Mauritania Road Surface and Passability Data for 5,700km of Arterial Roads

Delivering AI-derived road surface, width, and passability attributes for approximately 5,700 km of arterial roads in Mauritania. Produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT), the data utilizes PlanetScope satellite imagery from 2020 and 2024 to enhance OpenStreetMap (OSM) records. It specifically targets motorway, trunk, primary, and secondary road classes which form the country's national connectivity backbone.

Use Cases

• Monitoring national infrastructure development by identifying transitions from unpaved to paved in the 'Road Surface Change' column
• Route planning for heavy logistics by filtering for 'Road Width Class 3' segments which support unimpeded two-lane heavy truck flow
• Prioritizing humanitarian aid delivery routes based on the 'Humanitarian Passability Index (HPI)' scores

Strengths

• Surface model achieved 89.2% accuracy, significantly higher than the 64.7% accuracy of existing OSM surface tags
• Provides multi-temporal surface data for 2020 and 2024 to track infrastructure changes
• Standardized width classification (Classes 1-3) based on heavy truck flow capacity

Limitations

• Analysis is restricted to arterial roads and excludes the local or residential road network
• Predictions are AI-derived from satellite imagery and may contain errors not found in ground-truth surveys
• Geographic scope is limited to the national borders of Mauritania

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

AI-derived predictions from PlanetScope satellite imagery integrated with OpenStreetMap road geometries

Time Range

2020 - 2024

Freshness

Last updated March 2026; contains imagery-derived data from 2020 and 2024.

Geography

Mauritania