Belize Road Surface Classification from OpenStreetMap and Deep Learning

Approximately 16.9 thousand kilometers of roads are mapped for Belize, with AI-derived estimates classifying 11.5681% as paved and 88.4271% as unpaved. The dataset is produced by HeiGIT (Heidelberg Institute for Geoinformation Technology) and was last updated in March 2026. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface and urban/rural classification data.
• GIS applications for routing and network analysis using the geospatial road data.

Strengths

• Provides AI-derived surface classification for approximately 16.9 thousand kilometers of roads.
• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban layers.
• Includes detailed attributes for road classification, urban context, and OSM metadata.

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

Collection Method

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

Time Range

The dataset likely reflects the timestamp of the OSM extract and the DL predictions used.

Freshness

Last updated 2026-03-02 17:09:02.584413; freshness should be verified.

Geography

Belize.