Andorra Road Surface Classification from OpenStreetMap and Deep Learning

Approximately 1.9 km of roads are mapped in OpenStreetMap for Andorra, with AI-derived estimates classifying 19.33% as paved and 11.14% as unpaved. This dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OSM data with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated in March 2026.

Use Cases

• Transportation planning based on road surface type (paved/unpaved) and highway classification.
• Infrastructure analysis based on attributes like surface, smoothness, width, and bridge status.
• Climate emissions assessment based on road network composition and urban/rural classification.
• GIS applications for routing and logistics based on geospatial road network data.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban classification layers (GHSU, AFRICAPOLIS).
• Provides AI-derived surface estimates for roads where OSM data is missing, covering a predicted 0.09% of the missing information gap.
• Includes detailed OSM attributes (e.g., highway, surface, smoothness) and urban classification (urban/rural).

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• A significant portion (69.53%) of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT).

Collection Method

Data originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with urban classification layers.

Time Range

The mean timestamp of predictions used is recorded (DL_mean_timestamp), but the overall temporal coverage is not explicitly stated.

Freshness

Last updated 2026-03 02 17:20:09.168756; freshness should be verified.

Geography

Andorra.