Road Surface Classification for Trinidad and Tobago from OpenStreetMap and AI

Approximately 16,500 kilometers of roads are mapped in OpenStreetMap for Trinidad and Tobago. The dataset classifies road surfaces as paved or unpaved using a hybrid deep learning approach, augmented with urban classification layers. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis based on road surface type and associated OSM attributes like highway type and width.
• Climate emissions assessment based on road surface data linked to vehicle fuel efficiency.
• GIS applications for routing and network analysis based on geospatial road data.

Strengths

• Provides AI-derived surface classifications for approximately 16,500 km of roads.
• Includes multiple classification schemes (OSM-priority and DL-priority) for comparison.
• Augments OSM data with urban classification from GHSU and AFRICAPOLIS layers.

Limitations

• Approximately 10,700 km (65%) of road surface information is missing in the underlying 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.

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in a referenced arXiv paper.

Time Range

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

Freshness

Last updated 2026-03-02 17:11:58.516296; freshness should be verified.

Geography

Trinidad and Tobago.