Guatemala Road Surface Classification from OSM and AI

Approximately 119,800 kilometers of roads are mapped in OpenStreetMap for Guatemala. The Heidelberg Institute for Geoinformation Technology (HeiGIT) augmented this data with AI-derived surface classifications, estimating that 13.3% are paved and 23.3% are unpaved, while 63.4% of road surface information was originally missing from OSM.

Use Cases

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

Strengths

• Includes AI-derived predictions for road surface (paved/unpaved) to fill data gaps in OSM.
• Combines multiple data sources: OSM, Mapillary imagery, and urban settlement layers.
• Provides specific statistics on road length and classification coverage (e.g., 0.0159 million km paved).

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

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented using a hybrid deep learning approach on Mapillary imagery, combined with urban settlement layers.

Time Range

null

Freshness

Last updated 2026-03 02 17:02:17.285087; freshness should be verified.

Geography

Guatemala