Nauru Arterial Road Surface and Width Data (2020-2024)

HeiGIT produced this geospatial dataset providing AI-derived road surface, width, and passability metrics for Nauru's arterial road network using PlanetScope imagery from 2020 and 2024. It covers motorway, trunk, primary, and secondary road classes, offering 89.2% accuracy for surface predictions compared to 64.7% in OpenStreetMap.

Use Cases

• Evaluating vehicle accessibility for disaster relief using the Humanitarian Passability Index (HPI) and Road Width Class
• Identifying infrastructure improvements by tracking transitions in the Road Surface Change (2020-2024) field
• Validating and updating OpenStreetMap data by comparing the surface tag with DL Surface Prediction 2024

Strengths

• 89.2% accuracy for AI-derived surface models
• Multi-temporal coverage comparing 2020 and 2024 imagery
• Standardized width classification (Class 1-3) for logistical planning

Limitations

• Restricted to arterial road classes, omitting local and residential roads
• Small sample size due to Nauru's limited total road network length
• Potential for AI prediction errors in areas with dense canopy or cloud cover

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

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

Time Range

2020-2024

Freshness

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

Geography

Nauru