Angola Road Surface Classification from OpenStreetMap and AI

Road surface data for Angola, distinguishing paved and unpaved roads, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset covers approximately 0.284 million kilometers of roads, with AI-derived estimates classifying 8.46% as paved and 38.90% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the distinction between paved and unpaved road networks.
• GIS applications for routing and logistics using the geospatial road network data.
• Urban-rural classification analysis based on the urban attribute (0/1).

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban layers from GHSU and AFRICAPOLIS.
• Provides explicit statistics: 0.284 million km of roads mapped, with 8.4616% paved and 38.9039% unpaved.
• Includes AI-derived features like predicted surface class and confidence metrics.
• Available in standard geospatial formats (GeoJSON, GeoPackage).

Limitations

• A significant portion (52.6345%) of road surface information is missing in the base 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), based on OpenStreetMap.

Collection Method

Hybrid deep learning approach applied to Mapillary street-level imagery, combined with OSM tags and urban classification layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:23:29.867556; freshness should be verified.

Geography

Angola