Cyprus Road Surface Classification from OpenStreetMap and AI

Approximately 0.0484 million kilometers of roads are mapped in OpenStreetMap for Cyprus. The dataset, created by HeiGIT, uses a hybrid deep learning approach to classify roads as paved or unpaved, augmenting OSM data with predictions from Mapillary imagery and urban layers. It was last updated on March 2, 2026.

Use Cases

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

Strengths

• Provides AI-derived estimates for 0.0147 million km of paved and 0.0092 million km of unpaved roads.
• Augments OpenStreetMap data with deep learning predictions to address missing surface information.
• Includes multiple classification outputs (OSM, DL, combined) and urban/rural attributes for analysis.

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

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, with methodology detailed in linked research papers.

Time Range

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

Freshness

Last updated 2026-03-02 17:16:03.411191; freshness should be verified.

Geography

Cyprus.