Nepal Arterial Road Surface and Passability Data (2020-2024)

Approximately 11,100 km of Nepal's arterial roads are mapped with AI-derived surface and width attributes generated by HeiGIT. Using PlanetScope satellite imagery from 2020 and 2024, the data provides surface predictions and a Humanitarian Passability Index for motorway, trunk, primary, and secondary road classes.

Use Cases

• Evaluating vehicle accessibility for disaster response using the Humanitarian Passability Index (HPI) scores
• Identifying infrastructure upgrades by filtering for Road Surface Change from unpaved to paved between 2020 and 2024
• Optimizing transport logistics based on Road Width Class to determine heavy truck suitability and blockage risks

Strengths

• 89.2% surface prediction accuracy outperforming standard OSM tags
• Provides surface data for 6,800 km of roads previously missing tags in OSM
• Standardized width classification based on heavy truck capacity

Limitations

• Restricted to arterial road classes only
• AI-derived predictions may have reduced accuracy in areas with dense canopy cover or extreme shadows
• Surface change detection is limited to the specific 2020-2024 interval

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

Deep learning analysis of PlanetScope satellite imagery combined with OpenStreetMap road geometries

Time Range

2020-2024

Freshness

Contains imagery-derived data from 2020 and 2024; last updated in March 2026.

Geography

Nepal