Bahamas Road Surface Classification from OpenStreetMap and AI

Bahamas road network data from OpenStreetMap, enhanced with AI-derived surface classifications. The dataset includes approximately 13,300 km of roads, with 19.7% classified as paved and 6.8% as unpaved. It was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment based on paved vs. unpaved road distribution.
• GIS applications leveraging geospatial road network data and urban classification attributes.

Strengths

• Includes AI-derived surface classifications for paved and unpaved roads.
• Combines data from multiple sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides specific road length statistics, including 0.0026 million km of paved roads.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• A significant portion (73.5%) of road surface information is reported as missing in the base 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, as detailed in the referenced papers.

Time Range

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

Freshness

Last updated 2026-03-02 17:23:16.345858; freshness should be verified.

Geography

The Bahamas.