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Global building datasets offer unprecedented coverage but may introduce biases by including different types of built-up structures, potentially leading to an overestimation of exposure and a misallocation of critical resources. This study aims to map the WUI and assess wildfire exposure in mainland Portugal by comparing the efficacy of different building datasets: the official residential database (BGE21), Microsoft Building Footprints (MSB24), and the World Settlement Footprint (WSF19). We employed a point-based mapping methodology (100-m radius, &gt; 6.17 buildings\/km\n                    <jats:sup>2<\/jats:sup>\n                    ) to classify WUI into\n                    <jats:italic>Intermix<\/jats:italic>\n                    and\n                    <jats:italic>Interface<\/jats:italic>\n                    zones. Our analysis revealed that the choice of dataset drastically alters WUI estimates. MSB24, which includes all structure types, identified 67% more buildings than the residential-focused BGE21, resulting in a 73% larger WUI area. Spatial agreement was low, with only 46% of the total WUI area being identified by all three datasets, falling to just 29% for the more vulnerable\n                    <jats:italic>Intermix<\/jats:italic>\n                    zones. While MSB24 showed high recall (0.97\n                    <jats:italic>Intermix<\/jats:italic>\n                    , 0.99\n                    <jats:italic>Interface<\/jats:italic>\n                    ), its precision was low (0.43\n                    <jats:italic>Intermix<\/jats:italic>\n                    , 0.67\n                    <jats:italic>Interface<\/jats:italic>\n                    ), confirming a significant overestimation of critical zones. Analysis of wildfire perimeters (2000\u20132023) showed that burned area within the WUI was disproportionately higher in years with smaller total fire extent (e.g., 2006, 2018) rather than in megafire years (e.g., 2003, 2017). We conclude that while global datasets like MSB24 are valuable for emergency response due to their high coverage, their use for preventive planning and resource allocation may cause inefficiencies. We recommend that local authorities prioritize validated residential data, like BGE21, for strategic wildfire prevention and mitigation planning in Portugal to ensure resources are targeted efficiently.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-53590-5","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:25:04Z","timestamp":1779366304000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing the influence of building data choice on wildland\u2013urban interface delineation in mainland Portugal"],"prefix":"10.1038","volume":"16","author":[{"given":"Bruno","family":"Barbosa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caetano","family":"M\u00e1rio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Rocha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"53590_CR1","doi-asserted-by":"publisher","unstructured":"San-Miguel-Ayanz, J. et al. 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