Remote sensing and AI for building climate adaptation applications

TL;DR

This paper explores how satellite remote sensing and AI can be combined to automatically measure and predict urban climate adaptation, aiding decision-making and resilience efforts.

Contribution

It proposes a novel framework integrating AI and simulation to extract indicators from remote sensing images for climate adaptation assessment.

Findings

Framework for AI-based indicator extraction from satellite images

Discussion on challenges of data-driven climate adaptation models

Potential for predictive estimation to inform urban resilience strategies

Abstract

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.