Bolivia Road Surface Classification from OpenStreetMap and AI

Road surface data for Bolivia distinguishing paved and unpaved roads, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset covers approximately 463,800 kilometers of roads, with AI-derived estimates classifying 6.4% as paved and 70.6% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length data.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment based on road network composition and urban/rural classification.
• GIS applications for routing and logistics using the geospatial road network.

Strengths

• Covers approximately 463,800 kilometers of roads in Bolivia.
• Provides AI-derived surface classifications for paved and unpaved roads, addressing a 22.9% gap in OSM surface information.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural attributes for contextual 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.

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 a referenced arXiv paper.

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:06:09.041865; freshness should be verified.

Geography

Bolivia (Plurinational State of).