Sudan Road Surface Classification from OpenStreetMap and AI Predictions

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Sudan. It covers approximately 501,200 kilometers of roads from OpenStreetMap, distinguishing between paved and unpaved surfaces. The data is augmented 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 using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for transport modeling.
• GIS applications integrating urban/rural classification and geospatial road networks.

Strengths

• Covers approximately 501,200 kilometers of roads in Sudan.
• Provides AI-derived surface classifications (paved/unpaved) for roads missing OpenStreetMap tags.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• About 37.4% of road surface information is reported as missing in the underlying OpenStreetMap data.

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, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:20:57.509603; freshness should be verified.

Geography

Sudan