Paraguay Road Surface Classification from OpenStreetMap and AI

Approximately 0.2413 million kilometers of roads are mapped from OpenStreetMap for Paraguay. 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 road surface classification (paved/unpaved).
• Infrastructure analysis based on OSM attributes like highway type, surface, and width.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications for urban/rural classification using the urban attribute.
• Data augmentation for mapping projects using the combined surface classifications that prioritize OSM or deep learning predictions.

Strengths

• Includes AI-derived estimates for approximately 0.2413 million kilometers of roads.
• Provides multiple classification fields (pred_class, osm_surface_class, combined classifications) for cross-validation.
• Augments OSM data with deep learning predictions to address missing surface information for about 0.1218 million km of roads.

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.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

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

Collection Method

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

Time Range

Mean timestamp of predictions is provided in the DL_mean_timestamp field.

Freshness

Last updated 2026-03-02 17:04:52.907400; freshness should be verified.

Geography

Paraguay.