Puerto Rico Road Surface Classification from OSM and AI

HeiGIT (Heidelberg Institute for Geoinformation Technology) published this dataset on March 2, 2026. It provides detailed information on road surfaces in Puerto Rico, distinguishing between paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary street-level imagery.

Use Cases

• Transportation planning based on road surface type classification.
• Infrastructure analysis based on attributes like highway type, surface, and width.
• Climate emissions assessment based on road surface and urban/rural classification.
• GIS applications for humanitarian access and logistics based on road network data.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) from a hybrid deep learning approach.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, and global urban settlement layers.
• Provides multiple classification schemes (OSM-priority and DL-priority) for comparison.

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 (e.g., total road length) are listed as 0.0 million km, indicating placeholder values.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

Collection Method

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

Time Range

Mean timestamp of predictions (DL_mean_timestamp) is included, but specific coverage period is not stated.

Freshness

Last updated 2026-03-02 17:15:08.144254; freshness should be verified.

Geography

Puerto Rico.