Argentina Road Surface Classification from OpenStreetMap and AI Predictions

Approximately 1.3146 million kilometers of roads in Argentina are mapped, with AI-derived estimates classifying 15.4029% as paved and 48.8612% as unpaved. The dataset is created by HeiGIT, combining OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers, and was last updated in March 2026. It includes AI-predicted classes, OSM attributes, and urban classification features.

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 leveraging the paved/unpaved road classification.
• GIS applications utilizing the geospatial road network data and urban/rural classification.

Strengths

• Covers approximately 1.3146 million kilometers of roads in Argentina.
• Provides AI-derived surface estimates for paved (0.2025 million km) and unpaved (0.6423 million km) roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and global urban settlement layers.

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 GHSU/AFRICAPOLIS urban layers.

Collection Method

Surface classification derived using a hybrid deep learning approach, with methodology detailed in referenced papers.

Time Range

The mean timestamp of predictions used is recorded, but a specific temporal coverage range is not provided.

Freshness

Last updated 2026-03-02 17:15:15.484158; freshness should be verified.

Geography

Argentina