Mali Road Surface and Passability Data for 13,700 km of Arterial Roads

Delivering AI-derived road surface, width, and passability metrics for 13,700 km of arterial roads in Mali, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap attributes with deep-learning predictions to fill data gaps in the national transportation backbone.

Use Cases

• Logistics planning using the Humanitarian Passability Index (HPI) to identify accessible routes for heavy trucks
• Infrastructure development monitoring by analyzing the Road Surface Change (2020-2024) indicator
• Vehicle routing optimization based on Road Width Class to avoid high blockage risks for light vehicles

Strengths

• High classification accuracy of 89.2% for road surface predictions
• Temporal comparison capability with surface data from both 2020 and 2024
• Standardized width classification (Class 1-3) based on vehicle capacity

Limitations

• Limited to arterial roads, excluding local, residential, and unclassified street networks
• AI predictions may contain errors in areas with dense vegetation or cloud cover
• Relies on OpenStreetMap geometry which may have its own spatial inaccuracies

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Deep learning analysis of PlanetScope satellite imagery combined with OpenStreetMap vector data

Time Range

2020-2024

Freshness

Last updated March 2026 using satellite imagery from 2020 and 2024.

Geography

Mali