Colombia Road Surface Classification from OpenStreetMap and AI Predictions

Approximately 511,500 kilometers of roads are mapped in OpenStreetMap for Colombia, with AI-derived estimates classifying 14.9% as paved and 27.0% as unpaved. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OSM data with deep learning predictions from Mapillary imagery and urban 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, smoothness, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission models.
• GIS applications for routing and network analysis based on geospatial road vectors.
• Urban versus rural transportation studies using the urban classification attribute.

Strengths

• Provides AI-derived surface predictions for over 511,500 km of roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and global settlement layers.
• Includes detailed OSM attributes such as highway type, surface, smoothness, and width.

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 58.1% of road surface information is missing in the underlying 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, as detailed in the referenced arXiv papers.

Time Range

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

Freshness

Last updated 2026-03-02 17:09:40.341034; freshness should be verified.

Geography

Colombia.