Harnessing Large Language Models for Disaster Management: A Survey
Authors: Zhenyu Lei, Yushun Dong, Weiyu Li, Rong Ding, Qi Wang, Jundong Li
Accepted at: acl25

Harnessing Large Language Models for Disaster Management: A Survey

Zhenyu Lei†* Yushun Dong◊* Weiyu Li◊
Rong Ding◊ Qi Wang◊* Jundong Li†*
♦University of Virginia, ◊Florida State University, ◇Northeastern University
{vjd5zr, jundong}@virginia.edu, yd24f@fsu.edu
{weiy.li, ding.ro, q.wang}@northeastern.edu

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.

1 Introduction
Natural disasters are becoming increasingly frequent and severe, posing unprecedented threats to human life, infrastructure, and the environment (Manyena, 2006; Yu et al., 2018; Chaudhary and Piracha, 2021). The 2010 Haiti earthquake, for instance, resulted in over 200,000 fatalities and widespread infrastructure devastation (DesRoches et al., 2011). Similarly, the 2020 Australian bushfires caused the deaths of at least 33 people and an estimated loss of one billion animals (Deb et al., 2020). 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 (Zhang et al., 2024b,a). These capabilities position LLMs as powerful tools for natural disaster management, enabling them to analyze vast real-time disaster data, facilitate dynamic communication with affected communities, and support critical decision-making (Otal et al., 2024).

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:

• Systematical Review: We provide the first systematical review of explorations of LLMs applications in disaster management across four key disaster phases.

• Novel Taxonomy: We propose a taxonomy integrating application scenarios, specific tasks, and model architectures, providing both practical and technical insights into this survey.

• Resource Compilation: We compile essential resources (e.g., datasets), and highlight key challenges and future research directions to advance LLM-driven disaster management.
2 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 (Sun et al., 2020):

• Mitigation involves identifying risks and vulnerabilities while implementing proactive measures to prevent disasters.

• Preparedness includes developing comprehensive plans and public education initiatives to enhance readiness for potential disasters.

• Response identifies and addresses immediate needs during a disaster, including emergency rescue operations and resource distribution.

• Recovery involves rebuilding affected areas, addressing both physical and social impacts to facilitate a return to normalcy.

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 (Devlin, 2018)), 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 (Tiong et al., 2022; Madichetty et al., 2021) 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 fine-tuning and prompting are commonly employed.

3 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.

3.1 Disaster Mitigation

Assessing vulnerabilities is a crucial component of disaster mitigation, where LLMs have demonstrated promising potential. This process involves identifying and analyzing infrastructure and communities at risk, enabling proactive measures to reduce disaster impact.

Vulnerability Classification. A system named Infrastructure Ombudsman has leveraged supervised learning with encoder-based LLMs and zero-shot prompt learning with (encoder-)decoder LLMs to detect and classify concerns about potential infrastructure failures from social media data (Chowdhury et al., 2024). This approach enables decision-makers to effectively prioritize resources and address critical issues in a timely manner.

Answer Generation. Beyond infrastructure vulnerability assessment, (encoder-)decoder LLMs can help address community vulnerability-related queries by retrieving and leveraging the Social Vulnerability Index (SVI) (Martelo and Wang, 2024).

3.2 Disaster Preparedness

In the long term, LLMs can play a pivotal role in disaster preparedness through (1) enhancing public awareness by disseminating accurate and accessible information, and (2) supporting disaster forecasting with advanced data analysis. Building on these forecasts, LLMs can aid decision-makers in issuing (3) timely disaster warnings, improving short-term preparedness. Furthermore, LLMs can support well-structured (4) evacuation planning, ensuring the safe relocation of individuals and assets.

3.2.1 Public Awareness Enhancement

Enhancing public awareness of disasters is crucial, particularly by providing insights and knowledge derived from past disaster experiences.

Knowledge Extraction. Encoder-based LLMs have been fine-tuned to extract disaster-related knowledge from news articles and social media (Fu et al., 2024), as well as from extensive disaster literature (Zhang and Wang, 2023), using Named Entity Recognition (NER). To improve the logical coherence of extracted entities, Ma et al. propose BERT-BiGRU-CRF for NER, enabling the construction of disaster knowledge graphs (Ma et al., 2023). In addition, (encoder-)decoder LLMs have been fine-tuned with instructional learning to extract knowledge triplets from documents for knowledge graph construction (Wu et al., 2024).
Figure 1: Taxonomy of applications of LLMs in disaster management. This survey categorizes the utilization of LLMs across four disaster phases, highlighting specific applications where tasks such as classification, estimation, extraction, and generation are performed by three types of LLMs (Encoder-based, (Encoder-)Decoder, and Multimodal LLM). The chart in the bottom-right corner presents the distribution of papers across each phase.

Answer Generation. The extracted disaster knowledge could be incorporated in *encoder-/decoder LLMs’* prompts, facilitating disaster-related question answering (Hostetter et al., 2024; Martelo and Wang, 2024; Li et al., 2023). Additionally, techniques such as retrieval-augmented generation (RAG) have been employed to further improve knowledge integration (Zhu et al., 2024).

3.2.2 Disaster Prediction
Effective disaster preparedness also relies on accurate and reliable disaster prediction.

Occurrence Classification. *Encoder-based LLMs* have been widely employed for disaster prediction. For instance, BERT has been integrated with GRU and CNN to predict disasters (Indra and Duraipandian, 2023). However, textual data alone is often limited due to its subjective and imprecise nature, prompting the adoption of *multimodal LLMs* that incorporate multiple data modalities. For instance, Zeng et al. combine historical flood data with geographical descriptions of specific locations to assess disaster risk (Zeng and Bertsimas, 2023). Additionally, satellite imagery has been leveraged to provide visual context, enhancing predictive accuracy (Liu and Zhong, 2023). To further improve disaster prediction with explicit external knowledge, *(encoder-)decoder LLMs* have been integrated with RAG to retrieve historical flood data, aiding in risk assessment and action recommendation (Wang et al., 2024).

3.2.3 Disaster Warning
Once a disaster is anticipated, timely warnings are essential for ensuring public safety.

Warning Generation. *(Encoder-)decoder LLMs* have proven valuable in generating warning messages based on rule-based alerts derived from streaming data (Chandra et al., 2024), significantly improving the responsiveness of warning systems. Additionally, RAG has enhanced LLMs by enabling the retrieval of disaster alerts from official APIs, providing real-time information on impending disasters (Martelo and Wang, 2024).

