Harnessing Large Language Models for Disaster Management: A Survey (2025)

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including their emerging role in mitigating threats to human life, infrastructure, and the environment during natural disasters. Despite increasing research on disaster-focused LLMs, there remains a lack of systematic reviews and in-depth analyses of their applications in natural disaster management. To address this gap, this paper presents a comprehensive survey of LLMs in disaster response, introducing a taxonomy that categorizes existing works based on disaster phases and application scenarios. By compiling public datasets and identifying key challenges and opportunities, this study aims to provide valuable insights for the research community and practitioners in developing advanced LLM-driven solutions to enhance resilience against natural disasters.

## introduction natural disasters are becoming increasingly frequent and severe, posing unprecedented threats to human life, infrastructure, and the environment @xcite @xcite . the 2010 haiti earthquake, for instance, resulted in over 200,000 fatalities and widespread infrastructure devastation @xcite . similarly, the 2020 australian bushfires caused the deaths of at least 33 people and an estimated loss of one billion animals @xcite . the profound impact of such catastrophic events underscores the urgent need for effective disaster management strategies. recently, large language models (llms) have transformed research and technological innovation with their exceptional capabilities in contextual understanding, logical reasoning, and complex problem-solving across multiple modalities @xcite . these capabilities position llms as powerful tools for * corresponding authors natural disaster management, enabling them to analyze vast real-time disaster data, facilitate dynamic communication with affected communities, and support critical decision-making @xcite . despite their potential, a systematic review of llms in disaster management remains absent, limiting researchers and practitioners in identifying best practices, addressing research gaps, and optimizing llm deployment for disaster-related challenges. to bridge this gap, this paper presents a comprehensive survey of llm applications in disaster management, categorizing them across three model architectures and the four key disaster phases: mitigation, preparedness, response, and recovery. we introduce a novel taxonomy that integrates application scenarios, specific tasks, and model architectures tailored to disaster-related challenges. additionally, we summarize publicly available datasets, identify key challenges, and explore avenues for enhancing the effectiveness, efficiency, and trustworthiness of llms in disaster response. this review aims to inspire and guide ai researchers, policymakers, and practitioners toward developing llm-driven disaster management frameworks. our key contributions are as follows: ## background disaster management is a multidisciplinary field that integrates resources, expertise, and strategies to mitigate the impact of increasingly severe disasters. its primary goal is to minimize immediate damage while fostering long-term resilience and adaptive recovery. disaster management comprises four interconnected phases @xcite ): in general, llms have the potential to serve as general-purpose foundations for developing specialized ai tools that enhance various aspects of disaster management. here, we categorize llm architectures into three main types: (1) encoder-based llm (e.g., bert @xcite ), which excel in contextual understanding; (2) (encoder-)decoder llm (e.g., gpt (brown, 2020)), which are optimized for sequential prediction; and (3) multimodal llms, which integrate multiple modalities to enhance information processing @xcite in disaster management, common downstream tasks include classification (e.g., damage classification), estimation (e.g., severity estimation), extraction (e.g., knowledge extraction), and generation (e.g., report generation). to tailor llms for these tasks, techniques such as finetuning and prompting are commonly employed. ## llm for disaster management foundation models can be utilized across the four disaster management phases: mitigation, preparedness, response, and recovery. within each phase, existing works are categorized based on application scenarios, specific tasks, and model architectures. figure 1 presents an overview of our taxonomy, with detailed summaries provided in appendix a. ## datasets multiple disaster-related datasets have been employed to evaluate llms in disaster management. a comprehensive list of publicly available datasets is provided in appendix b. classification datasets primarily consist of textual inputs from platforms such as twitter and news outlets, categorizing data based on informativeness (relevance) @xcite (illustrated in figure 2 ), humanitarian types @xcite , damage levels @xcite , and other relevant attributes. some datasets also incorporate visual data, including satellite imagery and social media images @xcite . model performance is typically evaluated using metrics such as accuracy and f1 score. ## challenges and opportunities large language models (llms) hold great promise for disaster management but face several key limitations. most studies deploy generic llms as universal solutions, overlooking domain-specific challenges and the need for tailored frameworks, as shown in figure 3 . additionally, current applications are heavily concentrated on disaster response, leaving other phases underexplored, as illustrated in figure 1 . to fully harness the potential of llms in disaster management, researchers must address the disaster-specific challenges outlined below.