Zimbabwe Road Surface Classification from OpenStreetMap and AI Predictions

Zimbabwe road network data from OpenStreetMap, enhanced with AI-derived surface classifications. The dataset covers approximately 294,500 km of roads, with deep learning predictions for paved and unpaved surfaces. It was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis based on OSM attributes like highway type, surface, and width.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications for routing and logistics based on urban/rural classification and road geometry.

Strengths

• Covers approximately 294,500 km of roads in Zimbabwe.
• Provides AI-derived surface classifications for paved (16,000 km) and unpaved (40,300 km) roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.

Limitations

• Approximately 238,100 km (80.86%) 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 is unknown, which may limit suitability assessment.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in the referenced arXiv paper.

Time Range

Mean timestamp of predictions is provided (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:17:23.045011; freshness should be verified.

Geography

Zimbabwe.