Venezuela Road Surface Classification from OpenStreetMap and AI

Road surface data for Venezuela (Bolivarian Republic of) distinguishes paved and unpaved roads. The dataset contains approximately 0.2997 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 15.0369% as paved and 22.9079% as unpaved. It was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length estimates.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment based on paved versus unpaved road networks.
• GIS applications leveraging geospatial road data with urban/rural classification.

Strengths

• Provides AI-derived surface classification for approximately 0.2997 million kilometers of roads.
• Includes detailed OSM attributes such as name, highway type, surface, and width.
• Augments OSM data with deep learning predictions from Mapillary imagery.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• About 62.0553% of road surface information is reported as 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 paper.

Time Range

Mean timestamp of predictions is provided via the DL_mean_timestamp attribute.

Freshness

Last updated 2026-03-02 17:27:56.971229; freshness should be verified.

Geography

Venezuela (Bolivarian Republic of).