Saint Kitts and Nevis Road Surface Classification from OSM and AI

Approximately 0.0012 million km of roads are mapped in OpenStreetMap for Saint Kitts and Nevis. This dataset, created by HeiGIT and last updated in March 2026, classifies road surfaces as paved or unpaved using a hybrid deep learning approach that augments OSM data with Mapillary imagery and urban layers.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications for urban and rural classification using the urban attribute.

Strengths

• Provides AI-derived estimates for 41.6484% paved and 15.126% unpaved roads, quantifying surface coverage.
• Augments OSM data with deep learning predictions from Mapillary imagery and urban classification layers.
• Includes multiple classification outputs (OSM-priority and DL-priority) for flexibility in analysis.

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.
• Approximately 43.2257% 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, GHSU Global Human Settlement Urban Layer 2019, and AFRICAPOLIS 2020.

Collection Method

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

Time Range

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Freshness

Last updated 2026-03-02 17:28:21.789480; freshness should be verified.

Geography

Saint Kitts and Nevis