Sweden Road Surface Classification from OpenStreetMap and AI Predictions

Sweden's road network, covering approximately 0.7549 million kilometers, is classified into paved and unpaved surfaces. The dataset, created by HeiGIT, combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging road surface data for vehicle emission models.
• GIS applications utilizing geospatial road data with urban/rural classification.

Strengths

• Covers approximately 0.7549 million kilometers of roads in Sweden.
• Provides AI-derived estimates for 0.1915 million km of paved and 0.2495 million km of unpaved roads.
• Augments OSM data with an additional 0.0037 million km of surface information from Mapillary.

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, Mapillary imagery, GHSU Global Human Settlement Urban Layer 2019, AFRICAPOLIS 2020.

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

null

Freshness

Last updated 2026-03-02 17:05:09.712398; freshness should be verified.

Geography

Sweden