Curaçao Road Surface and Passability: AI-Derived Metrics for Arterial Roads

Delivering AI-derived road surface, width, and passability metrics for approximately 300 km of arterial roads in Curaçao, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to fill gaps where surface tags are missing or inaccurate.

Use Cases

• Monitoring infrastructure progress by identifying transitions from unpaved to paved roads using the Road Surface Change attribute
• Logistics planning for heavy machinery by filtering for Road Width Class 3 (>5.5 m) to ensure unimpeded flow
• Prioritizing humanitarian aid routes based on the Humanitarian Passability Index (HPI) scores

Strengths

• AI surface model achieves 89.2% accuracy, significantly higher than the 64.7% accuracy of OSM surface tags
• Includes multi-temporal surface predictions (2020 vs 2024) for change detection
• Provides standardized width classes based on physical vehicle capacity (e.g., <3.5m vs >5.5m)

Limitations

• Small geographic sample size limited to Curaçao's arterial network
• AI-derived predictions may contain errors in complex urban or heavily shadowed environments
• Excludes minor road networks which may be critical for last-mile humanitarian access

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Deep learning analysis of PlanetScope satellite imagery (3m resolution) integrated with OpenStreetMap vector data.

Time Range

2020-2024

Freshness

Last updated March 2026; includes satellite-derived data from 2020 and 2024.

Geography

Curaçao