India Road Surface Classification from OpenStreetMap and AI Predictions

A geospatial dataset detailing road surfaces across India, derived from OpenStreetMap and augmented with AI predictions from street-level imagery. The dataset covers approximately 4.8 million kilometers of roads, with AI-derived estimates classifying 0.53 million km as paved and 0.29 million km as unpaved. It was created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission factors.
• GIS applications for routing and accessibility studies using the geospatial road network.

Strengths

• Covers approximately 4.8 million kilometers of roads mapped in OpenStreetMap.
• Provides AI-derived surface estimates for over 0.8 million kilometers of roads.
• Includes detailed OSM attributes such as highway type, surface, smoothness, and width.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Approximately 83% of road surface information is reported as missing in the base OSM data.

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 the referenced arXiv papers.

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:29:57.395878; freshness should be verified.

Geography

India