Kenya transport data

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

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

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

The dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's major cities. The map is an output of the sub-Saharan African Corridor, Mobile Warehouse Location pilot project. Modeled cities are: Nairobi (7,626,752); Mombasa (1,535,899); Nakuru (610,637); Kisumu (567,963) The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks, it is assumed accessibility for large cargo freight vehicles, tertiary and local traffic roads are not included; B. Lake and river navigation are treated as a surface (polygons) not taking into consideration navigation infrastructure (points). The production of the travel time surfaces followed the steps: rasterization of transportation network vector layers and surfaces; production of cost/time layer; computation of a cumulative cost/time layer from cities (Major Cities Accessibility Map). **Data publication:** 2020-08-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for road, railways, cities; GAUL country border; FAO Inland Waters in Africa; FAO Major Rivers in Africa; **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 Accessibility: Travel Time-Cost to Major Cities (Kenya - ~1km )

The dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's Mombasa port. The map is an output of the sub-Saharan African Corridor, Mobile Warehouse Location pilot project. The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks, it is assumed accessibility for large cargo freight vehicles, tertiary and local traffic roads are not included; B. Lake and river navigation are treated as a surface (polygons) not taking into consideration navigation infrastructure (points). The production of the travel time surfaces followed the steps: rasterization of transportation network vector layers and surfaces; production of cost/time layer; computation of a cumulative cost/time layer from ports (Ports Accessibility Map). **Data publication:** 2020-08-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for road, railways; GAUL country border; FAO Inland Waters in Africa; FAO Major Rivers in Africa; FAO Ports; **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**

The dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's regional major cities . The map is an output of the sub-Saharan African Corridor, Mobile Warehouse Location pilot project version 2. Modeled cities are: Tanzania: Dar es Salam (5,572,776); Mwanza (830,342); Zanzibar (796,903); Dodoma (571,629); Arusha (466,892); Somalia: Mogadiscio (2,275,976); Merka (480,543); Kismaayo (402,691); Ethiopia: Addis-Abeba (4,567,857); Diré Dawa (453,000); South Sudan: Djouba (917,910); Wau (328,651); Uganda: Kampala (4,101,302); Jinja (589,661); The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks, it is assumed accessibility for large cargo freight vehicles, tertiary and local traffic roads are not included; B. Lake and river navigation are treated as a surface (polygons) not taking into consideration navigation infrastructure (points). Regional travel time surfaces production steps are: rasterization of transportation network and surfaces and definition of cell travel time; creation of countries time/cost layers; combining countries into a regional cost layer; computation of a cumulative cost/time accessibility layer from cities (Regional Major Cities Accessibility Map). **Data publication:** 2020-08-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for road, railways, cities; GAUL country border; FAO Inland Waters in Africa; FAO Major Rivers in Africa; **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**

Travel time/cost surface (raster grid), port accessibility map for Kenya's Mombasa port. Outputs produced in the sub-Saharan African Corridor, Mobile Warehouse Location pilot project. The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks only, it is assumed that the accessibility is related to large cargo freight vehicles, tertiary and local traffic roads are not included; B. Lake and river navigation are treated as a surface (polygons) not taking into consideration navigation infrastructure (points). The steps to produce travel time surfaces are: rasterization of transportation network vector layers and surfaces, production of cost/time layer summing different grids cell values, and computation of a cumulative cost/time layer (Ports Accessibility Map). **Data publication:** 2020-07-13 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement lineage statement Produced using OpenStreetMap data for road, railways; GAUL country border; FAO Inland Waters in Africa; FAO Major Rivers in Africa; FAO Ports; **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:**

Locations of seaports, harbours and maritime infrastructure in Kenya. 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.

Railway and rail transport network of Kenya 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.

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 Kenya **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.5791 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.023 million km (3.9642%) - Unpaved roads: 0.1669 million km (28.8242%) About 0.3892 million km (67.2116%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0021 million km (0.5353%) 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