Seychelles Road Surface Classification from OpenStreetMap and AI

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for the Seychelles. It distinguishes paved and unpaved roads using a hybrid deep learning approach on OpenStreetMap data, augmented with Mapillary imagery and urban layers. The data indicates approximately 0.0014 million km of roads are mapped, with AI estimates for paved (0.0002 million km) and unpaved (0.0001 million km) segments.

Use Cases

• Transportation planning based on road surface type and length data.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging road surface classification for vehicle emission factors.
• GIS applications utilizing geospatial road network data with urban/rural classification.
• Validating and augmenting OpenStreetMap road data with AI predictions.

Strengths

• Includes AI-derived surface predictions (paved/unpaved) for approximately 0.0014 million km of roads.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, and GHSU/AFRICAPOLIS urban layers.
• Provides 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 is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap data using a hybrid deep learning approach on Mapillary imagery, combined with urban classification layers.

Time Range

The dataset's temporal coverage is not explicitly stated.

Freshness

Last updated 2026-03-02 17:30:46.369163; freshness should be verified.

Geography

Seychelles