South Africa GIS data

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means [here](https://wiki.openstreetmap.org/wiki/Tags) ) : tags['waterway'] IS NOT NULL OR tags['water'] IS NOT NULL OR tags['natural'] IN ('water','wetland','bay') Features may have these attributes: - [name](http://wiki.openstreetmap.org/wiki/Key:name) - [name:en](http://wiki.openstreetmap.org/wiki/Key:name:en) - [waterway](http://wiki.openstreetmap.org/wiki/Key:waterway) - [covered](http://wiki.openstreetmap.org/wiki/Key:covered) - [width](http://wiki.openstreetmap.org/wiki/Key:width) - [depth](http://wiki.openstreetmap.org/wiki/Key:depth) - [layer](http://wiki.openstreetmap.org/wiki/Key:layer) - [blockage](http://wiki.openstreetmap.org/wiki/Key:blockage) - [tunnel](http://wiki.openstreetmap.org/wiki/Key:tunnel) - [natural](http://wiki.openstreetmap.org/wiki/Key:natural) - [water](http://wiki.openstreetmap.org/wiki/Key:water) - [source](http://wiki.openstreetmap.org/wiki/Key:source) - [name:af](http://wiki.openstreetmap.org/wiki/Key:name:af) - [name:nr](http://wiki.openstreetmap.org/wiki/Key:name:nr) - [name:st](http://wiki.openstreetmap.org/wiki/Key:name:st) - [name:nso](http://wiki.openstreetmap.org/wiki/Key:name:nso) - [name:ssw](http://wiki.openstreetmap.org/wiki/Key:name:ssw) - [name:ts](http://wiki.openstreetmap.org/wiki/Key:name:ts) - [name:tn](http://wiki.openstreetmap.org/wiki/Key:name:tn) - [name:ve](http://wiki.openstreetmap.org/wiki/Key:name:ve) - [name:xh](http://wiki.openstreetmap.org/wiki/Key:name:xh) - [name:zu](http://wiki.openstreetmap.org/wiki/Key:name:zu) This dataset is one of many [OpenStreetMap exports on HDX](https://data.humdata.org/organization/hot). See the [Humanitarian OpenStreetMap Team](http://hotosm.org/) website for more information.

geoBoundaries ADM4 boundaries for South Africa: 4392 units, representing 2020. Source: OCHA ROSEA, South African Municipal Demarcation Board.

Point locations of named places in Lesotho, including populated places, mountains and other feature types. Each feature records a name, alternate ASCII name, coordinates, feature class and code, country, administrative units, population, elevation and timezone. Published by Orion Terrametrics.

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

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](https://github.com/whosonfirst/whosonfirst-sources/blob/main/sources/README.md) file for complete data source metadata. Refer to our [blog post](https://whosonfirst.org/blog/2023/05/31/shapefiles/) for explanation of field names. Data corrections can be proposed using [Write Field](https://writefield.nextzen.org/), 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.

Water resources and water infrastructure in South Africa including rivers, lakes, boreholes, wells and water points. Use this layer for water access analysis, WASH programme planning, drought monitoring, flood risk assessment, and water resource management.

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](https://github.com/whosonfirst/whosonfirst-sources/blob/main/sources/README.md) file for complete data source metadata. Refer to our [blog post](https://whosonfirst.org/blog/2023/05/31/shapefiles/) for explanation of field names. Data corrections can be proposed using [Write Field](https://writefield.nextzen.org/), 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.

This point dataset represents populated places in South Africa, such as cities and towns. Each feature includes attributes like name, place type, population, and administrative location. The data is published by Orion Terrametrics and contains 3,075 features.

geoBoundaries ADM3 boundaries for South Africa: 213 units, representing 2020. Source: OCHA ROSEA, South African Municipal Demarcation Board.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means [here](https://wiki.openstreetmap.org/wiki/Tags) ) : tags['healthcare'] IS NOT NULL OR tags['amenity'] IN ('doctors', 'dentist', 'clinic', 'hospital', 'pharmacy') Features may have these attributes: - [name](http://wiki.openstreetmap.org/wiki/Key:name) - [name:en](http://wiki.openstreetmap.org/wiki/Key:name:en) - [amenity](http://wiki.openstreetmap.org/wiki/Key:amenity) - [building](http://wiki.openstreetmap.org/wiki/Key:building) - [healthcare](http://wiki.openstreetmap.org/wiki/Key:healthcare) - [healthcare:speciality](http://wiki.openstreetmap.org/wiki/Key:healthcare:speciality) - [operator:type](http://wiki.openstreetmap.org/wiki/Key:operator:type) - [capacity:persons](http://wiki.openstreetmap.org/wiki/Key:capacity:persons) - [addr:full](http://wiki.openstreetmap.org/wiki/Key:addr:full) - [addr:city](http://wiki.openstreetmap.org/wiki/Key:addr:city) - [source](http://wiki.openstreetmap.org/wiki/Key:source) - [name:af](http://wiki.openstreetmap.org/wiki/Key:name:af) - [name:nr](http://wiki.openstreetmap.org/wiki/Key:name:nr) - [name:st](http://wiki.openstreetmap.org/wiki/Key:name:st) - [name:nso](http://wiki.openstreetmap.org/wiki/Key:name:nso) - [name:ssw](http://wiki.openstreetmap.org/wiki/Key:name:ssw) - [name:ts](http://wiki.openstreetmap.org/wiki/Key:name:ts) - [name:tn](http://wiki.openstreetmap.org/wiki/Key:name:tn) - [name:ve](http://wiki.openstreetmap.org/wiki/Key:name:ve) - [name:xh](http://wiki.openstreetmap.org/wiki/Key:name:xh) - [name:zu](http://wiki.openstreetmap.org/wiki/Key:name:zu) This dataset is one of many [OpenStreetMap exports on HDX](https://data.humdata.org/organization/hot). See the [Humanitarian OpenStreetMap Team](http://hotosm.org/) website for more information.

