Honduras Road Surface Classification from OpenStreetMap and AI Predictions

94,200 kilometers of Honduran roads are mapped in OpenStreetMap, with AI-derived surface classifications for 44.8% of them. The Heidelberg Institute for Geoinformation Technology (HeiGIT) created this dataset by augmenting OSM data with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated in March 2026.

Use Cases

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

Strengths

• Provides AI-derived surface classification for approximately 44,817.6 km of roads where OSM data was missing.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and global urban settlement layers.
• Includes detailed OSM and urban classification attributes for each road segment.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• 55.2% (approximately 52,000 km) of road surface information is still missing from the original OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

Collection Method

Surface classification derived using a hybrid deep learning approach, with methodology detailed in linked arXiv papers.

Time Range

Mean timestamp of predictions (DL_mean_timestamp) is provided per feature.

Freshness

Last updated 2026-03-02 17:27:37.254630; freshness should be verified.

Geography

Honduras.