Image Generation. In addition to textual warnings, visual warnings can provide more vivid and intuitive descriptions, effectively reaching a broader audience. To achieve this, *multimodal LLMs* enhanced by diffusion-based text-to-image generative models can generate detailed visual representations of impending disasters (Lubin et al., 2024), enhancing the clarity and impact of disaster alerts.
3.2.4 Evacuation Planning
Plan Generation. To safeguard individuals and property from impending disasters, (encoder-)decoder LLMs have been prompted to generate escape plans and provide evacuation recommendations (Hostetter et al., 2024).

3.3 Disaster Response
With accurate and real-time (1) disaster identification and (2) situation assessment, decision-makers can acquire critical insights to establish a solid foundation for response efforts. Additionally, LLMs can facilitate (3) disaster information coordination, enhancing collaboration among stakeholders for more effective disaster response. As a result, decision-makers can leverage LLMs to execute key actions, including (4) disaster rescue operations and (5) disaster-related consultations.

3.3.1 Disaster Identification
Effective disaster response begins with accurate and real-time identification, enabling efficient interventions (Said et al., 2019; Weber et al., 2020). Social media serves as a valuable resource in this process, offering real-time updates from affected individuals (Anderson, 2016; Trono et al., 2015).
Relevance Classification with Encoder-based LLMs. Classifying social media posts to identify disaster-related content is a crucial step in disaster detection, where LLMs have proven to be highly effective. Encoder-based LLMs augmented with trainable adapters are commonly employed for this task through fine-tuning on annotated disaster corpora (Ningsih and Hadiana, 2021; Singh et al., 2022; Lamsal et al., 2024a). Recognizing the diverse sources of disaster data, ensemble methods combine predictions from multiple LLMs to leverage their complementary strengths in processing varied linguistic patterns (Mukhtiar et al., 2023). Pure LLM-based approaches may struggle to capture fine-grained structural features in disaster-related posts. To address this, hybrid architectures integrate CNNs to capture local n-gram patterns (Franceschini et al., 2024; Song and Huang, 2021; Meghatria et al., 2024), attention-based BiLSTMs to model sequential dependencies (Huang et al., 2022), and graph neural networks (GNNs) to represent semantic word relationships (Manthena, 2023; Ghosh et al., 2022). To tackle the challenge of limited labeled training data, active learning has been employed to automatically label informative samples (Paul et al., 2023).

Relevance Classification with Encoder-based LLMs. Furthermore, (encoder-)decoder LLMs such as GPT-4 have demonstrated strong performance in relevance classification using prompt learning techniques (Taghian Dinani et al., 2023).
Relevance Classification with Multimodal LLMs. Image data also provide valuable insights for disaster analysis and can be integrated to enhance classification using multimodal LLMs. This integration can be achieved through simple aggregation (Kamoji et al., 2023; Madichetty et al., 2021) or attention-based mechanisms (Shetty et al., 2024). To address challenges arising from multimodal heterogeneity, Zhou et al. employ a Cycle-GAN combined with a mixed fusion strategy (Zhou et al., 2023b). Beyond multimodal heterogeneity, research also tackles other challenges in multimodal learning. These include addressing label scarcity through semi-supervised minimax entropy domain adaptation frameworks (Wang and Wang, 2022) and enhancing model performance by leveraging the complementary strengths of diverse LLMs and visual models using ensemble methods (Hanif et al., 2023). Beyond social media, data from sources such as satellite imagery and news articles can further enhance disaster analysis (Jang et al., 2024).

3.3.2 Disaster Situation Assessment
After disaster identification, assessing its severity and spread is essential for formulating effective response strategies.

Situation Classification. encoder-based LLMs have been fine-tuned to for binary classification to identify situational posts (Madichetty and Sridivi, 2021). Raj et al. employ BERT and NER to extract disaster-related locations, using location counts as an indicator of disaster severity (Raj et al., 2023). Additionally, multimodal LLMs integrate visual data to further enhance disaster situational assessment (Kanth et al., 2022).

Severity Estimation. While classification provides only a coarse understanding, severity estimation offers precise quantitative insights. (encoder-)decoder LLMs enhanced with chain-of-thought (CoT) reasoning have been used to estimate earthquake intensity, expressed as Modified Mercalli Intensity (MMI) (Mousavi et al., 2024). In addition, multimodal LLMs use rich image data for more accurate estimations. For example, FloodDepth-GPT employs prompt-based guidance with GPT-4 to estimate floodwater depth from flood images.
Description Generation. Beyond categorical and statistical descriptions, multimodal LLMs can generate more comprehensible textual situational reports from disaster images (Hu and Rahnemoonfar, 2024; Wolf et al., 2023).

3.3.3 Disaster Information Coordination

Coordinating disaster-related information is crucial for ensuring an organized and collaborative response (Comfort et al., 2004; Bharosa et al., 2010). Social media plays a pivotal role in this process, as individuals actively share posts containing warnings, urgent needs, and other critical information (Lindsay, 2011; Imran et al., 2015).

Usefulness Estimation. To improve the accessibility of valuable information, encoder-base LLMs are utilized to filter informative tweets by computing usefulness ratings (Yamamoto et al., 2022). However, this approach requires a predefined threshold to determine the relevance of a tweet.

Relevance Classification. Several studies fine-tune encoder-based LLMs for binary relevance classification, as discussed in Section 3.3.1. Additionally, LLMs have been applied to multi-level relevance classification to further refine disaster-related information filtering (Blomeier et al., 2024).

Information Classification. To facilitate information dissemination, several studies have fine-tuned encoder-based LLMs to classify posts based on different information types, including actionable types such as "important for managers" (Sharma et al., 2021); humanitarian types such as "Injured people" (Yuan et al., 2022); and disaster-specific types (Liu et al., 2021). When fine-tuning data is limited, augmentation strategies such as manual hashtag annotation (Boros et al., 2022) and self-training with soft labeling (Li et al., 2021) are employed to enhance classification performance.

Pure LLM-based methods may have limitations, as discussed in Section 3.3.1. In contrast, hybrid architectures enhance performance by integrating CNNs and BiLSTMs to improve local pattern comprehension (Zou et al., 2024) and employing Graph Attention Networks (GATs) to capture correlations between tweet embeddings and information types (Zahera et al., 2021). Additionally, FF-BERT leverages an ensemble of BERT and CNN to combine model strengths for improved classification (Wilkho et al., 2024). Other studies enhance the application of LLMs in disaster information classification by extracting rationales—evidence that supports classification decisions (Nguyen and Rudra, 2022b, 2023). RACLC (Nguyen and Rudra, 2022a) employs a two-stage framework, utilizing contrastive learning to refine rationale extraction and improve classification performance.

