Solomon Islands Road Surface Classification from OSM and Deep Learning

Approximately 0.017 million km of roads are mapped in OpenStreetMap for the Solomon Islands. This dataset, created by HeiGIT and last updated in March 2026, classifies road surfaces as paved or unpaved using a hybrid deep learning approach on Mapillary imagery, augmented with urban layers. It provides AI-derived estimates for paved, unpaved, and missing surface information.

Use Cases

• Transportation planning based on road surface classification.
• Infrastructure analysis based on predicted road length and surface type.
• Climate emissions assessment based on road network composition.
• GIS applications based on geospatial road attributes and urban classification.

Strengths

• Provides AI-derived estimates for 0.0004 million km of paved and 0.0034 million km of unpaved roads.
• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban layers.
• Includes detailed attributes like highway type, surface, smoothness, and urban classification.

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.

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 prediction timestamp is included (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:16:26.104745; freshness should be verified.

Geography

Solomon Islands.