Kuwait Road Surface Classification from OpenStreetMap and AI

Kuwait's road network, covering approximately 38,300 kilometers, is classified by surface type using a hybrid deep learning approach. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), provides AI-derived estimates for paved and unpaved roads, augmented with Mapillary imagery and urban layers. It was last updated in March 2026.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface and urban/rural classification data.
• GIS applications for mapping and spatial analysis of Kuwait's road network.

Strengths

• Provides AI-derived surface classifications for approximately 38,300 km of roads.
• Includes detailed OSM attributes such as name, highway type, surface, and lanes.
• Augments OSM data with deep learning predictions from Mapillary imagery to reduce information gaps.
• Integrates urban classification from GHSU and AFRICAPOLIS layers.

Limitations

• Approximately 46.3% (17,700 km) of road surface information is missing in the original OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.

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; methodology detailed in referenced arXiv papers.

Time Range

null

Freshness

Last updated 2026-03-02 17:03:34.822788; freshness should be verified.

Geography

Kuwait