Hungarian Road Surface Classification from OpenStreetMap and AI Predictions

Approximately 0.3295 million kilometers of roads in Hungary are classified as paved or unpaved using a hybrid deep learning approach. The dataset, created by HeiGIT, combines OpenStreetMap data with predictions from Mapillary imagery and urban layers to address a 65.2% surface information gap in OSM. It was last updated on March 2, 2026.

Use Cases

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

Strengths

• Covers approximately 0.3295 million kilometers of roads in Hungary.
• Provides AI-derived estimates for 22.2% paved and 12.6% unpaved roads, addressing OSM data gaps.
• Includes multiple classification fields (OSM, AI-prioritized, combined) for flexibility in analysis.

Limitations

• Approximately 0.2147 million kilometers (65.2%) of road surface information is missing from the underlying OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and GHSU/AFRICAPOLIS urban layers.

Collection Method

Surface classification derived using a hybrid deep learning approach; methodology detailed in linked arXiv papers.

Time Range

Mean prediction timestamp is included (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:16:21.705602; freshness should be verified.

Geography

Hungary.