Timor-Leste Road Surface Classification from OSM and Satellite Imagery

Approximately 12,300 km of roads are mapped in OpenStreetMap for Timor-Leste. The dataset, created by HeiGIT, uses a hybrid deep learning approach to classify surfaces as paved or unpaved, augmenting OSM data with predictions from Mapillary imagery and urban layers. It was last updated in March 2026.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using road surface and highway type attributes.
• Climate emissions assessment based on road network composition.
• GIS applications for urban/rural classification using the urban attribute.

Strengths

• Includes AI-derived surface classifications for approximately 12,300 km of roads.
• Combines data from multiple sources: OSM, Mapillary imagery, and urban settlement layers.
• Provides multiple classification outputs (OSM-priority, DL-priority) for comparison.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Approximately 64% of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

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 17:20:45.016199; freshness should be verified.

Geography

Timor-Leste.