French Guiana Road Surface Data with AI-Derived Paved and Unpaved Classifications

Road surface data for French Guiana derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset includes AI-derived classifications for paved and unpaved roads, along with standard OSM attributes and urban classification layers. It was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and highway classification.
• Infrastructure analysis based on attributes like surface, smoothness, width, and bridge status.
• Climate emissions assessment based on the differentiation between paved and unpaved road networks.
• GIS applications for urban/rural classification using the integrated urban layer attributes.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) from a hybrid deep learning model.
• Combines data from multiple sources: OpenStreetMap, Mapillary imagery, and global settlement layers.
• Available in common geospatial formats (GeoJSON and GeoPackage).
• Published with associated methodological papers detailing the AI approach.

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 in the description show placeholder values (0.0 million km, nan%), indicating the summary may not reflect actual data scale.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

Data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU and AFRICAPOLIS urban layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:24:48.600165; freshness should be verified.

Geography

French Guiana