Nepal Road Surface Classification from OpenStreetMap and Satellite Imagery

Approximately 0.29 million kilometers of roads are mapped in OpenStreetMap for Nepal. The dataset classifies road surfaces as paved or unpaved using a hybrid deep learning approach based on Mapillary imagery, augmented with urban classification layers. It was published 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 and urban/rural classification data.
• GIS applications for routing and network analysis based on geospatial road data.

Strengths

• Covers approximately 0.29 million kilometers of roads in Nepal.
• Provides AI-derived surface estimates for paved (0.0075 million km) and unpaved (0.0495 million km) roads.
• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban classification layers.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

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

Time Range

null

Freshness

Last updated 2026-03-02 17:20:21.028354; freshness should be verified.

Geography

Nepal