(Encoder-)decoder LLMs have also been employed for disaster type and humanitarian classification through instruction tuning (Otal and Canbaz, 2024; Yin et al., 2024), as well as zero-shot and few-shot prompting (Dinani et al., 2024).

Multimodal LLMs can integrate rich visual data from social media to enhance classification by leveraging multiple modalities. This integration can be achieved through simple feature aggregation (Zhang et al., 2022; Yu and Wang, 2024) or more advanced fusion techniques, such as cross-attention mechanisms (Abavisani et al., 2020) and dual transformer architectures (Zhou et al., 2023a). Additionally, Basit et al. classify posts into humanitarian or structural categories only when the text and image classification outputs align; otherwise, the posts are uninformative (Basit et al., 2023).

Need Classification. Social media enables individuals to express urgent needs during disasters. Encoder-based LLMs have been employed to detect disaster-related needs (Yang et al., 2024; Vitiugin and Purohit, 2024) and rescue requests (Tormanen et al., 2023). Responders also use social media to share available resources. Encoder-based LLMs have been employed to match needs with resources using cosine similarity-based retrieval methods, where both offer and request posts are embedded using XLM-RoBERTa (Conneau, 2019), optimizing resource allocation.

Location Extraction. Additionally, various post-processing techniques enhance information dissemination, particularly through location extraction. Several studies fine-tune encoder-based LLMs for location reference recognition (LRR), classifying tokens into categories such as "Inside Locations" (ILOC) and "Other Tokens" (O) (Mehmood et al., 2024; Suwaleh et al., 2022; Koshy and Elango, 2024). LRR can be further improved by integrating a conditional random field (CRF) model, which enhances the logical consistency of extracted locations (Ma et al., 2022; Zhang et al., 2021). Furthermore, external knowledge corpora can support location extraction. For instance, Caillaut et al. use cosine similarity to match post entities with a knowledge base, ensuring the authenticity of extracted locations (Caillaut et al., 2024).
(Encoder-)decoder LLMs are widely used for extracting location-relevant information through prompt learning (Yu and Wang, 2024). To enhance accuracy, external knowledge has been incorporated into prompts, including geo-knowledge (Hu et al., 2023) and Object Character Recognition-based object descriptions (Firmansyah et al., 2024).

Summary Extraction. Furthermore, summarizing disaster-related posts provides a macro-level understanding during crises. Several studies focus on identifying critical and informative posts for summarization by integrating advanced techniques into encoder-based LLMs, such as integer linear programming (ILP) (Nguyen and Rudra, 2022a; Nguyen et al., 2022) and Rapid Automatic Keyword Extraction (RAKE) (Garg et al., 2024).

Summary Generation. (Encoder-)decoder LLMs extend summarization capabilities by generating summaries from retrieved text. For example, Vituigin et al. rank key tweets using an LSTM model and apply a T5 model to generate summaries based on the top-ranked tweets (Vituigin and Castillo, 2022). Crisis2Sum performs query-focused summarization through a multi-step process, including query-informed document retrieval, reranking, fact extraction, clustering, fusion into event nuggets, and final selection for summarization (Seeberger and Riedhammer, 2024a). Additionally, agent-based approaches can enhance summary quality by leveraging multiple LLMs for document retrieval, reranking, and instruction-following summarization (Seeberger and Riedhammer, 2024b).

Report Generation. (Encoder-)decoder LLMs have been employed for disaster report generation, utilizing techniques such as RAG to extract relevant web data (Colverd et al., 2023) and Chain-of-Thought reasoning to enhance the coherence and accuracy of generated reports.

3.3.4 Disaster Rescuing

Grounded in a comprehensive understanding of the disaster situation, disaster rescue focuses on saving lives and protecting property through timely and coordinated actions.

Plan Generation. Effective rescue operations require well-structured rescue plans. (Encoder-)decoder LLMs are prompted to generate actionable response plans, offering essential guidance for disaster response (Goecks and Waytowich, 2023).

Code Generation. Once a plan is established, (encoder-)decoder LLMs can support its execution by assisting organizations and rescue teams. For instance, they can facilitate robotic system guidance during rescue operations by translating verbal inputs into actionable operational commands using RAG (Panagopoulos et al., 2024).

3.3.5 Disaster Issue Consultation

During disasters, affected individuals and organizations often seek reliable guidance. Disaster issue consultation provides advice, safety updates, and expert recommendations, helping them access resources, evaluate options, and make informed decisions (Jiang, 2024).

Answer Generation. (Encoder-)decoder LLMs are employed to generate answers for frequently asked questions and provide disaster-related guidance (Rawat, 2024; Chen and Fang, 2024). To mitigate hallucination, RAG is integrated with verified disaster-related documents. For example, WildfireGPT retrieves wildfire-related literature and data to enhance prompts (Xie et al., 2024). Chen et al. introduce a prompt chain to guide LLM reasoning over a disaster knowledge graph, incorporating structured knowledge (Chen et al., 2024). Unlike traditional RAG approaches without training, Xia et al. combine fine-tuning for implicit knowledge updates with RAG for explicit knowledge, further improving response quality (Xia et al., 2024).

Additionally, multi-modal LLMs can integrate textual and visual data to enhance disaster response. For example, several visual question answering (VQA) models, such as Plug-and-Play VQA (Tiong et al., 2022), have been prompted for zero-shot VQA in disaster scenarios (Sun et al., 2023). To handle complex user queries, ADI introduces sequential modular tools, incorporating vision-language models (VLMs), object detection models, and semantic segmentation models (Liu et al., 2024). Furthermore, FloodLense combines ChatGPT with diffusion models to highlight disaster-affected areas in images, enhancing flood-related geographical question answering (Kumbam and Vejre, 2024).

3.4 Disaster Recovery

LLMs can play a crucial role in (1) disaster impact assessment, a vital step in the recovery process. By providing a comprehensive understanding of disaster impacts, LLMs can assist decision-makers in (2) generating recovery plans tailored to specific needs. Additionally, disaster responders have leveraged LLMs for (3) continuous recovery process track-
ing, ensuring effectiveness and progress throughout the recovery phase.

3.4.1 Disaster Impact Assessment

Accurately assessing the extent of damage across both physical and social dimensions is essential for prioritizing recovery efforts effectively.

Damage Classification. From the physical dimension, encoder-based LLMs have been employed to identify and categorize disaster-related damage (e.g., human/infrastructure damage (Malik et al., 2024), water/power supply damage (Chen and Lim, 2021)) Additionally, Zou et al. propose a BERT-BiLSTM-Sit-CNN framework, improving textual understanding for damage-related post identification and damage-type classification (Zou et al., 2024). Beyond type classification, LLMs have been utilized to assess damage severity. For instance, Jeba et al. employ BERT to classify damage impact severity in social media posts and news articles (Jeba et al., 2024).

