Guinea-Bissau Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides detailed road surface information for Guinea-Bissau, distinguishing paved from unpaved roads. It contains approximately 21,600 km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 7.2% as paved and 38.1% as unpaved. The data was last updated on March 2, 2026, and combines OSM data with deep learning predictions from Mapillary imagery.

Use Cases

• Transportation planning based on road surface classification (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 humanitarian access and logistics based on geospatial road data.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban layers from GHSU and AFRICAPOLIS.
• Provides AI-derived estimates for 45.3% of the road network (paved and unpaved classifications).
• Includes detailed OSM and urban classification attributes for contextual analysis.

Limitations

• 54.7% 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 and dataset size are unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

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

Time Range

null

Freshness

Last updated 2026-03-02 17:06:05.369628; freshness should be verified.

Geography

Guinea-Bissau