North Macedonia Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides AI-derived classifications of paved and unpaved roads in North Macedonia, based on OpenStreetMap data and a hybrid deep learning model. It covers approximately 34,400 km of mapped roads, with 16.8% classified as paved and 12.8% as unpaved. The dataset was last updated in March 2026 and is augmented with Mapillary imagery and urban classification layers.

Use Cases

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

Strengths

• Includes AI-derived predictions for road surface type (paved/unpaved) where OpenStreetMap data is missing.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides specific statistics: 34,400 km of roads mapped, with 16.8% paved and 12.8% unpaved.

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.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

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

Collection Method

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

Time Range

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

Freshness

Last updated 2026-03-02 17:08:49.508256; freshness should be verified.

Geography

North Macedonia.