Iceland Road Surface Classification from OpenStreetMap and AI

Approximately 51,500 km of roads in Iceland are mapped, with AI-derived estimates classifying 29.5% as paved and 40.3% as unpaved. This dataset from HeiGIT combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated on March 2, 2026.

Use Cases

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

Strengths

• Covers approximately 51,500 km of roads in Iceland.
• Provides AI-derived surface classifications for roads where OSM data is missing.
• Includes multiple classification schemes (OSM-priority and DL-priority) for flexibility.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• About 30.2% (15,500 km) of road surface information is missing in the original OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers (GHSL, AFRICAPOLIS).

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in a referenced arXiv paper.

Time Range

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

Freshness

Last updated 2026-03-02 17:06:13.649342; freshness should be verified.

Geography

Iceland.