Panama Road Surface Classification from OpenStreetMap and AI

Approximately 49,700 kilometers of roads are mapped in OpenStreetMap for Panama. The dataset classifies road surfaces as paved or unpaved using a hybrid deep learning approach based on Mapillary imagery, augmented with urban classification layers. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using road attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission modeling.
• GIS applications utilizing the geospatial road network and urban/rural classifications.

Strengths

• Provides AI-derived estimates for 49,700 km of roads, with 23.5% classified as paved and 22.3% as unpaved.
• Augments OpenStreetMap data with deep learning predictions and urban layers from GHSU and AFRICAPOLIS.
• Includes multiple classification fields (e.g., pred_class, osm_surface_class) and OSM attributes for detailed analysis.

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 54.2% of road surface information is missing in the underlying OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with Mapillary imagery and predictions from HeiGIT.

Collection Method

Surface classification derived using a hybrid deep learning approach, combined with urban layers.

Time Range

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

Freshness

Last updated 2026-03-02 17:29:53.399200; freshness should be verified.

Geography

Panama.