Remote sensing and AI for building climate adaptation applications
This work addresses climate adaptation challenges for urban populations and decision-makers, but it is incremental as it builds on existing remote sensing and AI methods without introducing a new paradigm.
The paper tackles the problem of automatically measuring climate adaptation in cities by proposing a framework that combines AI and simulation to extract indicators from remote-sensing images and predict future states, with potential applications for decision-makers and early responders.
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, we address some of the opportunities brought by satellite remote sensing imaging and artificial intelligence (AI) in order to measure climate adaptation of cities automatically. We propose a framework combining AI and simulation which may be useful for extracting indicators from remote-sensing images and may 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 may help decision makers and early responders to choose the best actions to sustain the well-being of society, natural resources and biodiversity. We underline that this is an open field and an on-going area of research for many scientists, therefore we offer an in-depth discussion on the challenges and limitations of data-driven methods and the predictive estimation models in general.