Mali Road Surface Classification with AI-Derived Paved and Unpaved Estimates

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides detailed road surface information for Mali. It contains approximately 0.4783 million kilometers of roads from OpenStreetMap, augmented with deep learning predictions from Mapillary imagery to classify surfaces as paved or unpaved. The dataset is intended for transportation planning, infrastructure analysis, and climate emissions assessment.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis based on road attributes like surface type, width, and lanes.
• Climate emissions assessment based on road surface type and urban/rural classification.
• GIS applications for routing and accessibility studies based on the geospatial road network.

Strengths

• Provides AI-derived estimates for 0.4783 million kilometers of roads, classifying 1.6908% as paved and 68.0296% as unpaved.
• Augments OpenStreetMap data with deep learning predictions to address a 30.2796% surface information gap.
• Includes urban classification attributes (e.g., urban/rural flag) from GHSU and AFRICAPOLIS layers.

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.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:15:23.969909; freshness should be verified.

Geography

Mali