Spain Road Surface Classification from OpenStreetMap and AI Predictions

Approximately 1.7949 million kilometers of roads in Spain are mapped, with AI-derived estimates classifying 0.3143 million km as paved and 0.1956 million km as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery to fill gaps in surface information.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment informed by road surface material data.
• GIS applications leveraging geospatial road network data with urban/rural classification.

Strengths

• Covers approximately 1.7949 million kilometers of roads in Spain.
• Provides AI-derived surface classifications for roads where OSM data is missing.
• Includes multiple classification schemes (OSM-priority and DL-priority) for comparison.

Limitations

• Approximately 1.285 million km (71.5906%) of road surface information is missing in the base OSM data.
• The AI-augmented Mapillary-derived data adds only 0.0112 million km (0.8711%) to the missing surface information.
• Column-level documentation and sample data are unavailable, requiring inference after download.

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 papers.

Time Range

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Freshness

Last updated 2026-03-02 17:09:42.071508.

Geography

Spain