Road Surface Classification for French Southern Territories from OSM and AI

French Southern Territories road data provides detailed surface classifications distinguishing paved and unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban layers. It was last updated on March 2, 2026, by the Heidelberg Institute for Geoinformation Technology (HeiGIT).

Use Cases

• Transportation planning based on road surface type and highway classification.
• Infrastructure analysis based on attributes like surface, smoothness, width, and bridge status.
• Climate emissions assessment based on road surface composition and urban/rural classification.
• GIS applications for logistics and humanitarian access based on geospatial road network data.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) from a hybrid deep learning approach.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS urban 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.
• Key statistics like total road length and coverage percentages are reported as 0.0 million km and nan%.

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

Mean timestamp of predictions is provided in the DL_mean_timestamp field.

Freshness

Last updated 2026-03-02 17:15:54.894159; freshness should be verified.

Geography

French Southern Territories