Road Surface Classification for Saudi Arabia from OpenStreetMap and AI Predictions

A geospatial dataset providing detailed information on road surfaces across Saudi Arabia, distinguishing between paved and unpaved roads. The data originates from OpenStreetMap and is augmented with AI-derived surface classifications from Mapillary imagery, covering approximately 0.6464 million kilometers of mapped roads. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications leveraging urban classification attributes like rural/urban designation.

Strengths

• Provides AI-derived estimates for 0.1355 million km of paved and 0.015 million km of unpaved roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and global settlement layers.
• Includes detailed attributes for road classification, urban context, and model predictions.

Limitations

• Approximately 0.4959 million km (76.7212%) of road surface information is missing in the base OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file 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, with methodology detailed in linked research papers.

Time Range

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

Freshness

Last updated 2026-03-02 17:01:15.029626; freshness should be verified.

Geography

Saudi Arabia.