Road Surface Classification for the Netherlands from OSM and AI

Approximately 378,600 kilometers of roads in the Netherlands are mapped, with AI-derived estimates classifying 169,800 km as paved and 41,100 km as unpaved. This dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) combines OpenStreetMap data with deep learning predictions from Mapillary imagery to fill information gaps. It was last updated in March 2026 and supports transportation and infrastructure analysis.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis based on road surface type, width, and lane data.
• Climate emissions assessment based on road surface material and urban/rural classification.
• GIS applications for routing and logistics based on the geospatial road network.

Strengths

• Covers approximately 378,600 km of roads in the Netherlands.
• 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.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

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

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

Mean timestamp of predictions is provided per feature (DL_mean_timestamp).

Freshness

Last updated 2026-03-02 17:25:48.849968; freshness should be verified.

Geography

Netherlands (Kingdom of the).