Fiji Road Surface Classification from OpenStreetMap and AI Predictions

Approximately 18,300 kilometers of roads are mapped in OpenStreetMap for Fiji. The dataset classifies road surfaces as paved or unpaved using a hybrid deep learning approach based on Mapillary imagery, augmented with urban classification layers. It was created by HeiGIT (Heidelberg Institute for Geoinformation Technology) and last updated in March 2026.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis based on road attributes like surface type, width, and lanes.
• 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

• Provides AI-derived surface classification for approximately 18,300 km of roads.
• Includes detailed OSM attributes such as name, highway type, surface, and width.
• Augments OSM data with deep learning predictions to address missing surface information.

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 from GHSU and AFRICAPOLIS.

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

null

Freshness

Last updated 2026-03-02 17:16:50.618435; freshness should be verified.

Geography

Fiji