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Artificial intelligence can enhance our ability to manage natural disasters. However, understanding and addressing its limitations is required to realize its benefits. Here, we argue that interdisciplinary, multistakeholder, and international collaboration is needed for developing standards that facilitate its implementation.Acute events of natural origin (e.g., atmospheric, hydrologic, geophysical, oceanographic, or biologic) can result in disruption and devastation to society, nature, and beyond 1,2 . Such events, which disproportionately impact certain regions (e.g., least developed countries 3 ) and populations (e.g., women and children 4 ), are often referred to as natural disasters by experts in the geoscience and disaster risk reduction communities, as reflected in the scientific literature and in Sustainable Development Goals 11.5 and 13.1.Recently, interest has grown in leveraging innovative technologies such as artificial intelligence (AI) to bolster natural disaster management 5 . In many fields, such as medicine and finance, AI has gained traction due to advances in algorithms, a growth in computational power, and the availability of large data sets. Within natural disaster management, it is hoped that such technologies can also be a boon: capitalizing on a wealth of geospatial data to strengthen our understanding of natural disasters, the timeliness of detections, the accuracy and lead times of forecasts, and the effectiveness of emergency communications.This Comment looks at successes and limitations of data collection methods and AI development for natural disaster management. It then examines the challenges and solutions surrounding AI implementation. It is shown that, although AI has the promise to enhance our ability to manage natural disasters, its effective adoption depends on collaborative efforts to address these challenges. Successes and limitations to dataThe foundation of any AI-based approach is high-quality data. A recent success is the emergence of new (and novel use of traditional) data collection methods. For example, sensor networks now help us to gather data from topographically complex regions, which are otherwise difficult to monitor, at high spatiotemporal resolutions. Such networks have proven successful for flash

Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes. At the same time, traffic flow data are a fundamental type of information for any transportation system. In this paper, we tackle the problem of traffic sensor placement on roadways to address two tasks at the same time. The first task is traffic data estimation in ordinary situations, which is vital for traffic monitoring and city planning. We design a graph-based method to estimate traffic flow on roads where sensors are not present. The second one is enhanced observability of roadways in case of extreme weather events. We propose a satellite-based multi-domain risk assessment to locate roads at high risk of closures. Vegetation and flood hazards are taken into account. We formalize the problem as a search method over the network to suggest the minimum number and location of traffic sensors to place while maximizing the traffic estimation capabilities and observability of the risky areas of a city.

<p>The ITU/WMO/UNEP Focus Group on AI for Natural Disaster Management (FG-AI4NDM) explores the potential of AI to support the monitoring and detection, forecasting, and communication of natural disasters. Building on the presentation at EGU2021, we will show how detailed analysis of real-life use cases by an interdisciplinary, multistakeholder, and international community of experts is leading to the development of three technical reports (dedicated to best practices in data collection and handling, AI-based algorithms, and AI-based communications technologies, respectively), a roadmap of ongoing pre-standardization and standardization activities in this domain, a glossary of relevant terms and definitions, and educational materials to support capacity building. It is hoped that these deliverables will form the foundation of internationally recognized standards.</p>