The CUGA Method: A Reliable Framework for Identifying Public Urban Green Spaces in Metropolitan Regions

This study addresses the challenge of reliably identifying Public Urban Green Spaces (PUGS) in metropolitan areas, a key requirement for advancing equitable access to green infrastructure and monitoring progress toward SDG 11.7 and WHO recommendations. In the absence of consistent local datasets, we propose the Candidate Urban Green Area (CUGA) method, which integrates OpenStreetMap and Copernicus Urban Atlas data through a structured, transparent workflow. The method applies spatial and functional filters to isolate green spaces that are publicly accessible, meet minimum size and usability criteria, and are embedded within the urban fabric. We validate CUGA in the Dublin Region using a stratified random sample of 1-ha cells and compare its performance against five alternative datasets. Results show that CUGA achieves the highest classification accuracy, spatial coverage, and statistical robustness across all counties, significantly outperforming administrative, crowdsourced, and satellite-derived sources. The method also delivers greater net spatial impact in terms of green area, catchment coverage, and residential land intercepted. These findings support the use of CUGA as a reliable and transferable tool for urban green space planning, policy evaluation, and sustainability reporting, particularly in data-scarce or fragmented governance contexts.