Honduras Road Surface and Passability for 6,400 km of Arterial Roads

HeiGIT produced this dataset of AI-derived surface, width, and passability metrics for approximately 6,400 km of arterial roads in Honduras using 2020 and 2024 PlanetScope satellite imagery. It supplements OpenStreetMap (OSM) data by providing surface predictions for the 21% of segments lacking tags, focusing on motorway, trunk, primary, and secondary classifications.

Use Cases

• Evaluating disaster relief logistics using the Humanitarian Passability Index (HPI) to identify high-risk segments
• Monitoring national infrastructure progress by analyzing the Road Surface Change column for unpaved-to-paved transitions
• Planning heavy transport routes using Road Width Class to identify segments supporting two-lane heavy truck flow

Strengths

• Surface model accuracy of 89.2% compared to 64.7% for OSM tags
• Multi-temporal data for 2020 and 2024 enables infrastructure change analysis
• Standardized Humanitarian Passability Index (HPI) for logistical planning

Limitations

• Geographic scope is limited to arterial road networks
• AI predictions may be affected by environmental factors like cloud cover or shadows in satellite imagery
• HPI scores are static model outputs and do not reflect real-time traffic or weather conditions

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

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

Time Range

2020-2024

Freshness

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

Geography

Honduras