French Polynesia Road Surface Classification from OSM and Satellite Imagery

A geospatial dataset from HeiGIT, last updated March 2026, providing road surface classifications for French Polynesia. It distinguishes paved from unpaved roads using a hybrid deep learning approach on OpenStreetMap data, augmented with Mapillary imagery and PlanetScope satellite data. The dataset includes AI-derived predictions, OSM attributes, and urban classification layers.

Use Cases

• Transportation planning based on road surface type and highway classification.
• Infrastructure analysis using attributes like surface, smoothness, width, and bridge status.
• Climate emissions assessment based on paved vs. unpaved road classifications.
• GIS applications for routing and logistics using geospatial road network data.
• Urban/rural development studies using the urban classification attribute.

Strengths

• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, PlanetScope satellite data, and urban settlement layers.
• Provides multiple classification fields, including AI predictions (pred_class), OSM tags (osm_surface_class), and combined priorities.
• Includes urban/rural classification (urban attribute) and extensive OSM metadata like name, highway type, and lanes.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The description states approximately 0.0 million km of roads are mapped, suggesting the dataset may be illustrative or a template rather than containing populated length values.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:24:15.143186; freshness should be verified.

Geography

French Polynesia