Samoa Road Surface Classification from OpenStreetMap and AI Predictions

HeiGIT's Samoa: Road Surface Data provides detailed information on road surfaces across Samoa, distinguishing between paved and unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, combined with urban classification layers. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface classification (paved/unpaved).
• Infrastructure analysis based on OSM attributes like highway type, surface, and width.
• Climate emissions assessment based on road network composition and urban/rural classification.
• GIS applications for routing and logistics based on the geospatial road network data.

Strengths

• Includes AI-derived surface classifications for approximately 0.0028 million km of roads.
• Augments OSM data with predictions from Mapillary street-level imagery, adding 0.0001 million km of surface information.
• Combines multiple data sources: OSM, deep learning predictions, GHSU, and AFRICAPOLIS urban layers.

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.
• Approximately 0.0016 million km (57.9261%) of road surface information is missing in the underlying OSM data.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

Derived from OpenStreetMap (OSM) and augmented with a hybrid deep learning approach using Mapillary imagery.

Time Range

null

Freshness

Last updated 2026-03 02 17:15:54.214359; freshness should be verified.

Geography

Samoa