Czechia Road Surface Classification from OpenStreetMap and AI

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Czechia. It covers approximately 0.3757 million kilometers of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The dataset distinguishes paved and unpaved roads, supporting transportation and infrastructure analysis.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment informed by road surface material data.
• GIS applications leveraging urban/rural classification and geospatial geometry.

Strengths

• Covers approximately 0.3757 million kilometers of roads in Czechia.
• Provides AI-derived estimates for 0.0812 million km of paved and 0.0347 million km of unpaved roads.
• Augments OpenStreetMap data with deep learning predictions from Mapillary imagery.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural labels.

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.
• Approximately 69.15% of road surface information was originally missing from the underlying OSM data.

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 a referenced arXiv paper.

Time Range

The mean timestamp of predictions used is indicated by the DL_mean_timestamp field.

Freshness

Last updated 2026-03-02 17:01:53.468060; freshness should be verified.

Geography

Czechia.