Brunei Darussalam Road Surface and Passability for 1,100 km of Arterial Roads

Supplying AI-derived road attributes for approximately 1,100 km of arterial roads in Brunei Darussalam, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap geometry with deep-learning predictions for surface type, width, and logistical passability across motorway, trunk, primary, and secondary road classes.

Use Cases

• Logistics planning using the Humanitarian Passability Index (HPI) to identify vehicle access constraints for heavy trucks
• Infrastructure monitoring by analyzing Road Surface Change to track transitions from unpaved to paved surfaces
• Transportation modeling using Road Width Class to estimate traffic capacity and potential blockage risks

Strengths

• 89.2% accuracy for AI-derived surface model predictions
• Provides surface data for 100% of segments missing OpenStreetMap surface tags
• Includes temporal change detection for road paving between 2020 and 2024

Limitations

• Small geographic scope restricted to Brunei Darussalam
• AI-derived predictions may contain errors compared to ground-truth surveys
• Focuses only on high-capacity transportation backbone, omitting rural tracks

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Deep learning analysis of PlanetScope satellite imagery combined with OpenStreetMap data extraction and classification.

Time Range

2020 - 2024

Freshness

The dataset uses imagery from 2020 and 2024, with the last metadata update in March 2026.

Geography

Brunei Darussalam