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Harnessing Large Language Models for Disaster Management: A Survey

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arxiv 2501.06932 v2 pith:YZYBC6KO submitted 2025-01-12 cs.CL cs.CYcs.LG

classification cs.CLcs.CYcs.LG
keywords disasterllmsmanagementnaturalexistinglanguagelargemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields. Among their practical applications, LLMs have been playing a crucial role in mitigating threats to human life, infrastructure, and the environment. Despite growing research in disaster LLMs, there remains a lack of systematic review and in-depth analysis of LLMs for natural disaster management. To address the gap, this paper presents a comprehensive survey of existing LLMs in natural disaster management, along with a taxonomy that categorizes existing works based on disaster phases and application scenarios. By collecting public datasets and identifying key challenges and opportunities, this study aims to guide the professional community in developing advanced LLMs for disaster management to enhance the resilience against natural disasters.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    RAPID is a multi-agent pipeline for zero-shot interpretable damage assessment and reporting from cross-view satellite and street-view imagery across multiple disaster types.

  2. A Survey of Scaling in Large Language Model Reasoning

    cs.AI 2025-04 unverdicted novelty 3.0 of 10

    A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.

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