Road Surface Classification for the British Indian Ocean Territory from OSM and AI

British Indian Ocean Territory road data provides AI-derived surface classifications for paved and unpaved roads, based on OpenStreetMap and Mapillary imagery. The dataset, created by HeiGIT and last updated in March 2026, includes approximately 0.0001 million kilometers of mapped roads. It is intended for transportation planning, infrastructure analysis, and GIS applications.

Use Cases

• Transportation planning based on road surface type classifications.
• Infrastructure analysis based on attributes like highway type, surface, and width.
• Climate emissions assessment based on road material and urban/rural classification.
• GIS applications for humanitarian access and logistics based on spatial road network data.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) from a hybrid deep learning approach.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban layers from GHSU and AFRICAPOLIS.
• Provides detailed OSM and urban classification attributes for each road segment.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Approximately 71.5901% of road surface information is reported as missing in the underlying OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

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

Collection Method

Surface classification derived using a hybrid deep learning approach; methodology detailed in linked arXiv papers.

Time Range

Mean timestamp of predictions is provided per feature (DL_mean_timestamp).

Freshness

Last updated 2026-03-02 17:12:23.963741; freshness should be verified.

Geography

British Indian Ocean Territory.