Kyrgyzstan Road Surface Classification with AI-Derived Estimates

Approximately 0.0861 million kilometers of roads are mapped from OpenStreetMap for Kyrgyzstan, with AI-derived estimates classifying 9.7637% as paved and 14.5367% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OSM data with deep learning predictions from Mapillary imagery and urban classification layers. It is intended for transportation planning, infrastructure analysis, and climate emissions assessment.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using road attributes like surface type, width, and lanes.
• Climate emissions assessment based on road surface material data.
• GIS applications integrating urban classification attributes for rural/urban analysis.

Strengths

• Includes AI-derived surface classifications for approximately 86,100 km of roads.
• Combines multiple data sources: OSM, Mapillary imagery, and urban settlement layers.
• Provides multiple classification schemes (OSM-priority and DL-priority) for comparison.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Approximately 75.7% of road surface information is reported as missing in the original OSM data.
• Row count and file size are unknown, which may limit suitability assessment.

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 referenced arXiv papers.

Time Range

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Freshness

Last updated 2026-03-02 17:15:53.979761; freshness should be verified.

Geography

Kyrgyzstan