Uganda Road Surface Classification from OpenStreetMap and AI Predictions

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Uganda. It covers approximately 416,600 kilometers of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The dataset distinguishes paved from unpaved roads and is intended for transportation and infrastructure analysis.

Use Cases

• Transportation planning based on the classification of paved versus unpaved roads.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission modeling.
• GIS applications for routing and logistics based on urban/rural classification and road network attributes.

Strengths

• Covers approximately 416,600 kilometers of roads in Uganda.
• Provides AI-derived predictions to fill gaps, adding surface information for about 2,000 kilometers of roads missing in OSM.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural attributes for nuanced analysis.

Limitations

• Approximately 275,900 kilometers (66.2%) of road surface information is missing in the underlying OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap (OSM) and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

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Freshness

Last updated 2026-03-02 17:21:09.045005; freshness should be verified.

Geography

Uganda