Comoros Road Surface and Passability Data with 2020-2024 AI Predictions

Delivering AI-derived road surface, width, and passability metrics for approximately 300 kilometers of arterial roads in Comoros, developed by the Heidelberg Institute for Geoinformation Technology (HeiGIT). It utilizes PlanetScope satellite imagery from 2020 and 2024 to fill gaps in OpenStreetMap (OSM) data, where over 60% of surface tags were previously missing. The analysis focuses on motorway, trunk, primary, and secondary road classifications.

Use Cases

• Logistics planning using the 'Humanitarian Passability Index' to identify segments with high blockage risk
• Infrastructure monitoring by analyzing 'Road Surface Change' to identify transitions from unpaved to paved roads
• Transportation modeling based on 'Road Width Class' to determine heavy truck accessibility

Strengths

• 89.2% accuracy for road surface predictions compared to 64.7% for standard OSM tags
• Provides surface data for 60.35% of segments that lack tags in OpenStreetMap
• Multi-temporal coverage allows for tracking infrastructure development between 2020 and 2024

Limitations

• Small geographic footprint restricted to the Comoros archipelago
• AI-derived predictions may contain errors relative to ground-truth physical surveys
• Focuses exclusively on high-capacity arterial roads

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

Comoros