Uganda GIS data
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Polygons representing areas that would be inundated by a static river level of 3 metres depth in Uganda. Each feature is an inundated area under this scenario. Published by Orion Terrametrics.
This line dataset represents a modelled river network in Uganda. Each feature is a river segment with a grid code. The data is published by Orion Terrametrics.
geoBoundaries ADM4 boundaries for Uganda: 1521 units, representing 2019. Source: Operational Data Portal Refugee Situations, UNHCR.
Spatial distribution of estimated pregnancies across Uganda at 1km resolution. Each grid cell shows the estimated number of pregnancies including live births, stillbirths and abortions. Use this layer for maternal health planning, antenatal care programme design, health facility placement, and reproductive health analysis.
Digital Elevation Model (DEM) of Kenya showing the terrain surface elevation above sea level. Each pixel represents the elevation in metres. Use this layer for terrain analysis, slope and aspect mapping, watershed delineation, flood modelling, infrastructure siting, and visibility analysis.
Point locations of educational facilities across Uganda, including primary schools, secondary schools and other learning institutions. Each feature records the facility name, type and administrative location. Use this layer to map education access, identify underserved areas, plan school placement, and analyse catchment areas and travel distances to schools.
Gridded population density of Uganda for the year 2020 at 1km resolution, adjusted to match UN national population estimates. Each grid cell shows the estimated number of people per square kilometre. Use this layer for settlement pattern analysis, infrastructure planning, service delivery optimisation, and identifying high and low density areas.
Polygon boundaries of Uganda's second-level administrative units, published by Orion Terrametrics. Each feature represents a district such as Abim or Adjumani, with its name, code, parent region, area, and validity dates.
Line features representing administrative boundaries in Uganda, published by Orion Terrametrics. Each line is an administrative boundary at a given level, with validity dates and version information.
Railway and rail transport network of Uganda showing the extent and classification of rail infrastructure. Includes passenger and freight lines. Use this layer for rail transport planning, freight logistics analysis, infrastructure assessment, and multimodal transport mapping.
Point locations of health facilities in Uganda 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.
A 100-metre raster of population for Uganda, published by Orion Terrametrics. Each cell holds a population value.
A 1-kilometre raster of population for Uganda, published by Orion Terrametrics. Each cell holds a population value.
This polygon dataset represents potential inundated areas for a static river level of 2 meters depth in Uganda. Each feature indicates an area that could be inundated under this scenario. The data is published by Orion Terrametrics.
Polygon boundaries of Uganda's fourth-level administrative units, published by Orion Terrametrics. Each feature represents a sub-county such as Abako or Abanga, with its name, code, parent units, area, centre coordinates, and validity dates.
Point locations of named places in Uganda, including hills and other features. Each row is one place with its name, coordinates, feature class and code, administrative units, population, elevation and timezone. Published by Orion Terrametrics.
Point features representing administrative locations in Uganda, published by Orion Terrametrics. Each point carries administrative names and codes at levels 0 to 4, along with coordinates and validity dates.
Administrative level 3 boundary polygons for Uganda, published by Orion Terrametrics. Each polygon represents an administrative unit with names and codes for country, region, district, and sub-county levels, plus area and center coordinates. The dataset covers Uganda and includes 208 features.
This data is about Uganda Red Cross Society Projects Footprint as of 01 01 2020
This data is about the 51 Uganda Red Cross Society Branches as of 01 12 2020
This data is about Accident hotspots from Road Traffic Accidents survey/ assessment by Uganda Red Cross Society, Ministry of Health and Uganda Police, 14th July 2020
This data is a zipped shapefile of the COVID-19 risk index based on the INFORM index framework.
The data is risk indices associated with COVID-19 PANDEMIC in Uganda at district administrative level.
This data is about Uganda Districts affected by Floods and Rising Water Levels in Lakes as of Friday, 12th June 2020
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: FL20220803UGA** This map illustrates satellite-detected landslides in Budwale, Lwasso, Wanale, Bungokho Mutoto sub-countries and Wanale Division and Northern Division, Mbale district, eastern region, Uganda as observed from a Sentinel-2 image aquired on 9 August 2022. Within the analyzed area, 27ha landslide scars are observed. Based on Wordlpop population data about 200,700 people live inside the analyzed 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).
**UNOSAT code: LS20191209UGA** This map illustrates satellite-detected landslide at in Humya city, Bwanba County, Budibungyo District, Western Region, as seen on Pleiades-1 satellite imagery, 50 cm resolution, collected on 12 December 2019. The landslides and mudflows hit Humyai along the main streams of Bwanba County. 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 surface water extent in Elegu town and surroundings using a Sentinel-1 satellite image acquired on the 23 August 2017. Within the map extent, surface waters extended of about 12% more particularly south and north-west of Elugu where evidences of floods could be observed. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-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 Uganda **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.4166 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.027 million km (6.4788%) - Unpaved roads: 0.1137 million km (27.3023%) About 0.2759 million km (66.219%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.002 million km (0.7318%) 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
Uganda Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2022
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