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        <title>FOSS4GE 2025 | An Open-Source Deep Learning Framework for Scalable Urban Heat Island Detection ...</title>
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        <description>Increased urbanization rates have had a significant effect on changing land surface characteristics, leading to the rise of Urban Heat Islands (UHIs), localized regions where temperatures are considerably higher than in surrounding rural areas. This phenomenon is primarily driven by dense urban structures, reduced vegetation cover, and anthropogenic heat discharge, which collectively contribute to enhancing the absorption and retention of heat in urban areas (Anjos et al., 2025; Qin &amp; Jiang, 2024). As climate change intensifies, UHIs worsen environmental problems, including increased energy consumption, lower air quality, and severe public health concerns like heat stress and cardiovascular disease (Chanpichaigosol &amp; Chaichana, 2025). The rapid expansion of urban areas has elevated UHI mitigation to one of the highest priorities. Yet, existing detection and analysis methods often lack scalability, automation, limiting their ability to produce high-resolution, globally consistent assessments (Fu et al., 2024). Mercy Ọ̀nàọpẹ́mipọ̀ Akintola Gresa Neziri https://talks.osgeo.org/foss4g-europe-2025/talk/38KDUJ/ Room: PA01 (Quarticle) @ 17.07.2025 15:05:00 #foss4ge2025 #AcademicTrack</description>
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