Road Surface Classification for Denmark from OpenStreetMap and AI

Approximately 0.2232 million kilometers of roads in Denmark are mapped, with AI-derived estimates classifying 0.1015 million km as paved and 0.0388 million km as unpaved. The dataset, created by the Heidelberg Institute for Geoinformation Technology (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 and 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

• Covers approximately 223,200 kilometers of roads in Denmark.
• Provides AI-derived surface classifications (paved/unpaved) for over 62% of the mapped road network.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban classification layers.

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.

Provenance

Source

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

Collection Method

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

Time Range

The mean timestamp of predictions used is recorded in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:13:35.148339; freshness should be verified.

Geography

Denmark.