Pitcairn Road Surface Classification from OpenStreetMap and Satellite Imagery

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for Pitcairn. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery and PlanetScope satellite data to label roads as paved or unpaved. The dataset is intended for transportation planning, infrastructure analysis, and GIS applications.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis leveraging road attributes like surface type, smoothness, and width.
• Climate emissions assessment using road surface data to model vehicle emissions.
• GIS applications integrating urban classification attributes like rural/urban flags.

Strengths

• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and PlanetScope satellite data.
• Includes AI-derived predictions for road surface type (paved/unpaved) with confidence metrics.
• Provides combined classifications that prioritize either OSM tags or deep learning predictions.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic bias inherent to the source platforms for a remote region.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary and PlanetScope imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:23:32.823429; freshness should be verified.

Geography

Pitcairn