Jamaica Road Surface Data with AI-Derived Paved and Unpaved Classifications

OpenStreetMap road data for Jamaica, augmented with deep learning predictions from Mapillary imagery. The dataset includes approximately 0.0295 million km of roads, with AI-derived estimates classifying 26.9897% as paved and 5.1163% as unpaved. 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 classifications (paved/unpaved).
• Infrastructure analysis using OSM attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road type and urban/rural classification.
• GIS applications utilizing geospatial road network data with predicted lengths.

Strengths

• Includes AI-derived surface classifications for paved and unpaved roads.
• Combines data from multiple sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides specific statistics on road coverage, including a 67.894% gap in OSM 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 and file size are unknown, which may limit suitability assessment.

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 likely reflects the state of OpenStreetMap and available imagery at the time of creation; specific temporal coverage is not stated.

Freshness

Last updated 2026-03-02 17:17:33.425195; freshness should be verified.

Geography

Jamaica