Anguilla Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides AI-derived road surface classifications for Anguilla, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, combined with 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 predictions for road surface class (paved/unpaved) with a binary label.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and global urban settlement layers.
• Provides specific road length statistics, including 0.0001 million km of paved roads.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and file size are unknown, which may limit suitability assessment.
• Approximately 0.0002 million km (36.347%) of road surface information is reported as missing in the base OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap, augmented with a hybrid deep learning approach using Mapillary imagery and 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 17:01:25.002816; freshness should be verified.

Geography

Anguilla