Saudi Arabia Road Surface and Passability for 117,800 km of Arterial Roads

This dataset maps 117,800 km of arterial roads in Saudi Arabia using AI-derived attributes from PlanetScope satellite imagery captured in 2020 and 2024. Produced by HeiGIT, it provides surface type, width classes, and passability scores for motorway, trunk, primary, and secondary road segments. The analysis fills critical data gaps where OpenStreetMap (OSM) tags are missing, achieving 89.2% accuracy in surface prediction.

Use Cases

• Monitoring infrastructure development by identifying segments that transitioned from unpaved to paved via the 'Road Surface Change' field
• Logistics and heavy transport routing using 'Road Width Class' to identify segments wider than 5.5 meters (Class 3)
• Humanitarian accessibility mapping using the 'Humanitarian Passability Index (HPI)' to identify high-risk or restricted transit corridors

Strengths

• AI surface model achieves 89.2% accuracy, significantly higher than the 64.7% accuracy of existing OSM surface tags
• Provides surface data for 77,600 km of roads that lack tags in OpenStreetMap
• Includes temporal change detection between 2020 and 2024 imagery

Limitations

• Geographic scope is limited to arterial roads, omitting the majority of the minor road network
• Predictions may include 'Unknown' labels where satellite imagery was obstructed or inconclusive

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Deep learning analysis of PlanetScope satellite imagery integrated with OpenStreetMap road geometries

Time Range

2020 - 2024

Freshness

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

Geography

Saudi Arabia