Burundi Road Surface and Passability: 5,500 km of AI-Derived Data

HeiGIT produced this geospatial dataset covering approximately 5,500 km of arterial roads in Burundi using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides deep-learning predictions for road surface, width, and a Humanitarian Passability Index (HPI) to fill significant gaps in OpenStreetMap (OSM) data. The analysis focuses specifically on motorway, trunk, primary, and secondary road classes.

Use Cases

• Logistics planning using the Humanitarian Passability Index (HPI) to identify high-capacity transport routes
• Infrastructure monitoring by analyzing Road Surface Change to track paving progress between 2020 and 2024
• Emergency response routing based on Road Width Class to determine if heavy trucks can pass unimpeded

Strengths

• 89.2% surface prediction accuracy
• Covers 5,500 km of arterial road network
• Includes temporal change detection between 2020 and 2024
• Provides standardized Humanitarian Passability Index (HPI) scores

Limitations

• Restricted to arterial road classes only
• AI-derived predictions may have errors compared to ground-level surveys
• Geographic scope is limited to Burundi

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

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

Time Range

2020 - 2024

Freshness

Imagery analysis covers 2020 and 2024; last updated in March 2026.

Geography

Burundi