Road Surface Classification for Guam from OSM and Satellite Imagery

Guam road surface data derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary imagery. The dataset, created by HeiGIT, classifies roads as paved or unpaved and includes urban classification attributes. It was last updated on 2026-03 02.

Use Cases

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

• Includes AI-derived surface classifications (paved/unpaved) from a hybrid deep learning approach.
• Combines data from multiple sources: OSM, Mapillary imagery, GHSU, and AFRICAPOLIS urban layers.
• Provides multiple classification outputs (OSM-prioritized, DL-prioritized) for flexibility.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:06:24.792957; freshness should be verified.

Geography

Guam