Mauritius Road Surface and Passability Data from PlanetScope (2020-2024)

HeiGIT generated this dataset of road surface, width, and passability metrics for 1,400 km of arterial roads in Mauritius using 2020 and 2024 PlanetScope satellite imagery. It combines deep-learning predictions with OpenStreetMap attributes to provide a 89.2% accurate assessment of road conditions for motorway, trunk, primary, and secondary classifications.

Use Cases

• Monitoring infrastructure upgrades using the 'Road Surface Change' column to identify newly paved segments
• Logistics planning for heavy machinery using 'Road Width Class' to identify routes wider than 5.5 meters
• Humanitarian access mapping using the 'Humanitarian Passability Index (HPI)' to rank route accessibility

Strengths

• 89.2% accuracy for the PlanetScope-derived road surface model
• Temporal coverage spanning 2020 to 2024 for change detection
• Covers 1,400 km of arterial road segments in Mauritius

Limitations

• Excludes local and residential road classifications
• AI-derived predictions may contain classification errors despite high accuracy
• Geographic scope is limited to Mauritius

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

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

Time Range

2020-2024

Freshness

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

Geography

Mauritius