New Zealand Road Surface Classification from OpenStreetMap and AI

New Zealand road network data provides AI-derived surface classifications for approximately 232,500 kilometers of roads. The dataset, created by HeiGIT, combines OpenStreetMap (OSM) attributes with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis based on road attributes like surface, smoothness, and width.
• Climate emissions assessment based on road surface type and urban/rural classification.
• GIS applications for routing and network analysis based on geospatial road data.

Strengths

• Covers approximately 232,500 kilometers of roads in New Zealand.
• Provides AI-derived estimates for paved (99,300 km) and unpaved (61,200 km) surfaces.
• Augments OSM data with deep learning predictions from Mapillary imagery.

Limitations

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

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers (GHSU, AFRICAPOLIS).

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

Mean timestamp of predictions is provided (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:29:58.898840; freshness should be verified.

Geography

New Zealand.