Road Surface Classification for Saint Vincent and the Grenadines via AI and OSM

Saint Vincent and the Grenadines road network data distinguishes paved and unpaved surfaces using a hybrid deep learning approach on OpenStreetMap and Mapillary imagery. The dataset includes approximately 0.9 km of mapped roads, with AI-derived estimates classifying 14.7% as paved and 5.8% as unpaved. It was created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

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

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary imagery, and urban layers from GHSU and AFRICAPOLIS.
• Provides dual classification schemes prioritizing either OSM tags or deep learning predictions.
• Includes detailed OSM and urban classification attributes for contextual analysis.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• A significant portion (79.5%) of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03 02 17:16:09.500291; freshness should be verified.

Geography

Saint Vincent and the Grenadines