Cambodia Road Surface Classification from OpenStreetMap and Satellite Imagery

Cambodia's road network, covering approximately 122,600 kilometers, is classified by surface type using a hybrid deep learning approach. The dataset, created by HeiGIT, combines OpenStreetMap data with AI predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for transport modeling.
• GIS applications integrating urban classification attributes for rural/urban analysis.

Strengths

• Provides AI-derived surface classification for approximately 122,600 km of roads.
• Includes multiple classification schemes (OSM-priority and DL-priority) for flexibility.
• Augments OSM data with predictions from Mapillary street-level imagery to reduce information gaps.

Limitations

• Approximately 100,700 km (82.15%) 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 is unknown, which may limit suitability assessment.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery, GHSU Global Human Settlement Urban Layer 2019, and AFRICAPOLIS 2020.

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

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

Freshness

Last updated 2026-03-02 17:35:15.548251; freshness should be verified.

Geography

Cambodia.