Yemen 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 Yemen. It covers approximately 122,500 kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning method. The data is augmented with predictions from Mapillary imagery and urban layers.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using road attributes like surface type, width, and bridge status.
• Climate emissions assessment leveraging road surface data for vehicle emission factors.
• GIS applications for routing and logistics using the geospatial road network.

Strengths

• Includes AI-derived surface predictions for approximately 122,500 kilometers of roads.
• Provides detailed OSM attributes like highway type, surface, smoothness, and width.
• Combines multiple data sources: OSM, deep learning from Mapillary, and urban classification layers.

Limitations

• Approximately 37.7% 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 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

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Freshness

Last updated 2026-03 02 17:31:51.500143; freshness should be verified.

Geography

Yemen