Road Surface Classification for Bahrain from OSM and Satellite Imagery

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Bahrain. It covers approximately 14.8 thousand kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning approach. The data is augmented with predictions from Mapillary street-level imagery and urban layers from GHSU and AFRICAPOLIS.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis based on OSM attributes like highway type, surface, and width.
• Climate emissions assessment based on road surface type and urban/rural classification.
• GIS applications for routing and logistics based on geospatial road network data.

Strengths

• Includes AI-derived surface predictions for approximately 14.8 thousand kilometers of roads.
• Provides multiple classification fields (OSM, DL-prioritized, OSM-prioritized) for comparison.
• Augments OSM data with an additional 11.6% of missing surface information from Mapillary.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Approximately 43.9% of road surface information is missing in the underlying OSM data.
• 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 (OSM) and augmented with deep learning predictions based on Mapillary imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

Timestamp of predictions is included ('DL_mean_timestamp'), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:08:02.741521; freshness should be verified.

Geography

Bahrain