<div class="csl-bib-body">
<div class="csl-entry">Zhu, G., Zhang, F., Ranjan, R., Dustdar, S., Li, J., Wang, Y., Wang, Y., Huang, X., Chen, Y., & Wang, L. (2025). A Survey of Large Language Models in Urban Natural Disaster Emergency Management: Progress, Application, and Challenges. <i>IEEE Internet Computing</i>, <i>29</i>(4), 66–76. https://doi.org/10.1109/MIC.2025.3566787</div>
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dc.identifier.issn
1089-7801
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/229330
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dc.description.abstract
The continuous impact of global climate change and human activities on the natural environment has significantly increased the frequency and complexity of emergencies and natural disasters in urban areas, posing higher requirements for the existing emergency management system. The rise of large language models (LLMs) provides a novel solution for emergency management to become intelligent. This article systematically analyzes the latest application progress of LLMs in disaster emergency management and discusses in detail the key role of LLMs in various stages before, during, and after disasters. In addition, this article puts forward the possible application framework and technical route of LLMs in emergency management in the future. Finally, this article summarizes the current challenges of applying LLMs to intelligent emergency management and outlines valuable research directions for the future.
en
dc.language.iso
en
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dc.publisher
IEEE COMPUTER SOC
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dc.relation.ispartof
IEEE Internet Computing
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dc.subject
Large Language Models
en
dc.subject
Disaster Emergency Management
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dc.subject
Urban Areas
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dc.title
A Survey of Large Language Models in Urban Natural Disaster Emergency Management: Progress, Application, and Challenges
