Guadeloupe Road Surface Classification from OpenStreetMap and AI

Guadeloupe road network data provides AI-derived surface classifications for paved and unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban layers. 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 type and urban/rural classification.
• Infrastructure analysis using predicted road surface classes and OSM attributes like highway type and width.
• Climate emissions assessment leveraging the distinction between paved and unpaved road lengths.
• GIS applications utilizing the geospatial road network data with combined OSM and deep learning classifications.

Strengths

• Includes AI-derived surface predictions (paved/unpaved) using a hybrid deep learning approach detailed in a referenced paper.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS urban layers.
• Provides multiple classification schemes, including OSM-priority and DL-priority combined surfaces.

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.
• Key statistics like total road length and percentages are listed as 0.0 million km and nan%, indicating missing values in the summary.

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 included (DL_mean_timestamp), but specific coverage period is not stated.

Freshness

Last updated 2026-03-02 17:27:23.656698; freshness should be verified.

Geography

Guadeloupe