Finland Arterial Road Surface and Passability Data (2020-2024)

This dataset maps 33,200 km of arterial roads in Finland, providing AI-derived surface types and width classifications generated by HeiGIT from 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to fill surface tag gaps for approximately 10.8% of the national arterial network. The data focuses on motorway, trunk, primary, and secondary road classes and their links.

Use Cases

• Logistics and accessibility planning using the Humanitarian Passability Index (HPI) to identify vehicle constraints
• Infrastructure development monitoring by analyzing the Road Surface Change column for paving progress
• Heavy vehicle routing optimization based on Road Width Class to ensure unimpeded flow

Strengths

• 89.2% accuracy for AI-derived surface models
• Fills 90.8% of missing OSM surface tags for the mapped extent
• Includes multi-temporal change detection for unpaved-to-paved transitions

Limitations

• AI-derived predictions may contain errors compared to ground-level reality
• Road width is provided as categorical classes (1-3) rather than continuous meter measurements
• Geographic focus is restricted to Finland's arterial network only

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Deep learning analysis of PlanetScope satellite imagery integrated with OpenStreetMap vector data

Time Range

2020 - 2024

Freshness

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

Geography

Finland