Mayotte Road Surface, Width, and Passability Data (2020-2024)

HeiGIT generated this dataset of AI-derived road surface, width, and passability metrics for arterial roads in Mayotte using 2020 and 2024 PlanetScope imagery. It covers approximately 200 km of infrastructure, integrating OpenStreetMap attributes with deep-learning predictions to fill data gaps in the region.

Use Cases

• Logistics planning using 'Humanitarian Passability Index (HPI)' to identify high-risk blockage zones
• Infrastructure monitoring via 'Road Surface Change' to detect transitions from unpaved to paved segments
• Vehicle suitability analysis using 'Road Width Class' to determine if segments support two-lane heavy trucks

Strengths

• 89.2% surface prediction accuracy
• Multi-temporal comparison between 2020 and 2024
• Standardized width classification (Class 1-3) based on vehicle capacity

Limitations

• Geographic scope is limited to Mayotte
• AI-derived predictions may contain errors inherent to satellite imagery analysis such as cloud cover interference
• Restricted to arterial road classes only

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

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

Time Range

2020-2024

Freshness

Last updated in March 2026, with temporal comparisons between 2020 and 2024 imagery.

Geography

Mayotte