Road Surface Classification for Greece from OpenStreetMap and AI

Approximately 0.498 million kilometers of roads are mapped from OpenStreetMap for Greece, with AI-derived estimates classifying 18.85% as paved and 8.47% as unpaved. The dataset is produced by HeiGIT (Heidelberg Institute for Geoinformation Technology) and was last updated in March 2026. It combines OSM data with deep learning predictions from Mapillary imagery and urban classification layers.

Use Cases

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

Strengths

• Provides AI-derived surface classification for approximately 0.498 million km of roads.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural labels.
• Augments OSM data with deep learning predictions to address missing surface information.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and file size are unknown, which may limit suitability assessment.
• About 72.68% of road surface information is reported as missing in the original OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with Mapillary imagery and GHSU/AFRICAPOLIS urban layers.

Collection Method

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

Time Range

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

Freshness

Last updated 2026-03-02 17:22:32.153875; freshness should be verified.

Geography

Greece.