A Global Dataset of Unified Road Centrelines for 2073 Coastal Urban Areas Using Voronoi Tessellation

Over 40% of the world's population lives within 100 km of the coast, creating an urgent need for accurate spatial data to support climate adaptation and urban planning. However, existing road-network datasets such as Overture adopt a traffic-oriented representation in which multi-lane roads are stored as multiple parallel segments. This introduces a critical problem for spatial analysis: a four-lane road represented as four separate lines is counted four times in space-syntax and centrality calculations, distorting measurements of urban spatial structure. Traditional solutions rely on manual axial-map creation by trained experts, limiting analysis to individual case studies. Here we present a unified road-centreline dataset for 2073 coastal urban areas across 110 countries, derived using a Voronoi-based extraction method with adaptive spatial partitioning that makes medial-axis centreline extraction feasible at global scale. The extraction scales approximately linearly with network size (power-law exponent 0.91), so that a typical urban area is processed in about a minute. Comprehensive quality validation confirms that all processed urban areas achieve excellent geometric-quality grades, with an average length-preservation rate of 85.7% and full coordinate-reference-system consistency; the extracted centrelines also agree with an independent authoritative reference to within a median of 2 m. The dataset transforms 67.6 million raw Overture road segments (13.19 million km) into a unified centreline network of 63.5 million segments totalling 11.04 million km, removing the duplicated length of parallel and multi-lane carriageways while preserving connectivity, and is provided in GeoParquet and GeoPackage format as a topologically consistent foundation for large-scale comparative studies in urban morphology and climate-resilience planning.