Damage Estimation. Damage severity can be more effectively quantified through fine-grained estimation. Chen et al. compute damage severity scores by measuring the similarity between post tokens and predefined seed words’ embeddings, both of which are derived from encoder-based LLMs (Chen and Lim, 2021).

Answer Generation. In addition, (encoder-)decoder LLMs can answer specific assessment questions. Ziaullah et al. employ RAG-enhanced LLMs to retrieve operational status updates of critical infrastructure facilities from social media data (Ziaullah et al., 2024). Multimodal LLMs further incorporate remote sensing data for enhanced assessment. Estevao et al. prompt GPT-4o to generate damage assessments based on building images (Estêvão, 2024). To improve modality alignment, SAM-VQA employs a supervised attention-based vision-language model (VLM) to integrate image and question features for visual question answering (VQA) tasks (Sarkar et al., 2023). Additionally, auxiliary tasks have been leveraged to enhance VQA performance. For instance, DATWEP dynamically balances the significance of segmentation and VQA tasks by adjusting class weights during training (Alsan and Arsan, 2023).

Statistic Extraction. (Encoder-)decoder LLMs have also used few-shot learning to extract fatality information from social media (Hou and Xu, 2022), offering timely insights into human loss.

<table>
  <tr>
    <th>Get over it Choc you lost. Anyone with half a brain could see it. http://t.co/ytha5NX6</th>
    <th>off-topic</th>
  </tr>
  <tr>
    <th>Flooding hits eastern Australia: Hundreds of homes are inundated and several people reported missing as flood waters rise in the ...</th>
    <th>on-topic</th>
  </tr>
</table>

Figure 2: A sample of dataset for disaster relevance classification from CrisisLexT6 (Olteanu et al., 2014).

Sentiment Classification. From the social dimension, disasters can influence public sentiment, where encoder-based LLMs (Han et al., 2024a; Berbère et al., 2023) have been fine-tuned to classify social media posts into positive and negative emotions. In addition, Li et al. employ (encoder-)decoder LLM (e.g., GPT 3.5) to classify posts into five emotional types, such as "panic" and "sadness", using zero-shot prompting (Li et al., 2025). This approach helps responders better understand and address the emotional impact of disasters.

3.4.2 Recovery Plan Generation

Based on impact assessment, a recovery plan is formulated to rebuild infrastructure, restore services, and strengthen resilience (Hallegatte et al., 2018).

Plan Generation. (Encoder-)decoder LLMs have been applied in certain recovery scenarios to generate recovery and reconstruction plans. For example, ChatGPT has been prompted to develop disaster recovery strategies for business restoration (White and Liptak, 2024; Lakhera, 2024).

3.4.3 Recovery Process Tracking

Continuous tracking of the recovery process ensures that progress remains aligned with the planned timeline, allowing decision-makers to adapt recovery strategies to evolving needs.

Sentiment Classification. Encoder-based LLMs (e.g., BERTweet) have been employed to assess public sentiment throughout the recovery period (CONTRERAS et al.), enabling responders to tailor recovery efforts to effectively address the emotional needs of affected populations.

4 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.
Figure 3: Publication counts utilizing existing LLMs and developing new models across the four phases of disaster management.

Classification datasets primarily consist of textual inputs from platforms such as Twitter and news outlets, categorizing data based on informativeness (relevance) (Olteanu et al., 2014) (illustrated in Figure 2), humanitarian types (Imran et al., 2016), damage levels (Alam et al., 2021b), and other relevant attributes. Some datasets also incorporate visual data, including satellite imagery and social media images (Alam et al., 2018). Model performance is typically evaluated using metrics such as accuracy and F1 score.

Estimation datasets usually provide quantitative labels such as flood depths (Akinboyewa et al., 2024). Metrics like Mean Absolute Error (MAE) are used for evaluation.

Generation datasets are also extensively used and primarily fall into two categories: question answering and summarization. Question-answering datasets provide disaster-related questions paired with crowdsourced annotated answers (Rawat, 2024). Additionally, multimodal question-answering datasets, which incorporate disaster-related images as contextual information, are widely utilized (Sun et al., 2023). For the summarization task, large collections of documents serve as inputs, with reference summaries curated by domain experts (McCreadie and Buntain, 2023). Both question-answering and summarization tasks are evaluated using metrics such as BLEU.

Extraction datasets identify and label specific elements within a sentence, such as keywords (Nguyen and Rudra, 2022a) and locations (Suwaileh et al., 2022). Tokens are labeled as "outside," "start," or "end" to indicate their extraction status. These datasets are primarily used for token-level classification tasks and are evaluated using classification metrics.

5 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.

Dataset Construction. Current datasets are heavily skewed toward classification tasks, leaving other areas underexplored (Proma et al., 2022). Additionally, raw disaster data often contains uncertainty and bias (Smith and Katz, 2013), posing challenges in constructing reliable datasets. Innovative approaches, such as synthetic data generation (Kalluri et al., 2024), offer a promising solution to enhance dataset coverage across diverse disaster scenarios.

Efficient Deployment. Large-scale LLMs face efficiency challenges (Ramesh Raja et al., 2024), limiting their viability for real-time decision-making in emergency disaster scenarios. While lightweight models offer a more efficient alternative (Saleem et al., 2024), they often compromise robustness in disaster-related tasks. Developing models that balance efficiency and reliability is essential for effective disaster management.

Robust Generation. (Encoder-)decoder LLMs are prone to hallucination, generating factually inaccurate outputs that pose serious risks in disaster contexts, such as false evacuation routes, resource misallocation, and potential loss of lives. To mitigate these risks, strategies such as integrating RAG with external knowledge bases (Colverd et al., 2023), domain-specific training (Lamsal et al., 2024a), and uncertainty estimation (Xu et al.) can help reduce hallucinated outputs and improve reliability.

Unified Evaluation. Most generative benchmarks for disaster (e.g. report/summary generation) rely on reference sets produced by domain experts. While these curated answers provide high-quality supervision, the underlying annotation criteria such as what counts as a correct answer often differ from one dataset to another. Consequently, published results are difficult to compare directly, because each study is implicitly tied to its own expert standard. As a result, it’s important to build up a unified eval-
uation protocol to make more reliable comparisons.

6 Conclusion

This paper surveys the application of LLMs in disaster management across the four disaster phases, introducing a taxonomy that integrates application scenarios, specific tasks, and the architectures of models addressing these tasks. By presenting publicly available datasets and identifying key challenges, we aim to inspire collaborative efforts between AI researchers and decision-makers, ultimately enabling the full potential of LLMs to build more resilient communities and advance proactive disaster management practices.

