Liechtenstein Road Surface Classification from OpenStreetMap and AI

A geospatial dataset detailing road surfaces in Liechtenstein, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with AI-derived classifications using a hybrid deep learning approach based on Mapillary imagery. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis based on road attributes like surface type, width, and lanes.
• Climate emissions assessment based on road surface data and urban/rural classification.
• GIS applications for routing and network analysis based on the geospatial road data.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) for approximately 0.0014 million km of roads.
• Provides multiple classification schemes, including OSM-derived, AI-prioritized, and combined versions.
• Enriched with urban/rural classification from the GHSL and Africapolis layers.

Limitations

• Approximately 0.0004 million km (29.1099%) of road surface information is missing in the base OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers (GHSL, Africapolis).

Collection Method

Hybrid deep learning approach applied to street-level imagery; methodology detailed in linked arXiv papers.

Time Range

Mean timestamp of predictions is provided (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:19:50.762441; freshness should be verified.

Geography

Liechtenstein