Austria Road Surface Classification from OSM and Satellite Imagery

HeiGIT's dataset provides AI-derived road surface classifications for Austria, distinguishing paved from unpaved roads. It covers approximately 0.5122 million kilometers of roads from OpenStreetMap, augmented with deep learning predictions from Mapillary imagery. The dataset was last updated on March 2, 2026.

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 leveraging the distinction between paved and unpaved road networks.
• GIS applications utilizing the geospatial road network data with urban/rural classification.

Strengths

• Includes AI-derived surface classifications for approximately 0.5122 million kilometers of roads.
• Provides specific statistics: 0.11 million km paved (21.4685%) and 0.0709 million km unpaved (13.8363%).
• Augments OSM data with deep learning predictions to address missing surface information.
• Integrates multiple data sources: OSM, Mapillary imagery, and global human settlement layers.

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.

Time Range

null

Freshness

Last updated 2026-03-02 17:02:20.104535; freshness should be verified.

Geography

Austria