Slovenia Road Surface Classification from OSM and AI Predictions

Road surface data for Slovenia distinguishes paved and unpaved roads using a hybrid deep learning approach on OpenStreetMap and Mapillary imagery. The dataset covers approximately 106,100 kilometers of roads, with AI-derived estimates classifying 22.9% as paved and 16.3% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length data.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging the urban/rural classification of roads.
• GIS applications utilizing the geospatial geometry and combined surface classifications.

Strengths

• Provides AI-derived surface estimates for approximately 106,100 kilometers of roads.
• Includes multiple classification fields (pred_class, combined_surface_osm_priority, combined_surface_DL_priority) for analysis flexibility.
• Augments OSM data with deep learning predictions from Mapillary imagery to address data gaps.

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.
• Approximately 60.8% of road surface information is reported as missing in the underlying OSM data.

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, as detailed in referenced papers.

Time Range

Mean timestamp of predictions (DL_mean_timestamp) is included, but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:06:26.103256; freshness should be verified.

Geography

Slovenia.