Angola transport data

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 Angola **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.284 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.024 million km (8.4616%) - Unpaved roads: 0.1105 million km (38.9039%) About 0.1495 million km (52.6345%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0002 million km (0.15%) 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

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

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. 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: railway - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation 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: highway - Part of the country exports on HDX by HOT

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

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the 20 largest cities (>110k habitants) in the country. The following cities are included: City - Population Luanda - 6,759,313 Lubango - 600,751 Huambo - 595,304 Benguela - 555,124 Cabinda - 550,000 Malanje - 455,000 Saurimo - 393,000 Lobito - 357,950 Kuito - 355,423 Uíge - 322,531 Luena - 273,675 Moçâmedes - 255,000 Menongue - 251,178 Sumbe - 205,832 Soyo - 200,920 Dundo - 177,604 N'dalatando - 161,584 M'banza-Kongo - 148,000 Ondjiva - 121,537 Gabela - 116,903 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, rivers; UN Map country border; The HydroSHEDS 15' resolution GRID for the DEM, GHSL - Global Human Settlement Layer. For the city population **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Angola major cities accessibility

Accessibility to ports dataset is modeled as raster-based travel time/cost analysis. The model travel time/cost from/to ports is defined for major maritime ports only. This 500m resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for road, railways, rivers; UN Map country border; Population major cities **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Angola major port accessibility.

Railway and rail transport network of Congo 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 railway stations across Angola, including named stations such as Boas Águas and Nangombe. Each feature represents a station with attributes for name, railway type, elevation, operator, and address. Published by Orion Terrametrics.

Point locations of airports, airstrips and airfields in Angola. 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.