Cameroon Arterial Road Surface and Passability Data for 24,500 km

Offering AI-derived road attributes for approximately 24,500 km of arterial roads in Cameroon, generated by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It includes surface type, road width, and a Humanitarian Passability Index (HPI) for segments classified in OpenStreetMap as motorway, trunk, primary, and secondary. The analysis fills critical data gaps, providing surface information for 489% more road length than is currently tagged in OSM.

Use Cases

• Logistical planning for heavy vehicle transport using the Road Width Class (1-3) and Humanitarian Passability Index (HPI)
• Infrastructure development monitoring by identifying segments with Road Surface Change from unpaved to paved
• Enhancing navigation datasets by filling missing OSM surface tags using DL Surface Prediction 2024

Strengths

• Surface model achieved 89.2% accuracy compared to 64.7% for OSM tags
• Covers 24,500 km of the national arterial road network
• Includes temporal change detection for unpaved-to-paved transitions between 2020 and 2024

Limitations

• Attributes are AI-derived predictions rather than ground-truth physical measurements
• Geographic scope is restricted to Cameroon
• Limited to high-capacity roads, which may not reflect last-mile rural accessibility

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Deep learning analysis of PlanetScope satellite imagery integrated with OpenStreetMap road geometries.

Time Range

2020-2024

Freshness

Last updated March 2026; utilizes satellite imagery from 2020 and 2024.

Geography

Cameroon