Road Surface Classification for Guernsey from OpenStreetMap and AI

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Guernsey. It distinguishes paved and unpaved roads, covering approximately 1 km of roads mapped in OpenStreetMap, with 22.4% paved and 8.6% unpaved. The data is augmented with deep learning predictions from Mapillary imagery and urban classification layers.

Use Cases

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

Strengths

• Includes AI-derived predictions for road surfaces, reducing OSM data gaps.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, and urban classification 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 69% 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 (OSM) and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

The dataset's temporal coverage is not explicitly stated.

Freshness

Last updated 2026-03-02 17:09:24.294325; freshness should be verified.

Geography

Guernsey