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Climate change is increasing the intensity and frequency of many extremeweather events, including heatwaves, which results in increased thermaldiscomfort and mortality rates. While global mitigation action is undoubtedlynecessary, so is climate adaptation, e.g., through climate-sensitive urbanplanning. Among the most promising strategies is harnessing the benefits ofurban trees in shading and cooling pedestrian-level environments. Our workinvestigates the challenge of optimal placement of such trees. Physicalsimulations can estimate the radiative and thermal impact of trees on humanthermal comfort but induce high computational costs. This rules outoptimization of tree placements over large areas and considering effects overlonger time scales. Hence, we employ neural networks to simulate the point-wisemean radiant temperatures--a driving factor of outdoor human thermalcomfort--across various time scales, spanning from daily variations to extendedtime scales of heatwave events and even decades. To optimize tree placements,we harness the innate local effect of trees within the iterated local searchframework with tailored adaptations. We show the efficacy of our approachacross a wide spectrum of study areas and time scales. We believe that ourapproach is a step towards empowering decision-makers, urban designers andplanners to proactively and effectively assess the potential of urban trees tomitigate heat stress.

Climate-sensitive Urban Planning through Optimization of Tree Placements