Cameroon Road Surface Data with AI-Derived Paved and Unpaved Classifications

HeiGIT's dataset provides detailed road surface information for Cameroon, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. It covers approximately 226,600 km of roads, with AI estimates classifying 7.03% as paved and 48.685% as unpaved. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface classifications (paved/unpaved).
• Infrastructure analysis based on OSM attributes like highway type, surface, and width.
• Climate emissions assessment based on road network composition and urban/rural classification.
• GIS applications for logistics and development planning based on geospatial road data.

Strengths

• Covers approximately 226,600 km of roads in Cameroon.
• Provides AI-derived surface classifications for roads where OSM data is missing, adding an estimated 300 km of surface information.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS urban layers.

Limitations

• Approximately 100,300 km (44.2849%) of road surface information is missing in the underlying OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT).

Collection Method

Derived from OpenStreetMap and augmented with a hybrid deep learning approach using Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:12:19.954454; freshness should be verified.

Geography

Cameroon.