Burkina Faso Road Surface Classification from OSM and Deep Learning

Approximately 202,300 km of roads are mapped in OpenStreetMap for Burkina Faso, with AI-derived estimates classifying 6,400 km as paved and 70,500 km as unpaved. The dataset is produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated in March 2026. It combines OSM data with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis based on road surface attributes like smoothness 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

• Includes AI-derived surface classifications for over 202,000 km of roads.
• Combines multiple data sources: OSM, Mapillary imagery, and urban settlement layers.
• Provides multiple classification schemes (OSM-priority and DL-priority) for flexibility.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Approximately 125,400 km (61.97%) of road surface information is reported as missing in the base OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in the referenced arXiv papers.

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:23:21.482591; freshness should be verified.

Geography

Burkina Faso.