Cayman Islands Road Surface Classification from OSM and Deep Learning

Cayman Islands road network data distinguishes paved and unpaved surfaces using a hybrid deep learning approach. The dataset, created by HeiGIT, covers approximately 1.3 km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 44.5% as paved and 11.2% as unpaved. It was last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and urban classification attributes.
• Infrastructure analysis based on predicted surface class and OSM-derived attributes like highway type and width.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications for routing and logistics based on geospatial road network data.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary imagery, and urban layers from GHSU and AFRICAPOLIS.
• Provides AI-derived surface estimates for approximately 1.3 km of roads, with percentages for paved (44.5%) and unpaved (11.2%) categories.
• Includes multiple classification schemes (OSM-priority and DL-priority) for surface type.

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 44.3% of road surface information is reported as missing in the original OSM data.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology), using OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS data.

Collection Method

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

Time Range

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

Freshness

Last updated 2026-03-02 17:30:13.878115; freshness should be verified.

Geography

Cayman Islands.