Oman Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides AI-derived road surface classifications for Oman, distinguishing paved from unpaved roads. It contains approximately 0.1164 million kilometers of roads from OpenStreetMap, augmented with deep learning predictions from Mapillary imagery. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment based on paved versus unpaved road networks.
• GIS applications leveraging geospatial road data with urban/rural classification.

Strengths

• Includes approximately 0.1164 million kilometers of mapped roads.
• Provides AI-derived estimates for paved (0.0311 million km) and unpaved (0.0205 million km) roads.
• Augments OpenStreetMap data with deep learning predictions from Mapillary imagery.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Approximately 55.68% of road surface information is reported as missing in the underlying 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 paper.

Time Range

null

Freshness

Last updated 2026-03-02 17:09:13.133845; freshness should be verified.

Geography

Oman