Palau Road Surface Classification from OpenStreetMap and Satellite Imagery

HeiGIT's dataset provides detailed road surface information for Palau, distinguishing paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and PlanetScope satellite data. Approximately 0.0005 million kilometers of roads are mapped, with AI-derived estimates classifying 9.2565% as paved and 22.6005% as unpaved.

Use Cases

• Transportation planning based on road surface type and classification attributes.
• 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 geospatial road data.

Strengths

• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and PlanetScope satellite data.
• Provides AI-derived surface classifications (paved/unpaved) for roads.
• Includes urban classification attributes (rural/urban) from GHSU and AFRICAPOLIS 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 68.143% of road surface information is reported as missing in the base OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with a hybrid deep learning approach using Mapillary and PlanetScope imagery.

Time Range

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

Freshness

Last updated 2026-03-02 17:09:38.932238; freshness should be verified.

Geography

Palau