Road Surface Classification for Saint Barthélemy from OSM and Satellite Imagery

OpenStreetMap road data for Saint Barthélemy is augmented with deep learning predictions from Mapillary imagery to classify surfaces as paved or unpaved. The dataset, created by HeiGIT, includes AI-derived features like predicted class and length, and integrates urban classification layers. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length estimates.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the distinction between paved and unpaved roads.
• GIS applications utilizing the geospatial format and urban/rural classification.

Strengths

• Includes AI-derived features such as 'pred_class' and 'predicted_length' from a hybrid deep learning approach.
• Combines data from multiple sources: OpenStreetMap, Mapillary imagery, and global human settlement layers.
• Available in geospatial formats (GEOJSON, GEOPACKAGE) suitable for GIS workflows.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• The description states 0.0 million km of roads are mapped, suggesting the core OSM data may be limited for this region.

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

Mean timestamp of predictions is recorded in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:00:49.490615; freshness should be verified.

Geography

Saint Barthélemy