arXiv:2606.04658v1 Announce Type: cross Abstract: Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation. Because physics-based climate simulations are computationally expensive, planners typically evaluate fewer than ten manual designs. \gls{qd} algorithms offer a way to systematically illuminate the design space, but they require surrogate models to be practical. In this paper, we replace a slow, regulatory physics simulator with a spatial deep-learning surrogate (U-Net) inside an offline MAP-Elites loop. We systematically compare this s
The increasing computational power of AI, specifically deep learning surrogates, is enabling more complex and efficient urban planning solutions for climate adaptation, addressing an urgent global need.
This development allows for the systematic optimization of climate-adaptive urban layouts, moving beyond manual designs to significantly enhance urban resilience and resource efficiency in the face of climate change.
Urban planning can now leverage AI-powered predictive models to rapidly explore and optimize design spaces for climate resilience, rather than relying on time-consuming and limited physics-based simulations.
- · Urban planners
- · Smart city technology providers
- · AI/ML researchers and developers
- · Cities in climate-vulnerable regions
- · Legacy urban simulation software companies (if they don't adapt)
- · Traditional, slow urban planning methodologies
More resilient and efficient urban environments are designed and implemented.
Reduced infrastructure costs and improved quality of life in urban areas due to better climate adaptation.
Enhanced global climate mitigation efforts through optimized urban design potentially leading to reduced energy consumption and carbon footprint.
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