  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.
