Urban areas are not only one of the biggest contributors to climate change, but also they are one of the most vulnerable areas with high populations who would together experience the negative impacts. In this paper, I address some of the opportunities brought by satellite remote sensing imaging and artificial intelligence (AI) in order to measure climate adaptation of cities automatically. I propose an AI-based framework which might be useful for extracting indicators from remote sensing images and might help with predictive estimation of future states of these climate adaptation related indicators. When such models become more robust and used in real-life applications, they might help decision makers and early responders to choose the best actions to sustain the wellbeing of society, natural resources and biodiversity. I underline that this is an open field and an ongoing research for many scientists, therefore I offer an in depth discussion on the challenges and limitations of AI-based methods and the predictive estimation models in general.
翻译:城市地区不仅是造成气候变化的最大因素之一,也是人口众多的最脆弱地区之一,将共同经历这些不利影响。在本文件中,我谈到卫星遥感成像和人工智能带来的一些机会,以便自动测量城市的气候适应情况。我提议一个基于AI的框架,它可能有益于从遥感图像中提取指标,并有助于预测对这些与气候适应有关的指标的未来状况进行估计。当这些模型变得更稳健并用于实际应用时,它们可能帮助决策者和早期反应者选择最佳行动,以维持社会、自然资源和生物多样性的福祉。我强调这是一个开放的领域,是许多科学家不断研究的一个领域,因此我对基于AI的方法和一般预测估计模型的挑战和局限性进行了深入讨论。