Dominica Road Surface Classification from OpenStreetMap and Satellite Imagery

Approximately 0.0017 million kilometers of roads are mapped for Dominica, with 23.2719% classified as paved and 7.7348% as unpaved. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), uses a hybrid deep learning approach on OpenStreetMap and Mapillary imagery, augmented with urban classification layers. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis using road attributes like surface, smoothness, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission modeling.
• GIS applications integrating urban classification attributes for rural/urban analysis.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) for approximately 0.0017 million km of roads.
• Combines multiple data sources: OpenStreetMap, deep learning predictions from Mapillary imagery, and urban layers.
• Provides detailed OSM and urban classification attributes such as highway type, surface, and urban/rural flag.

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.
• About 68.9933% of road surface information is reported as missing in the underlying OSM data.

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, with methodology detailed in referenced arXiv papers.

Time Range

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

Freshness

Last updated 2026-03 02 17:19:09.516091; freshness should be verified.

Geography

Dominica.