Rwanda Road Surface Classification from OpenStreetMap and AI Predictions

Approximately 125,600 km of roads are mapped in OpenStreetMap for Rwanda, with AI-derived estimates classifying 5.6% as paved and 28.1% as unpaved. This dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OSM data with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length estimates.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment based on paved versus unpaved road networks.
• GIS applications leveraging urban/rural classification and geospatial road data.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban classification layers.
• Provides AI-derived surface estimates for approximately 125,600 km of roads.
• Includes detailed OSM attributes like highway type, surface, width, and lanes.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Approximately 66.3% of road surface information is missing in the underlying OSM data.
• 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 and augmented with deep learning predictions based on Mapillary imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:25:37.070045; freshness should be verified.

Geography

Rwanda