Acknowledgment

This work is supported in part by the National Science Foundation (NSF) under grants IIS-2006844, IIS-2144209, IIS-2223769, CNS-2154962, BCS-2228534, BCS-2228533, CMMI-2411248, CMMI-2125326, and CMMI-2402438. All authors would like to thank the reviewers and chairs for their constructive feedback and suggestions.

Limitations

Survey Scope. This work focuses exclusively on disaster management applications only where existing LLMs have been utilized, leaving out other potential scenarios (e.g. repair cost evaluation during the recovery phase) that have yet to be explored in current research. In addition, we focus mostly on the natural disaster instead of man-made disaster. While these unexplored areas hold significant promise for future advancements, they fall beyond the scope of this study due to space constraints.

Categorization. This work categorizes papers based on their model architecture. However, it would be also beneficial to analyze existing papers from the other perspectives such as model size, inference efficiency, and model performance.

Datasets. Additionally, we include only a subset of datasets used in existing studies, prioritizing those that are easily accessible. Many datasets either are not open-sourced, have restrictive access policies, or lack assured quality, making them less suitable for reproducibility and further research.

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Fedor Vitiugin and Hemant Purohit. 2024. Multilingual serviceability model for detecting and ranking help requests on social media during disasters. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 1571–1584.

Junaid Abdul Wahid, Lei Shi, Yufei Gao, Bei Yang, Lin Wei, Yongcai Tao, Shabir Hussain, Muhammad Ayoub, and Imam Yagoub. 2022. Topic2labels: A framework to annotate and classify the social media data through lda topics and deep learning models for crisis response. Expert Systems with Applications, 195:116562.

Congcong Wang, Paul Nulty, and David Lillis. 2021. Transformer-based multi-task learning for disaster tweet categorisation. arXiv preprint arXiv:2110.08010.

Gelan Wang, Yu Liu, Shukai Liu, Ling Zhang, and Liquan Yang. 2024. Remflow: Rag-enhanced multi-factor rainfall flooding warning in sponge airports via large language model.
Jing Wang and Kexin Wang. 2022. Bert-based semi-supervised domain adaptation for disastrous classification. Multimedia Systems, 28(6):2237–2246.

Ethan Weber, Nuria Marzo, Dim P Papadopoulos, Aritra Biswas, Agata Lapedriza, Ferda Offi, Muhammad Imran, and Antonio Torralba. 2020. Detecting natural disasters, damage, and incidents in the wild. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIX 16, pages 331–350. Springer.

Gwendolyn White and Sadie Liptak. 2024. Small business continuity and disaster recovery plans using ai and chatgpt. Issues in Information Systems, 25(4):118–126.

Rohan Singh Wilkho, Shi Chang, and Nasir G Gharaibeh. 2024. Ff-bert: A bert-based ensemble for automated classification of web-based text on flash flood events. Advanced Engineering Informatics, 59:102293.

Kristina Wolf, Dominik Winecki, and Arnab Nandi. 2023. Camera-first form filling: Reducing the friction in climate hazard reporting. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, pages 1–7.

Zhengrong Wu, Haibo Yang, Yingchun Cai, Bo Yu, Chuangheng Liang, Zheng Duan, and Qiuhua Liang. 2024. Intelligent monitoring applications of landslide disaster knowledge graphs based on chatglm2. Remote Sensing, 16(21):4056.

Yongqi Xia, Yi Huang, Qianqian Qiu, Xueying Zhang, Lizhi Miao, and Yixiang Chen. 2024. A question and answering service of typhoon disasters based on the t5 large language model. ISPRS International Journal of Geo-Information, 13(5):165.

Yangxinyu Xie, Bowen Jiang, Tanwi Mallick, Joshua David Bergerson, John K Hutchison, Duane R Verner, Jordan Branham, M Ross Alexander, Robert B Ross, Yan Feng, et al. 2024. Wildfiregpt: Tailored large language model for wildfire analysis. arXiv preprint arXiv:2402.07877.

Wei Xu, Xuanhua Xu, and Weiwei Zhang. Enhancing emergency decision making through risk quantification and action adjustment of human-llm dual agents. Available at SSRN 5037356.

Futo Yamamoto, Tadahiko Kumamoto, Yu Suzuki, and Akiyo Nadamoto. 2022. Methods of calculating usefulness ratings of behavioral facilitation tweets in disaster situations. In Proceedings of the 11th International Symposium on Information and Communication Technology, pages 88–95.

Pingjing Yang, Ly Dinh, Alex Stratton, and Jana Diesner. 2024. Detection and categorization of needs during crises based on twitter data. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 1713–1726.

Kai Yin, Chengkai Liu, Ali Mostafavi, and Xia Hu. 2024. Crissensse-llm: Instruction fine-tuned large language model for multi-label social media text classification in disaster informatics. arXiv preprint arXiv:2406.15477.

Chen Yu and Zhigou Wang. 2024. Multimodal social sensing for the spatio-temporal evolution and assessment of nature disasters. Sensors, 24(18):5889.

Manzhu Yu, Chaowei Yang, and Yun Li. 2018. Big data in natural disaster management: a review. Geosciences, 8(5):165.

Faxi Yuan, Chao Fan, Hamed Farahmand, Natalie Coleman, Amir Esmalian, Cheng-Chun Lee, Flavia I Patrascu, Cheng Zhang, Shangjia Dong, and Ali Mostafavi. 2022. Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness. Environmental Research: Infrastructure and Sustainability, 2(2):025006.

Hamada M Zahera, Rricha Jalota, Mohamed Ahmed Sherif, and Axel-Cyrille Ngonga Ngomo. 2021. I-aids: identifying actionable information from disaster-related tweets. IEEE Access, 9:118861–118870.

Kiran Zahra, Muhammad Imran, and Frank O Ostermann. 2020. Automatic identification of eyewitness messages on twitter during disasters. Information processing & management, 57(1):102107.

Cynthia Zeng and Dimitris Bertsimas. 2023. Global flood prediction: a multimodal machine learning approach. arXiv preprint arXiv:2301.12548.

Mengna Zhang, Qisong Huang, and Hua Liu. 2022. A multimodal data analysis approach to social media during natural disasters. Sustainability, 14(9):5536.

Min Zhang and Juanle Wang. 2023. Automatic extraction of flooding control knowledge from rich literature texts using deep learning. Applied Sciences, 13(4):2115.

Qiang Zhang, Keyang Ding, Tianwen Lv, Xinda Wang, Qingyu Yin, Yiwen Zhang, Jing Yu, Yuhao Wang, Xiaotong Li, Zhuoyi Xiang, et al. 2024a. Scientific large language models: A survey on biological & chemical domains. arXiv preprint arXiv:2401.14656.

