Road Surface Classification for Niue with AI-Derived Paved and Unpaved Estimates

Approximately 0.4 km of roads are mapped in OpenStreetMap for Niue. The dataset, created by HeiGIT and last updated in March 2026, provides AI-derived surface classifications (paved or unpaved) for roads, combining OSM data with deep learning predictions from Mapillary imagery and urban layers.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission factors.
• GIS applications integrating urban classification attributes like rural/urban designation.

Strengths

• Provides AI-derived surface estimates for 0.4 km of roads, addressing a 51.2643% data gap in OSM.
• Includes multiple classification outputs (OSM-derived, DL-prioritized) and urban/rural context from GHSU and AFRICAPOLIS layers.
• Available in geospatial formats (GEOJSON, GEOPACKAGE) suitable for GIS workflows.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect geographic bias inherent to the OpenStreetMap source for Niue.

Provenance

Source

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

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in the referenced arXiv papers.

Time Range

Mean timestamp of predictions used is provided in the 'DL_mean_timestamp' attribute.

Freshness

Last updated 2026-03 02 17:05:05.208952; freshness should be verified.

Geography

Niue