Tunisia Road Surface Classification from OpenStreetMap and AI Predictions

HeiGIT's dataset provides AI-derived classifications for road surfaces in Tunisia, distinguishing paved from unpaved roads. It covers approximately 202,600 km of roads mapped in OpenStreetMap, with 11.75% classified as paved and 9.66% as unpaved. The data was last updated in March 2026 and combines OSM data with deep learning predictions from Mapillary imagery.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission factors.
• GIS applications integrating urban classification attributes like rural/urban flags.
• Validating and augmenting OpenStreetMap data with AI-predicted surface classes.

Strengths

• Includes AI-derived predictions for road surface, addressing a 78.59% missing data gap in OSM.
• Combines multiple data sources: OSM, Mapillary imagery, and GHSU/AFRICAPOLIS urban layers.
• Provides detailed OSM and urban classification attributes for contextual analysis.

Limitations

• 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)

Collection Method

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

Time Range

null

Freshness

Last updated 2026-03-02 17:12:36.355002; freshness should be verified.

Geography

Tunisia