Poland Road Surface Classification from OSM and AI Predictions

Detailed road surface data for Poland, distinguishing paved and unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, combined with urban classification layers. It was last updated on March 2, 2026, by the Heidelberg Institute for Geoinformation Technology (HeiGIT).

Use Cases

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

Strengths

• Covers approximately 1.3337 million kilometers of roads mapped in OSM.
• Provides AI-derived estimates for 0.3707 million km of paved and 0.1929 million km of unpaved roads.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural attributes.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• About 0.7701 million km (57.7413%) of road surface information is missing in the base OSM data.

Provenance

Source

OpenStreetMap (OSM), Mapillary imagery, GHSU Global Human Settlement Urban Layer 2019, AFRICAPOLIS 2020.

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in referenced papers.

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' column.

Freshness

Last updated 2026-03-02 17:27:39.831524; freshness should be verified.

Geography

Poland.