Road Surface Classification for South Georgia and South Sandwich Islands via AI and OSM

HeiGIT provides AI-derived road surface classifications for South Georgia and the South Sandwich Islands, distinguishing paved from unpaved roads. The dataset combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and predicted length.
• Infrastructure analysis using combined OSM and deep learning surface classifications.
• Climate emissions assessment leveraging road surface data for vehicle emission modeling.
• GIS applications integrating urban classification attributes like continent and country codes.

Strengths

• Includes AI-derived surface predictions (paved/unpaved) with confidence metrics like 'n_of_predictions_used'.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS urban layers.
• Provides detailed OSM attributes including highway type, surface, smoothness, and bridge status.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• The description notes approximately 68.36% of road surface information is missing in the original 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

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:21:00.991258; freshness should be verified.

Geography

South Georgia and the South Sandwich Islands.