Fusion sensing for urban resilience

Urban resilience is crucial for achieving a city's sustainable development goals in the context of global change. However, a lack of sensing and intelligent analytical frameworks that can monitor the fine-grained spatiotemporal dynamics of the stressors and a city’s reactions to these stressors is largely prohibiting the smart decision support for urban resilience studies. Here, we propose the integration of multi-source sensing technologies, including remote sensing, social sensing, and IoT-based sensing, collectively referred to as “fusion sensing,” to enhance our monitoring capability of urban systems. These technologies enable real-time data collection and analysis, facilitating adaptive responses to disturbances. Additionally, the artificial intelligence methods play important roles in both short-term emergency management and long-term planning, optimizing resource allocation and resilience. Hence, we call for collaboration among governments, industry, academia, and individuals to leverage artificial intelligence and various sensing technologies, thereby enhancing urban resilience through proactive and informed decision-making.

Introduction

Global changes have exposed cities to a more complex and volatile external environment. Floods and heat waves, as two typical extreme weather events, pose serious challenges to building resilience in cities.1,2 Such disasters often trigger a variety of secondary hazards, causing huge socio-economic losses and casualties. Urban resilience refers to a city’s capacity to withstand external shocks, maintain critical functionality during disruptions, rapidly recover from adverse impacts, and adaptively transform its systems to mitigate future risks—ensuring sustained socio-economic stability and long-term sustainability.3 Key strategies for enhancing resilience include upgrading disaster-proof infrastructure, optimizing urban spatial layout, strengthening emergency management systems, and improving the self-repairing capacity of urban ecosystems.

Despite growing interest in this field, current research reveals notable gaps. There is an urgent need to systematically understand the spatiotemporal dynamics of urban stressors, such as extreme weather variability, infrastructure failures, disease outbreaks, and human reactions to these stressors. Many existing monitoring systems remain fragmented, static, or too coarse grained to capture fast-evolving conditions at a citywide scale, thus not directly applicable to support intelligent urban reliance monitoring and decision support.

The development of multi-source sensing technology provides a promising approach to constructing resilient cities by achieving comprehensive perception and accurate prediction of the city's state, and optimizing the spatial planning strategy for disaster prevention and mitigation. However, the existing integration of multi-source data mainly occurred among two kinds of data sources4,5 rather than all the aspects. Recognizing the importance of integrating multi-sourced data and the opportunity brought by artificial intelligence (AI), we explicitly proposed the concept of fusion sensing and advocate for the exploration of its theories and methodologies, particularly those augmented by artificial intelligence.