Iraq Road Surface Classification from OpenStreetMap and AI

Iraq road surface data distinguishes paved and unpaved roads using a hybrid deep learning approach on OpenStreetMap (OSM) and Mapillary imagery. Approximately 0.2839 million kilometers of roads are mapped, with AI-derived estimates for 9.1664% paved and 3.1261% unpaved. The dataset was produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

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

Strengths

• Includes AI-derived surface predictions for approximately 0.2839 million kilometers of roads.
• Augments OSM data with deep learning predictions from Mapillary imagery to address missing surface information.
• Provides combined classifications prioritizing either OSM tags or deep learning predictions for flexibility.

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.
• Approximately 87.7075% of road surface information is reported as missing in the original OSM data.

Provenance

Source

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

Collection Method

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

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:19:00.669735; freshness should be verified.

Geography

Iraq.