United Republic of Tanzania transport data

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

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

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

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the 10 largest cities (>200k habitants) in the country, normalized from 0 to 100, where 0 corresponds to the lowest city accessibility. The following cities are included: City - Population Dar es Salaam - 4,364,541 Mwanza - 706,453 Zanzibar - 501,459 Arusha - 416,442 Mbeya - 385,279 Morogoro - 305,840 Tanga - 221,127 Kigoma - 215,458 Dodoma - 213,636 Songea - 203,309 This 1km 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:** 2021-11-01 **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; 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 Tiff raster file of accessibility to major cities (Tanzania - ~ 1Km)

Accessibility to ports dataset is modelled as raster-based travel time/cost analysis, normalized from 0 to 100, where 0 corresponds to the lowest accessibility. The model travel time/cost from/to ports is defined for major maritime ports only, although major lake ports as those in Lake Victoria can have some relevance considering the export markets in neighboring countries that can be reached through them. This 1km 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: [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; 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 Tiff raster file of accessibility to major maritime ports (Tanzania - ~ 1Km)

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 United Republic of Tanzania **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.9904 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0289 million km (2.9189%) - Unpaved roads: 0.6462 million km (65.2423%) About 0.3153 million km (31.8387%) 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 (0.0722%) 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

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

The Tanzania Rural and Urban Roads Agency - road network, is a linear referencing system of roads showing roads intersection, start/end of the road. (FAO CSI geospatal analysis team - Comment: Abstract needs elaboration...) GCP bucket dataset fao-maps-gsrv-edit/projects/Tanzania **Contact points:** Resource Contact: [Balthazar Faustine](mailto:ceo@tarutra.go.tz) **Online resources:** TARURA Website