Xinsheng Zhang and Yulong Ma. 2023. An albert-based textcnn-hatt hybrid model enhanced with topic knowledge for sentiment analysis of sudden-onset disasters. Engineering Applications of Artificial Intelligence, 123:106136.

Yan Zhang, Zeqiang Chen, Xiang Zheng, Nengcheng Chen, and Yongqiang Wang. 2021. Extracting the location of flooding events in urban systems and analyzing the semantic risk using social sensing data. Journal of Hydrology, 603:127053.
Yu Zhang, Xiuxi Chen, Bowen Jin, Sheng Wang, Shuiwang Ji, Wei Wang, and Jiawei Han. 2024b. A comprehensive survey of scientific large language models and their applications in scientific discovery. arXiv preprint arXiv:2406.10833.

Tingting Zhao, Shubo Tian, Jordan Daly, Melissa Geiger, Minna Jia, and Jinfeng Zhang. 2024. Information retrieval and classification of real-time multi-source hurricane evacuation notices. arXiv preprint arXiv:2401.06789.

Bing Zhou, Lei Zou, Ali Mostafavi, Binbin Lin, Mingzheng Yang, Nasir Gharaibeh, Heng Cai, Joynal Abedin, and Debayan Mandal. 2022. Victimfinder: Harvesting rescue requests in disaster response from social media with bert. Computers, Environment and Urban Systems, 95:101824.

Jinyan Zhou, Xingang Wang, Ning Liu, Xiaoyu Liu, Jiandong Lv, Xiaomin Li, Hong Zhang, and Rui Cao. 2023a. Visual and linguistic double transformer fusion model for multimodal tweet classification. In 2023 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.

Jinyan Zhou, Xingang Wang, Jiandong Lv, Ning Liu, Hong Zhang, Rui Cao, Xiaoyu Liu, and Xiaomin Li. 2023b. Public crisis events tweet classification based on multimodal cycle-gan. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2251–2257. IEEE.

Jun Zhu, Pei Dang, Yungang Cao, Jianbo Lai, Yukun Guo, Ping Wang, and Weilian Li. 2024. A flood knowledge-constrained large language model inter-actable with gis: enhancing public risk perception of floods. International Journal of Geographical Information Science, 38(4):603–625.

Abdul Wahab Ziaullah, Ferda Ofli, and Muhammad Imran. 2024. Monitoring critical infrastructure facilities during disasters using large language models. arXiv preprint arXiv:2404.14432.

Henry Peng Zou, Yue Zhou, Weizhi Zhang, and Cornelia Caragea. 2023. Decrisismb: Debiased semi-supervised learning for crisis tweet classification via memory bank. arXiv preprint arXiv:2310.14577.

Liwei Zou, Zhi He, Chengle Zhou, and Wenbing Zhu. 2024. Multi-class multi-label classification of social media texts for typhoon damage assessment: a two-stage model fully integrating the outputs of the hidden layers of bert. International Journal of Digital Earth, 17(1):2348668.

A Summary of Papers

A.1 Summary Table

Table 1 summarizes the surveyed papers, detailing their disaster phases, application scenarios, specific tasks, and architecture types.

A.2 Pipeline Illustration

In this section, we present Figure 4, which illustrates the role of LLMs in disaster management. The figure outlines the major pipelines of three LLM architectures—encoder-based, (encoder-)decoder, and multimodal—applied across the four task types covered in this survey: classification, extraction, estimation, and generation. This visualization provides key insights into their mechanisms and applications in disaster management.

A.3 Statistics

To provide a comprehensive overview of the current state of LLMs in disaster management, we present statistics from the surveyed papers, highlighting a significant gap between the NLP and disaster management communities. This gap underscores the urgent need for stronger interdisciplinary collaboration to bridge these fields and fully harness the potential of LLMs in addressing disaster-related challenges.

Figure 3 illustrates the number of publications leveraging existing LLMs versus those developing new frameworks, revealing that most studies are heavily application-focused. The majority rely on fine-tuning or prompting existing LLMs for disaster management tasks, rather than designing novel architectures. While some efforts have provided valuable insights, most research remains concentrated on the response phase, with limited exploration across other critical disaster management scenarios. Figure 5 illustrates the distribution of publications across academic venues, revealing that relatively few disaster management papers appear in NLP- or AI-specific conferences and journals. This trend reflects limited engagement from the LLM research community in this domain, underscoring the need to increase awareness and foster greater collaboration within the field.

B Datasets

Table 2 summarizes existing publicly available datasets. For classification tasks, we exclude datasets that focus on a single disaster type if they
Figure 4: Pipeline of major tasks performed by different types of LLMs in disaster management.

are already incorporated into comprehensive benchmarks such as CrisisBench (Alam et al., 2021b).

B.1 Classification Datasets

• CrisisLexT6 (Olteanu et al., 2014): This dataset is designed for relevance classification. It contains data from six crisis events between October 2012 and July 2013.

• CrisisLexT26 (Olteanu et al., 2015): This dataset is an updated version of CrisisLexT6, which contains public data from 26 crisis events in 2012 and 2013 with relevance information and six humanitarian categories.

• CrisisNLP (Imran et al., 2016): This dataset is a large-scale dataset that includes classes from humanitarian disaster responses and classes related to health emergencies. It is collected from 19 different disaster events that happened between 2013 and 2015.

• SWDM2013 (Imran et al., 2013): This dataset is utilized for relevance classification that consists of tweets from two events: (i) The Joplin collection contains tweets from the tornado that struck Joplin, Missouri on May 22, 2011; (ii) The Sandy collection contains tweets collected from Hurricane Sandy that struck the Northeastern US on Oct 29, 2012.

• ISCRAM2013 (Imran et al., 2013): This dataset consists of tweets collected from the same events as in SWDM2013, containing both relevance and humanitarian categories.

• Disaster Response Data (DRD) (Alam et al., 2021b): This dataset consists of tweets collected during various crisis events that took place in 2010 and 2012. This dataset is annotated using 36 classes that include relevance as well as humanitarian categories.

• Disasters on Social Media (DSM) (Alam et al., 2021b): This dataset comprises 10K tweets annotated with relevance labels.

• AIDR (Imran et al., 2014): This dataset contains data obtained from the AIDR system on September 25, 2013, collecting tweets using hashtags such as "#earthquake". It is utilized for relevance and humanitarian classification.

• CrisisMMD (Alam et al., 2018): This dataset is a multimodal and multitask dataset comprising 16k labeled tweets and corresponding images. Tweets have been sourced from seven natural disaster events that took place in 2017. Each sample is annotated with relevance, humanitarian (eight classes), and damage severity categories (mild, severe, and none).

