El Salvador Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides detailed road surface information for El Salvador, distinguishing paved and unpaved roads. It combines OpenStreetMap data with AI-derived surface classifications from Mapillary imagery and urban layers. The dataset was last updated on March 2, 2026.

Use Cases

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

Strengths

• Covers approximately 45,800 km of roads mapped in OpenStreetMap.
• Provides AI-derived surface estimates for 20.9447% paved and 18.978% unpaved roads.
• Augments OSM data with deep learning predictions from Mapillary street-level imagery.

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 GHSU/AFRICAPOLIS urban layers.

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

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

Freshness

Last updated 2026-03-02 17:22:28.145125; freshness should be verified.

Geography

El Salvador.