Switzerland Road Surface Classification from OSM and Deep Learning

Road surface data for Switzerland distinguishes paved and unpaved roads using a hybrid deep learning approach on OpenStreetMap (OSM) and Mapillary imagery. The dataset covers approximately 0.2896 million kilometers of roads, with AI-derived estimates classifying 28.4811% as paved and 15.0571% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length estimates.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the urban/rural classification and road material data.
• GIS applications for routing and logistics based on the geospatial road network.

Strengths

• Provides AI-derived surface classification for approximately 0.2896 million kilometers of roads.
• Augments OSM data with deep learning predictions to address a 56.4618% missing information gap.
• Includes detailed OSM and urban classification attributes such as highway type, surface, and urban/rural flag.

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 urban layers from GHSU and AFRICAPOLIS.

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in the linked arXiv papers.

Time Range

Mean timestamp of predictions is provided in the DL_mean_timestamp field.

Freshness

Last updated 2026-03 02 17:18:09.999452; freshness should be verified.

Geography

Switzerland