• Multi-Crisis (Sánchez et al., 2023): This dataset was proposed to evaluate transfer learning scenarios where data from high-resource languages (e.g., English) is used to classify messages in low-resource languages (e.g., Spanish, Italian) and unseen crisis domains, with relevance and humanitarian categories. It is collected from 7 existing datasets, 53 crisis events, and contains 9 domains.
Figure 5: Publication distribution across various academic venues, with a focus on model design on the left and application-based research on the right.

• CrisisBench (Alam et al., 2021b): This dataset is a comprehensive benchmark consolidated from 9 existing datasets, utilized for relevance and humanitarian classification.

• Eyewitness Messages (Zahra et al., 2020): This dataset is designed to identify disaster eyewitness-related tweets and classify them into three categories: direct eyewitnesses, indirect eyewitnesses, and vulnerable eyewitnesses—individuals who anticipate a disaster and are present in regions where disaster warnings have been issued. It comprises 14,000 tweets collected from earthquakes, hurricanes, and wildfires.

• TREC Incident Streams (McCreadie et al., 2019): This dataset has been developed as part of the TREC-IS 2018 evaluation challenge and consists of 20k tweets labeled for actionable information identification and information criticality assessment.

• HumAID (Alam et al., 2021a): This dataset contains 77k labeled tweets, which are sampled from 24 million tweets collected during 19 disasters between 2016 and 2019, including hurricanes, earthquakes, wildfires, and floods. It is balanced in terms of disaster types and contains 7 humanitarian categories.

• EPIC: This dataset contains data primarily collected from Hurricane Sandy, including tweets from 93 users across four annotation schemes, with data spanning three weeks around the hurricane’s landfall. It is used for relevance and humanitarian classification.

B.2 Estimation Datasets

• Did You Feel It (DYFI) (Atkinson and Wald, 2007): This dataset includes ground shaking intensity and geographic distribution information, collected from post-earthquake reports through 750,000 online questionnaire responses from individuals who experienced the event.

• FloodDepth (Akinboyewa et al., 2024): This dataset consists of 150 flood photos collected online, used to estimate floodwater depth based on various reference objects, including stop signs, vehicles, and humans.

• Behavioral Facilitation (BF) (Yamamoto et al., 2022): This dataset, collected after the 2018 Hokkaido earthquake, includes data labeled with usefulness ratings based on behavioral facilitation information.

B.3 Extraction Datasets

• (Fu et al., 2024): This dataset contains county-level data from news media collected during urban flood events from 2000 to 2022. It is utilized to extract information such as the time and location of disasters.

• (Ma et al., 2023): This dataset is designed for entity and relation extraction, comprising 5,560 annotated instances, 12,980 entities, and 6,895 relations derived from reports on geological hazards.

• DisasterMM (Andreadis et al., 2022): This dataset was collected from Twitter by searching for flood-related keywords. It consists of two subsets: RCTP, which includes 6,672 tweets for relevance classification, and LETT, which contains 4,992 tweets used for location extraction. In the LETT subset, words are annotated with "B-LOC" for the first word in a sequence referring to a location, "I-LOC" for subsequent words within the same location sequence, and "O" for words that do not correspond to a location.

• (Suwaileh et al., 2022): This dataset contains 22,000 crisis-related tweets from various disasters, including floods, earthquakes, and hurricanes. It is annotated with location-related tags such as "inLOC" and "outLOC."

• Re’SOCIO (Caillaut et al., 2024): This dataset is constructed by merging Wikipedia datasets and multiple disaster-related datasets, annotated with a set of 9 NER labels with different types of information.
• (Nguyen and Rudra, 2022a): This dataset contains tweet data with annotated rationales from 4 subsets of CrisisNLP. It is used for rationale extraction, and the extracted rationales can assist in disaster classification.

B.4 Generation Datasets

• (Vitiugin and Castillo, 2022): This dataset is used to generate summaries of various disaster events, with the official report of each event serving as the ground truth.

• CrisisFACTS (McCreadie and Buntain, 2023): This dataset is a multi-stream collection comprising data from eight crisis events gathered across various platforms. It is designed to process daily multi-platform streams and generate summaries based on specific information needs, such as "Have airports closed?"

• DisasterQA (Rawat, 2024): This dataset includes disaster-related multiple choice questions from 7 different sources, examples could be "What causes a tsunami?".

• FFD-IQA (Sun et al., 2023): This dataset comprises 2,058 images and 22,422 question-meta ground truth pairs related to the safety of individuals trapped in disaster sites and the availability of emergency services. It includes three types of questions: free-form, multiple-choice, and yes-no questions.

• FloodNet (Rahnemoonfar et al., 2021): This dataset consists of 4,500 question-image pairs collected after Hurricane Harvey. The questions pertain to buildings, roads, and entire scenes, categorized into four groups: "Simple Counting," "Complex Counting," "Yes/No," and "Condition Recognition."
Table 1: Summary of LLMs in disaster management with their disaster phases, application scenarios, specific tasks, and architecture types. "Arch": Type of LLM architectures used; "NM": Whether the paper presents novel methods.

