Jersey Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides detailed road surface classifications for Jersey, distinguishing paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban settlement layers. It 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 leveraging the distinction between paved and unpaved road networks.
• GIS applications utilizing the geospatial road network data with urban/rural classification.

Strengths

• Includes AI-derived surface classifications for approximately 0.0016 million km of roads.
• Provides multiple classification schemes, including OSM-derived, model-predicted, and combined versions.
• Augments OSM data with deep learning predictions to address missing surface information.

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.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap (OSM) and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

Mean timestamp of predictions is provided (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03 02 17:01:49.046427; freshness should be verified.

Geography

Jersey