Libya Arterial Road Surface and Passability: 21,100 km AI-Derived Data

This dataset maps 21,100 km of arterial roads in Libya, providing AI-derived surface types, widths, and passability scores from 2020 and 2024 PlanetScope satellite imagery. Produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT), it focuses on motorway, trunk, primary, and secondary road classes to assess national connectivity.

Use Cases

• Logistical planning using the Humanitarian Passability Index (HPI) to identify high-risk blockages
• Infrastructure monitoring by analyzing the Road Surface Change field for transitions from unpaved to paved
• Vehicle routing optimization based on Road Width Class to ensure heavy truck accessibility

Strengths

• 89.2% surface model accuracy
• Covers 21,100 km of road network
• Temporal change detection between 2020 and 2024
• Fills surface tag gaps for 15.8% of OSM segments

Limitations

• Geographic scope limited to Libya
• Excludes minor and residential road classes
• AI-derived predictions may have residual errors compared to ground-level surveys

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; contains temporal comparisons between 2020 and 2024.

Geography

Libya