New Caledonia Road Surface Classification from OSM and Deep Learning

HeiGIT's dataset provides detailed road surface information for New Caledonia, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban layers. It was last updated on 2026-03-02.

Use Cases

• Transportation planning based on road surface classification.
• Infrastructure analysis based on attributes like highway type, surface, and width.
• Climate emissions assessment based on road network composition.
• GIS applications for routing and accessibility based on urban/rural classification.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) from a hybrid deep learning approach.
• Combines multiple data sources: OSM tags, Mapillary imagery, and GHSU/AFRICAPOLIS urban layers.
• Provides multiple classification outputs, including OSM-prioritized and DL-prioritized combined surfaces.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

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.

Time Range

null

Freshness

Last updated 2026-03-02 17:16:12.940422; freshness should be verified.

Geography

New Caledonia