Spatial distribution of the population of South Africa, showing where people live and in what densities across the country. Use this layer for understanding settlement patterns, sizing infrastructure, planning service delivery, and conducting demographic analysis.

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](https://github.com/whosonfirst/whosonfirst-sources/blob/main/sources/README.md) file for complete data source metadata. Refer to our [blog post](https://whosonfirst.org/blog/2023/05/31/shapefiles/) for explanation of field names. Data corrections can be proposed using [Write Field](https://writefield.nextzen.org/), 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.

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](https://github.com/whosonfirst/whosonfirst-sources/blob/main/sources/README.md) file for complete data source metadata. Refer to our [blog post](https://whosonfirst.org/blog/2023/05/31/shapefiles/) for explanation of field names. Data corrections can be proposed using [Write Field](https://writefield.nextzen.org/), 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: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow in eThekwini, Metropolitan Municipality, eThekwini District, KwaZulu-Natal Province, South Africa as observed from a Kompsat-3 image acquired on 21 April 2022. Within the analyzed area, 270 ha of landslide scars were observed. Based on Worldpop population data, about 283,000 people live inside the analyzed area. Within the analysis extent, UNITAR-UNOSAT identified 506 damaged structures and 197 potentially damaged 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: FL20220418ZAF** Landslide impact analysis in eThekwini, Metropolitan Municipality, This map illustrates satellite-detected water and landslides/mudflow in eThekwini, Metropolitan Municipality, eThekwini District, KwaZulu-Natal Province, South Africa as observed from a WorldView-3 image acquired on 20 April 2022. Within the analyzed area, 323 ha of landslide scars were observed. Based on Worldpop population data, about 570,000 people live inside the analyzed area. Within the analysis extent, UNITAR-UNOSAT identified 164 damaged/affeted structures and 79 potentially damaged 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).

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.

**UNOSAT code: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow in Umdloti village, eThekwini Metropolitan Municipality, KwaZulu-Natal Province, South Africa as observed from a NewSat image acquired on 13 April 2022. Within the analyzed area, 14 buildings appear to be affected and 13 are potentially affected. Affected roads were also identified. 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: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow-related impact in eThekwini, Metropolitan Municipality, eThekwini District, KwaZulu-Natal Province, South Africa as observed from a Kompsat-3 image acquired on 21 April 2022. Within the analyzed area, 21 ha of landslide scars were observed. Based on Worldpop population data, about 55,000 people live inside the analyzed area. Within the analysis extent, UNITAR-UNOSAT identified 121 damaged structures and 247 potentially damaged 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).

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in South Africa. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

**UNOSAT code: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow in eThekwini, Metropolitan Municipality, eThekwini District, KwaZulu-Natal Province, South Africa as observed from a Sentinel-2 image acquired on 28 April 2022.Within the analyzed area, 11 ha of landslide scars were observed. Based on Worldpop population data, about 27,000 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).

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 South Africa **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 1.0504 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.2143 million km (20.4009%) - Unpaved roads: 0.3681 million km (35.046%) About 0.468 million km (44.5531%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0039 million km (0.8381%) 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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**UNOSAT code: FL20220418ZAF** SUMMARY OF FINDINGS: Increased water level, inundated infrastructure & damaged road observed along the Mlazi river as of 14 April 2022; Inundated SAPREF Oil Refinery as of 14 April 2022; Increased water level and damaged roadway observed along the Mhlatuzana river as of 14 April 2022; Inundated area observed along the Umbilo river as of 14 April 2022; Increased water level and landslide observed along the Palmiet river as of 14 April 2022. Status: : Increased water level and landslides observed Further action(s): Continue monitoring

**UNOSAT code: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow in Umzumbe & Ray Nkonyeni Local Municipalities, Ugu District, KwaZulu-Natal Province, South Africa as observed from a Kompsat-3 image acquired on 21 April 2022. Within the analyzed area, 75 ha of landslide scars were observed. Based on Worldpop population data, about 67,000 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).

South Africa administrative level 0-4 boundaries (COD-AB) dataset version 01. This dataset is structured into 4 levels: - Admin 1: 9 Province - Admin 2: 52 District - Admin 3: 213 Local municipality - Admin 4: 4392 Dates associated with this dataset: - 28 January 2025: dataset reviewed for accuracy and completeness - 09 November 2020: valid for use by the humanitarian community - 27 December 2018: last edit to the dataset before publishing - 27 December 2018: boundaries created by the source Contributed by OCHA Regional Office for Southern and Eastern Africa (ROSEA). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

**UNOSAT code: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow West of Durban City, eThekwini Metropolitan Municipality, KwaZulu-Natal Province, South Africa as observed from a WorldView-3 imagery acquired on 14 April 2022. Within the analyzed area, at least 62 structures and 2 bridges appear to be affected by floods and/or landslides. 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).

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

**UNOSAT code: FL20220418ZAF** Webmap of satellite based analysis related to the Floods and landslides of 13 Apr. 2022 in KwaZulu-Natal (Republic of South Africa).