Bulgaria Road Surface Classification from OpenStreetMap and AI

Approximately 0.2248 million kilometers of roads are mapped for Bulgaria, with AI-derived estimates classifying 16.757% as paved and 7.3622% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery to fill information gaps. It includes attributes for highway type, surface, and urban classification.

Use Cases

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

Strengths

• Covers approximately 0.2248 million kilometers of roads in Bulgaria.
• 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 75.8808% of road surface information is originally 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 is provided via the DL_mean_timestamp attribute.

Freshness

Last updated 2026-03-02 17:06:09.068902; freshness should be verified.

Geography

Bulgaria.