Estonia Road Surface Classification from OSM and AI Predictions

HeiGIT's dataset provides detailed road surface information for Estonia, distinguishing paved from unpaved roads. It contains approximately 94,300 km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 27.95% as paved and 15.43% as unpaved. The data was last updated on March 2, 2026, and combines OSM attributes with deep learning predictions from Mapillary imagery.

Use Cases

• Transportation planning based on road surface type and length data.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment informed by paved versus unpaved road proportions.
• GIS applications leveraging geospatial road network data with urban/rural classification.

Strengths

• Contains approximately 94,300 km of roads mapped from OpenStreetMap.
• Provides AI-derived surface estimates for 27.95% paved and 15.43% unpaved roads.
• Integrates multiple data sources: OSM, Mapillary imagery, and urban settlement layers.
• Includes detailed attributes like predicted class, OSM tags, and urban classification.

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 and file size are unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap (OSM) and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

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Freshness

Last updated 2026-03-02 17:23:32.264675; freshness should be verified.

Geography

Estonia