September 2022, water extent appears to have increased of about 2,300 km². Based on Worldpop population data and the maximal flood water extent ~12,200,000 people are potentially exposed or living close to flooded areas amongst the 220 million people living in the analysed zone. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code FL20221019NGA** This map illustrates cumulative satellite-detected water using VIIRS in Nigeria between 19 to 23 October 2022 compared with the period from 13 to 17 October 2022. Within the cloud free analysed areas of about 873,000 km², a total of about 25,000 km² of land appears to be affected. In the analysed areas, 664 health facilities and 1,160 schools are identified to be within or close to a flooded zone. In comparison with the period between 13 to 17 October 2022, water extent appears to have decreased of about 5,000 km². Based on Worldpop population data and the maximal flood water extent ~5,200,000 people are potentially exposed or living close to flooded areas amongst the 197 million people living in the analysed zone. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (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: FL20240902NGA, GDACS ID: 1102720** This map illustrates cumulative satellite-detected water using VIIRS in Nigeria between 5 to 9 September 2024. Within the cloud free analyzed areas of about 815,000 km², a total of about 14,000 km² of lands appear to be affected with flood waters. Based on Worldpop population data and the maximum flood water extent, about 2,650,000 people are potentially exposed or living close to flooded areas. The pixelwise water fraction from VIIRS, using a 5-day composite at 375 m spatial resolution, indicates potential floodwater coverage ranging from 0% to 100%. This large-scale analysis is intended for guidance purposes and has not yet been validated with ground truth data or higher-resolution analysis. The population exposure analysis is based on floodwaters observed only in cloud-free areas, so the total number of people exposed may be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240902NGA, GDACS ID: 1102720** This application provides flood impact assessments for Nigeria in September 2024.

**UNOSAT code: FL20240902NGA, GDACS ID: 1102720** This map illustrates satellite-detected water extent along the Komadugu Gana River in Bursari & Tarmua Local Governments in Yobe State, Nigeria as observed from a Pleiades image acquired on 15 September 2024 at 10:38 local time. Within the cloud free analysed areas of about 120 km², a total of about 40 km² of lands appear to be affected with flood waters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240902NGA** This map illustrates the satellite-detected accumulated flood extent in Dikwa Town, Dikwa Local Government Area, Borno State, Nigeria. as observed from Sentinel-2 images acquired on 9 and 19 September 2024. Within analysed areas of about 200 km², a total of about 30 km² of lands appear to be affected with flood waters. UNITAR-UNOSAT identified about 1,060 potentially affected structures, along with a potentially affected road extending about 26 km within the analysed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

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 - railway - ele - operator:type - layer - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['waterway'] IS NOT NULL OR tags['water'] IS NOT NULL OR tags['natural'] IN ('water','wetland','bay') Features may have these attributes: - name - waterway - covered - width - depth - layer - blockage - tunnel - natural - water - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['aeroway'] IS NOT NULL OR tags['building'] = 'aerodrome' OR tags['emergency:helipad'] IS NOT NULL OR tags['emergency'] = 'landing_site' Features may have these attributes: - name - aeroway - building - emergency - emergency:helipad - operator:type - capacity:persons - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office') Features may have these attributes: - name - amenity - operator - network - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] IN ('kindergarten', 'school', 'college', 'university') OR tags['building'] IN ('kindergarten', 'school', 'college', 'university') Features may have these attributes: - name - amenity - building - operator:type - capacity:persons - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL Features may have these attributes: - name - amenity - building - port - operator:type - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

**UNOSAT code: FL20240902NGA, GDACS ID: 1102720** This map illustrates cumulative satellite-detected water using VIIRS in Nigeria between 09 to 13 November 2024. Within the cloud free analysed areas of about 900,000 km², a total of about 22,000 km² of lands appear to be affected with flood waters. Maximum flood water extent appears to have receded of about 5,700 km² since the period between 27 to 31 October 2024. Based on Worldpop population data and the maximum flood water extent, about 5.8 million people remain potentially exposed or live close to flooded areas. The pixelwise water fraction from VIIRS, using a 5-day composite at 375 m spatial resolution, indicates potential floodwater coverage ranging from 0% to 100%. This large-scale analysis is intended for guidance purposes and has not yet been validated with ground truth data or higher- resolution analysis. The population exposure analysis is based on floodwaters observed only in cloud-free areas, so the total number of people exposed may be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20250601NGA, GDACS ID: 1103303** This map illustrates the satellite-detected water extent in Mokwa Town, Niger Local Government Area, Niger State, Nigeria as observed from a GeoEye-1 imagery acquired on 2 June 2025 at 11:24 local time. Within the analysed area of approximately 100 km², an estimated 1.5 km² of land appears to be affected by floodwaters. UNOSAT has identified within this area 470 damaged structures and 199 potentially affected structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20250601NGA, GDACS ID: 1103303** This map illustrates the satellite-detected water extent in Mokwa Town, Niger Local Government Area, Niger State, Nigeria as observed from a GeoEye-1 imagery acquired on 2 June 2025 at 11:24 local time. Within the analysed area of approximately 100 km², an estimated 1.5 km² of land appears to be affected by floodwaters. UNOSAT has identified within this area 470 damaged structures and 199 potentially affected structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20250928NGA, GDACS ID: 1103409** This map illustrates the satellite-detected water extent in Adamawa, Borno, Gombe, Taraba and Yobe States, Nigeria, as observed from Sentinel-2 satellite images acquired on 29 July 2025 at 14:28 local time (13:28 UTC). Within the analyzed area of approximately 47,000 km², about 700 km² of land appears to be affected by floodwaters. Based on WorldPop population data and the flood extent, approximately 19,000 people are potentially exposed or living close to the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the 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 Nigeria **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 1.0944 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.1326 million km (12.119%) - Unpaved roads: 0.3908 million km (35.7111%) About 0.571 million km (52.1699%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0016 million km (0.2766%) 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

**UNOSAT code: FL20220721NGA** This map illustrates satellite-detected surface waters in Gujba LGA, Yobe state, Nigeria as observed from a Sentinel-1 image acquired on 18 Jul 2022 at 18:22 local time. Within the analyzed area of about 1,040 km2, about 22 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 1,400 people are potentially exposed or living within/close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

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The dataset contains information on flood-affected people and locations in Nigeria.

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

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. 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, building - Part of the country exports on HDX by HOT