Azerbaijan Road Surface Classification from OpenStreetMap and AI Predictions

HeiGIT's dataset provides AI-derived road surface classifications for Azerbaijan, distinguishing paved and unpaved roads. It covers approximately 0.1237 million km of roads mapped in OpenStreetMap, with deep learning predictions augmenting missing surface data. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using predicted road surface types and lengths.
• Climate emissions assessment based on road surface material data.
• GIS applications leveraging combined OSM and deep learning surface classifications.

Strengths

• Includes AI-derived predictions for approximately 0.1237 million km of roads.
• Provides specific statistics: 6.7686% paved and 6.3892% unpaved roads based on AI estimates.
• Augments OSM data with Mapillary-derived predictions for an additional 0.0006 million km of missing surface information.

Limitations

• A significant portion (86.8422%) of road surface information is missing in the original OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is 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 imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:30:52.266965; freshness should be verified.

Geography

Azerbaijan