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

HeiGIT produced this dataset covering approximately 1,100 km of arterial roads in Suriname using PlanetScope satellite imagery from 2020 and 2024. It combines OpenStreetMap data with AI-derived predictions for road surface, width, and a Humanitarian Passability Index to fill infrastructure data gaps.

Use Cases

• Logistics routing using the Humanitarian Passability Index (HPI) to identify segments suitable for heavy truck transport
• Infrastructure monitoring by tracking the Road Surface Change indicator to identify segments that transitioned from unpaved to paved
• Vehicle suitability analysis using Road Width Class (1-3) to determine if a segment supports two-lane flow

Strengths

• 89.2% accuracy for the PlanetScope-derived surface model
• Multi-temporal coverage comparing 2020 and 2024 imagery
• Standardized Humanitarian Passability Index (HPI) scores from 1 to 6

Limitations

• Limited to arterial road classes, omitting local and residential networks
• AI model accuracy of 89.2% indicates potential classification errors compared to ground truth
• Small geographic scale limited to Suriname's primary road network

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

AI-derived predictions from PlanetScope satellite imagery integrated with OpenStreetMap vector data

Time Range

2020-2024

Freshness

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

Geography

Suriname