Tanzania Road Surface Classification from OSM and Deep Learning

HeiGIT's dataset provides detailed road surface classifications for Tanzania, distinguishing paved and unpaved roads. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery, covering approximately 0.9904 million kilometers of roads. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications leveraging geospatial road network data with urban/rural classification.

Strengths

• Includes AI-derived estimates for 0.9904 million kilometers of roads.
• Combines multiple data sources: OSM, Mapillary imagery, and urban settlement layers.
• Provides multiple classification fields, including model predictions and OSM-derived tags.

Limitations

• Approximately 31.8387% (0.3153 million km) of road surface information is missing in the base OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and sample data are unavailable, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:18:35.605980; freshness should be verified.

Geography

United Republic of Tanzania