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

Giving access to AI-derived road surface, width, and passability metrics for approximately 500 km of arterial roads in Malta, produced by HeiGIT. It utilizes PlanetScope satellite imagery from 2020 and 2024 to supplement OpenStreetMap data with deep-learning predictions for segments lacking ground-truth tags.

Use Cases

• Evaluating logistical accessibility for heavy transport using the 'Humanitarian Passability Index (HPI)'
• Identifying infrastructure improvements by filtering for 'Road Surface Change (2020-2024)' transitions
• Enhancing navigation datasets by integrating 'DL Surface Prediction 2024' where OSM surface tags are null

Strengths

• 89.2% accuracy for AI-derived surface predictions
• Multi-temporal surface data for 2020 and 2024
• Fills surface tag gaps for 20.6% of OSM road segments in the region

Limitations

• Geographic scope is limited to Malta
• Excludes non-arterial road classes
• AI-derived width classes may have lower precision than ground-truth surveys

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

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

Time Range

2020-2024

Freshness

Last updated March 2026; imagery covers 2020 and 2024.

Geography

Malta