Armenia Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides AI-derived road surface classifications for Armenia, distinguishing paved and unpaved roads. It covers approximately 74,900 km of roads from OpenStreetMap, with 13.0% classified as paved and 11.6% as unpaved. The data was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis based on attributes like highway type, surface, and smoothness.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications for urban/rural classification based on the urban attribute.

Strengths

• Provides AI-derived surface classifications for approximately 74,900 km of roads.
• Includes detailed OSM attributes such as name, highway type, surface, and smoothness.
• Augments OSM data with deep learning predictions from Mapillary imagery.

Limitations

• Approximately 75.4% of road surface information is missing in the underlying OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

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

Time Range

null

Freshness

Last updated 2026-03-02 17:23:47.671913; freshness should be verified.

Geography

Armenia