Jordan Road Surface Classification from OpenStreetMap and Deep Learning

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Jordan. It covers approximately 81,200 km of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The data is intended for transportation planning, infrastructure analysis, and climate emissions assessment.

Use Cases

• Transportation planning based on paved vs. 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

• Includes AI-derived surface predictions for approximately 81,200 km of roads.
• Combines OpenStreetMap data with deep learning predictions from Mapillary imagery.
• Provides multiple classification fields (pred_class, osm_surface_class, combined classifications).

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

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

The dataset uses a 'DL_mean_timestamp' field, but a specific temporal coverage range is not stated.

Freshness

Last updated 2026-03-02 17:27:58.616747; freshness should be verified.

Geography

Jordan