Bermuda Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides AI-derived road surface classifications for Bermuda, distinguishing paved from unpaved roads. It is based on OpenStreetMap data augmented with deep learning predictions from Mapillary imagery and urban layers. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using road attributes like surface type, width, and lanes.
• Climate emissions assessment leveraging road surface data for vehicle emission modeling.
• GIS applications integrating urban classification attributes for spatial analysis.

Strengths

• Includes AI-derived surface predictions (paved/unpaved) for approximately 0.0009 million km of roads.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS urban layers.
• Provides detailed OSM and urban classification attributes for each road segment.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Approximately 57.5% of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:02:04.358601; freshness should be verified.

Geography

Bermuda