Road Surface Classification for Kazakhstan from OpenStreetMap and AI

HeiGIT's dataset provides AI-derived road surface classifications for Kazakhstan, distinguishing paved from unpaved roads. It covers approximately 0.5396 million kilometers of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging road surface data for vehicle emission models.
• GIS applications for urban/rural classification using the urban attribute.

Strengths

• Includes AI-derived predictions for 0.0798 million km of paved and 0.0875 million km of unpaved roads.
• Augments OpenStreetMap data with deep learning predictions from Mapillary imagery.
• Provides multiple classification outputs (OSM-priority, DL-priority) for flexibility.

Limitations

• Approximately 0.3722 million km (68.9883%) of road surface information is missing in the base OSM data.
• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

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Freshness

Last updated 2026-03-02 17:08:47.831875; freshness should be verified.

Geography

Kazakhstan