Senegal Road Surface Classification from OSM and Deep Learning

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Senegal. It covers approximately 243,600 kilometers of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The data distinguishes paved and unpaved roads and is intended for transportation and infrastructure analysis.

Use Cases

• Transportation planning based on paved versus unpaved road network data.
• Infrastructure analysis using road surface classifications and attributes like highway type and width.
• Climate emissions assessment leveraging road surface data for vehicle emission modeling.
• GIS applications integrating urban/rural classification and road geometry.

Strengths

• Covers approximately 243,600 kilometers of roads in Senegal.
• Provides AI-derived surface classifications for roads where OSM data is missing.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural labels.
• Data is available in common geospatial formats (GeoJSON and GeoPackage).

Limitations

• Approximately 133,100 kilometers (54.63%) of road surface information is missing in the original OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file 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, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

The dataset's temporal coverage is not explicitly stated, but it was last updated in 2026.

Freshness

Last updated 2026-03-02 17:13:36.604785; freshness should be verified.

Geography

Senegal.