Uruguay Road Surface Classification from OpenStreetMap and AI

Approximately 112,200 km of roads are mapped for Uruguay, with AI-derived estimates classifying 20,800 km as paved and 31,700 km as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It is intended to support transportation planning, infrastructure analysis, and climate emissions assessment.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis based on road surface type and associated OSM attributes like highway type and width.
• Climate emissions assessment based on road surface data linked to vehicle efficiency models.
• GIS applications for routing and network analysis based on geospatial road data and urban/rural classification.

Strengths

• Includes AI-derived surface classifications for approximately 112,200 km of roads.
• Combines multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban settlement layers.
• Provides specific statistics: 18.5% paved, 28.3% unpaved, and identifies a 53.2% gap in OSM surface data.

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 and file size are 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, with methodology detailed in linked research papers.

Time Range

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

Freshness

Last updated 2026-03-02 17:24:53.801318; freshness should be verified.

Geography

Uruguay.