Lithuania Road Surface Classification from OpenStreetMap and Satellite Imagery

Approximately 0.1639 million kilometers of roads are mapped from OpenStreetMap for Lithuania, with AI-derived estimates classifying 22.4649% as paved and 29.5771% as unpaved. The dataset is produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT) using a hybrid deep learning approach on Mapillary imagery and was last updated in March 2026. It includes OSM attributes, urban classifications, and AI prediction features to address gaps in surface information.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission factors.
• GIS applications for routing and network analysis based on geospatial road data.
• Urban versus rural road condition studies using the urban classification attribute.

Strengths

• Covers approximately 163,900 kilometers of roads in Lithuania.
• Provides AI-derived surface classifications for paved and unpaved roads, addressing a known data gap.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and global settlement layers.

Limitations

• About 47.9579% of road surface information is missing in the underlying 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 on street-level imagery.

Time Range

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

Freshness

Last updated 2026-03-02 17:05:18.409267; freshness should be verified.

Geography

Lithuania.