Belarus Road Surface Classification from OpenStreetMap and Deep Learning

A geospatial dataset detailing road surfaces in Belarus, distinguishing paved from unpaved roads. It covers approximately 0.3754 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 22.13% as paved and 29.19% as unpaved. The dataset was created by HeiGIT using a hybrid deep learning approach and was last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length estimates.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment based on road network composition and urban/rural classification.
• GIS applications for routing and logistics using the geospatial road network data.

Strengths

• Provides AI-derived surface classification for approximately 0.3754 million kilometers of roads.
• Includes multiple classification schemes (OSM-priority and DL-priority) for flexibility.
• Augments OSM data with deep learning predictions from Mapillary imagery to address missing information.

Limitations

• Approximately 48.68% of road surface information is missing in the original OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are 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, as detailed in the referenced papers.

Time Range

null

Freshness

Last updated 2026-03-02 17:27:30.351505; freshness should be verified.

Geography

Belarus