Barbados Road Surface, Width, and Passability Data (2020-2024)

HeiGIT developed this dataset providing AI-derived road surface, width, and passability metrics for approximately 400 km of arterial roads in Barbados using 2020 and 2024 PlanetScope imagery. It integrates OpenStreetMap (OSM) segments with deep-learning predictions to fill gaps where surface tags are missing or inaccurate.

Use Cases

• Evaluating logistical accessibility for disaster relief using the Humanitarian Passability Index (HPI) scores
• Identifying infrastructure improvements by monitoring the Road Surface Change column for unpaved-to-paved transitions
• Planning heavy vehicle routes based on Road Width Class 3 (>5.5 m) segments

Strengths

• 89.2% accuracy for road surface predictions
• Multi-temporal coverage (2020 and 2024) for change detection
• Standardized 6-point Humanitarian Passability Index for logistical planning

Limitations

• Geographic scope limited to Barbados
• Excludes minor and residential road networks
• AI-derived predictions may have errors in areas with dense vegetation or cloud cover

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Deep learning analysis of PlanetScope satellite imagery combined with OpenStreetMap road network data.

Time Range

2020-2024

Freshness

Last updated March 2026; contains temporal data from 2020 and 2024 imagery.

Geography

Barbados