Saint Pierre and Miquelon: Road Surface and Passability Data (2020-2024)

HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for arterial roads in Saint Pierre and Miquelon using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap (OSM) data by providing surface predictions for segments lacking tags, achieving 89.2% accuracy in surface classification.

Use Cases

• Analyzing infrastructure development by comparing 'DL Surface Prediction 2020' and 'DL Surface Prediction 2024' to identify unpaved-to-paved transitions
• Planning emergency logistics using the 'Humanitarian Passability Index (HPI)' to identify routes suitable for heavy trucks
• Assessing vehicle restrictions based on 'Road Width Class' to determine if segments support two-lane flow or are restricted to light vehicles

Strengths

• 89.2% accuracy for surface model predictions compared to 64.7% for standard OSM tags
• Temporal surface change detection between 2020 and 2024
• Standardized logistical scoring via the 1-6 Humanitarian Passability Index

Limitations

• Geographic scope is limited to Saint Pierre and Miquelon
• Excludes non-arterial road classes which may be critical for last-mile logistics
• AI predictions may be affected by satellite imagery artifacts or cloud cover

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

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

Time Range

2020-2024

Freshness

Last updated March 2026; contains temporal comparisons between 2020 and 2024 imagery.

Geography

Saint Pierre and Miquelon