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

AI-derived road surface, width, and passability metrics for approximately 5,700 km of arterial roads in Congo, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to fill gaps where surface tags are missing, covering motorway, trunk, primary, and secondary road classes.

Use Cases

• Evaluating logistical accessibility for humanitarian aid delivery using the Humanitarian Passability Index (HPI) scores
• Monitoring national infrastructure development by tracking transitions in the Road Surface Change (2020-2024) indicator
• Route planning for heavy transport vehicles based on Road Width Class to identify segments supporting two-lane flow

Strengths

• 89.2% accuracy for road surface model predictions
• Fills surface data for 39.7% of OSM segments that previously lacked tags
• Multi-temporal coverage (2020 and 2024) allows for infrastructure change detection

Limitations

• AI-derived predictions may contain errors compared to physical ground-truth surveys
• Limited to arterial road networks, omitting the majority of rural and local tracks

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

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

Time Range

2020-2024

Freshness

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

Geography

Congo