Romanian Road Surface Classifications from OpenStreetMap and AI Predictions

HeiGIT's dataset provides detailed road surface classifications for Romania, derived from OpenStreetMap data and augmented with deep learning predictions from Mapillary imagery. It covers approximately 0.4242 million kilometers of roads, with AI estimates classifying 26.2097% as paved and 24.1636% as unpaved. The dataset 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 smoothness.
• Climate emissions assessment informed by road surface material data.
• GIS applications leveraging geospatial road network data with urban/rural classification.

Strengths

• Covers approximately 0.4242 million kilometers of roads in Romania.
• Provides AI-derived surface estimates for paved (0.1112 million km) and unpaved (0.1025 million km) roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban classification layers.

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

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:24:39.627059; freshness should be verified.

Geography

Romania