Antigua and Barbuda Road Surface Classification from OSM and Satellite Imagery

HeiGIT's dataset provides detailed road surface classifications for Antigua and Barbuda, distinguishing paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and PlanetScope satellite data. Approximately 0.0023 million kilometers of roads are mapped, with AI-derived estimates for paved and unpaved lengths provided.

Use Cases

• Transportation planning based on road surface type and predicted length.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the urban/rural classification and road material data.
• GIS applications for routing and logistics based on the geospatial road network.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) for approximately 0.0023 million km of roads.
• Augments OSM data with deep learning predictions from Mapillary and PlanetScope satellite imagery.
• Provides multiple classification fields (OSM-priority, DL-priority) and urban/rural labels.

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.

Provenance

Source

OpenStreetMap (OSM), Mapillary imagery, PlanetScope satellite imagery, GHSU and AFRICAPOLIS urban layers.

Collection Method

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

Time Range

Mean timestamp of predictions (DL_mean_timestamp) is included, but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:28:08.242164; freshness should be verified.

Geography

Antigua and Barbuda.