Afghanistan 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 Afghanistan. It covers approximately 260,200 kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning model. The data is augmented with urban classification layers and street-level imagery predictions.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and bridge status.
• Climate emissions assessment based on road surface type and urban/rural classification.
• GIS applications for routing and logistics using the geospatial road network data.

Strengths

• Covers approximately 260,200 kilometers of roads in Afghanistan.
• Provides AI-derived surface classifications (paved/unpaved) for roads where OSM data is missing.
• Integrates multiple data sources: OpenStreetMap, deep learning predictions, and urban settlement layers.

Limitations

• Approximately 89,500 kilometers (34.38%) of road surface information is missing from the underlying OSM data.
• 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 imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

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Freshness

Last updated 2026-03-02 17:27:44.981590.

Geography

Afghanistan