Mauritania Road Surface Classification from OSM and AI Predictions

HeiGIT's dataset provides detailed road surface information for Mauritania, distinguishing paved and unpaved roads. It contains approximately 0.0871 million km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 0.0123 million km as paved and 0.0433 million km as unpaved. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment based on paved vs. unpaved road classifications.
• GIS applications leveraging the geospatial road network data.

Strengths

• Includes AI-derived surface predictions for roads where OSM data is missing.
• Combines data from multiple sources: OSM, Mapillary imagery, and urban classification layers.
• Provides specific road length statistics, including 0.0123 million km of paved roads.

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

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary imagery.

Time Range

The mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:13:57.082281; freshness should be verified.

Geography

Mauritania.