Chile Road Surface Classification from OpenStreetMap and AI Predictions

HeiGIT's dataset provides AI-derived road surface classifications for Chile, distinguishing paved from unpaved roads. It covers approximately 0.4409 million kilometers of roads from OpenStreetMap, augmented with deep learning predictions from Mapillary imagery. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the distinction between paved and unpaved road networks.
• GIS applications utilizing the geospatial road network data with urban/rural classification.

Strengths

• Includes AI-derived predictions for approximately 0.4409 million kilometers of roads.
• Provides specific statistics: 0.1117 million km paved (25.34%) and 0.2348 million km unpaved (53.26%).
• Augments OpenStreetMap data with deep learning predictions to reduce information gaps.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and file size are unknown, which may limit suitability assessment.
• Approximately 0.0943 million km (21.4%) of road surface information is missing in the underlying OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap (OSM) and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

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Freshness

Last updated 2026-03-02 17:22:29.932303; freshness should be verified.

Geography

Chile