Grenada Road Surface Classification from OpenStreetMap and AI

Approximately 1.2 km of roads are mapped in OpenStreetMap for Grenada, with AI-derived estimates classifying 22.438% as paved and 8.0695% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OSM data with deep learning predictions from Mapillary imagery and urban layers. It provides attributes for surface type, road characteristics, and urban classification.

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 utilizing geospatial road network data with urban/rural tags.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban settlement layers.
• Provides AI-derived surface classifications (paved/unpaved) for roads where OSM data is missing.
• Includes detailed OSM and urban classification attributes for each road segment.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and file size are unknown, which may limit suitability assessment.
• 69.4925% 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 GHSU/AFRICAPOLIS urban layers.

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in a linked arXiv paper.

Time Range

The mean timestamp of predictions used is recorded in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:27:43.599325; freshness should be verified.

Geography

Grenada.