Monaco Road Surface, Width, and Passability from 2020-2024 Imagery

Monaco arterial road network data featuring AI-derived surface types, widths, and passability scores generated by HeiGIT. The dataset utilizes PlanetScope satellite imagery from 2020 and 2024 to augment OpenStreetMap road segments with deep-learning predictions. It specifically covers motorway, trunk, primary, and secondary road classes.

Use Cases

• Logistical planning using the Humanitarian Passability Index (HPI) to identify accessibility constraints for relief efforts
• Infrastructure monitoring via the Road Surface Change attribute to detect transitions from unpaved to paved surfaces
• Vehicle routing optimization based on Road Width Class (1-3) to determine suitability for heavy truck flow

Strengths

• 89.2% surface model accuracy outperforming standard OSM tags
• Multi-temporal comparison between 2020 and 2024 imagery
• Standardized 6-point Humanitarian Passability Index for logistical assessment

Limitations

• Excludes minor, residential, and unclassified road networks
• AI-derived predictions may contain localized errors compared to ground truth
• Geographic scope is limited to the territory of Monaco

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

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

Time Range

2020 - 2024

Freshness

Based on satellite imagery from 2020 and 2024; last updated March 2026.

Geography

Monaco