<table>
  <tr>
    <th>Paper</th>
    <th>Phase</th>
    <th>Application</th>
    <th>Task</th>
    <th>Arch</th>
    <th>NM</th>
  </tr>
  <tr>
    <td>(Chowdhury et al., 2024)</td>
    <td>Mitigation</td>
    <td>Vulnerability Assessment</td>
    <td>Vulnerability Classification</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Martelo and Wang, 2024)</td>
    <td>Mitigation</td>
    <td>Vulnerability Assessment</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Fu et al., 2024)</td>
    <td>Preparedness</td>
    <td>Public Awareness Enhancement</td>
    <td>Knowledge Extraction</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Zhang and Wang, 2023)</td>
    <td>Preparedness</td>
    <td>Public Awareness Enhancement</td>
    <td>Knowledge Extraction</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Ma et al., 2023)</td>
    <td>Preparedness</td>
    <td>Public Awareness Enhancement</td>
    <td>Knowledge Extraction</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Wu et al., 2024)</td>
    <td>Preparedness</td>
    <td>Public Awareness Enhancement</td>
    <td>Knowledge Extraction</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Hostetter et al., 2024)</td>
    <td>Preparedness</td>
    <td>Public Awareness Enhancement</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Martelo and Wang, 2024)</td>
    <td>Preparedness</td>
    <td>Public Awareness Enhancement</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Li et al., 2023)</td>
    <td>Preparedness</td>
    <td>Public Awareness Enhancement</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Indra and Duraiapandian, 2023)</td>
    <td>Preparedness</td>
    <td>Disaster Forecast</td>
    <td>Occurrence Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Zeng and Bertsimas, 2023)</td>
    <td>Preparedness</td>
    <td>Disaster Forecast</td>
    <td>Occurrence Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Liu and Zhong, 2023)</td>
    <td>Preparedness</td>
    <td>Disaster Forecast</td>
    <td>Occurrence Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Wang et al., 2024)</td>
    <td>Preparedness</td>
    <td>Disaster Forecast</td>
    <td>Occurrence Classification</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Chandra et al., 2024)</td>
    <td>Preparedness</td>
    <td>Disaster Warning</td>
    <td>Warning Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Martelo and Wang, 2024)</td>
    <td>Preparedness</td>
    <td>Disaster Warning</td>
    <td>Warning Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Lubin et al., 2024)</td>
    <td>Preparedness</td>
    <td>Disaster Warning</td>
    <td>Image Generation</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Hostetter et al., 2024)</td>
    <td>Preparedness</td>
    <td>Evacuation Planning</td>
    <td>Plan Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Ningsih and Hadiana, 2021)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Madichetty and Madisetty, 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Singh et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Powers et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Duraisamy and Natarajan, 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Ullah et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Li and Chen, 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Zhao et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Karanjit et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Pabari et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(de Bruijn et al., 2019)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Zhao et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Wang et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Habib et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Liu et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Fontalis et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Mehmood et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Paul et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Lamsal et al., 2024a)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Manthena, 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Danday and Murthy, 2022)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Ghosh et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Taghian Dinani et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Kamoji et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Madichetty et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Koshy and Elango, 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Shetty et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Zhou et al., 2023b)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Yu and Wang, 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Zhang et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Kota et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Wang and Wang, 2022)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Hanif et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
</table>
<table>
  <tr>
    <th>Paper</th>
    <th>Phase</th>
    <th>Application</th>
    <th>Task</th>
    <th>Arch</th>
    <th>NM</th>
  </tr>
  <tr>
    <td>(Jang et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Identification</td>
    <td>Relevance Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Madichetty and Sridevi, 2021)</td>
    <td>Response</td>
    <td>Disaster Situation Assessment</td>
    <td>Situation Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Raj et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Situation Assessment</td>
    <td>Situation Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Kanth et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Situation Assessment</td>
    <td>Situation Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Mousavi et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Situation Assessment</td>
    <td>Severity Estimation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Akinboyea et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Situation Assessment</td>
    <td>Severity Estimation</td>
    <td>Multimodal</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Hu and Rahemoonfar, 2024)</td>
    <td>Response</td>
    <td>Disaster Situation Assessment</td>
    <td>Description Generation</td>
    <td>Multimodal</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Wolf et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Situation Assessment</td>
    <td>Description Generation</td>
    <td>Multimodal</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Yamamoto et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Usefulness Estimation</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Blomeier et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Relevance Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Adesokan et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Wahid et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Chandrakala and Raj, 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Naaz et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Du et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Adesokan et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Han et al., 2024b)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Sharma et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Yuan et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Liu et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Boros et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Li et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Zou et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Zahera et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Wilkho et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Nguyen and Rudra, 2022b)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Zou et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Nguyen and Rudra, 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Nguyen and Rudra, 2022a)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Dar et al., 2025)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Otal and Canbaz, 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Yin et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Dinani et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Zhang et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Yu and Wang, 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Shetty et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Abavisani et al., 2020)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Zhou et al., 2023a)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Basit et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Information Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Yang et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Need Classification</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Toraman et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Need Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Zhou et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Need Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Vitiugin and Purohit, 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Need Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Conneau, 2019)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Need Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Lamsal et al., 2024b)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Need Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Mehmood et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Suwaiheh et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Koshy and Elango, 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Ma et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Zhang et al., 2021)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Caillaud et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Yu and Wang, 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Hu et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Firmansyah et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Location Extraction</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Nguyen and Rudra, 2022a)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Summary Extraction</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Nguyen et al., 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Summary Extraction</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
</table>
<table>
  <tr>
    <th>Paper</th>
    <th>Phase</th>
    <th>Application</th>
    <th>Task</th>
    <th>Arch</th>
    <th>NM</th>
  </tr>
  <tr>
    <td>(Garg et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Summary Extraction</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Vitiugin and Castillo, 2022)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Summary Extraction</td>
    <td>Decoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Colverd et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Report Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Pereira et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Report Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Seeberger and Riedhammer, 2024b)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Report Generation</td>
    <td>Decoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Seeberger and Riedhammer, 2024a)</td>
    <td>Response</td>
    <td>Disaster Information Coordination</td>
    <td>Report Generation</td>
    <td>Decoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Goeks and Waytowich, 2023)</td>
    <td>Response</td>
    <td>Disaster Rescuing</td>
    <td>Plan Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Panagopoulos et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Rescuing</td>
    <td>Code Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Rawat, 2024)</td>
    <td>Response</td>
    <td>Disaster Issue Consultation</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Chen and Fang, 2024)</td>
    <td>Response</td>
    <td>Disaster Issue Consultation</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Xie et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Issue Consultation</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Chen et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Issue Consultation</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Xia et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Issue Consultation</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Sun et al., 2023)</td>
    <td>Response</td>
    <td>Disaster Issue Consultation</td>
    <td>Answer Generation</td>
    <td>Multimodal</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Liu et al., 2024)</td>
    <td>Response</td>
    <td>Disaster Issue Consultation</td>
    <td>Answer Generation</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Kumbum and Vejre, 2024)</td>
    <td>Response</td>
    <td>Disaster Issue Consultation</td>
    <td>Answer Generation</td>
    <td>Multimodal</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Malik et al., 2024)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Damage Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Chen and Lim, 2021)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Damage Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Jeba et al., 2024)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Damage Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Zou et al., 2024)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Damage Classification</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Chen and Lim, 2021)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Damage Estimation</td>
    <td>Encoder</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>(Ziaullah et al., 2024)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Answer Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Estêvão, 2024)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Answer Generation</td>
    <td>Multimodal</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Sarkar et al., 2023)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Answer Generation</td>
    <td>Multimodal</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Alsan and Arsan, 2023)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Answer Generation</td>
    <td>Multimodal</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Hou and Xu, 2022)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Statistic Extraction</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Han et al., 2024a)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Sentiment Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Alharm and Naim)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Sentiment Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Zhang and Ma, 2023)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Sentiment Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Varghese et al., 2024)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Sentiment Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Berbère et al., 2023)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Sentiment Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Li et al., 2025)</td>
    <td>Recovery</td>
    <td>Disaster Impact Assessment</td>
    <td>Sentiment Classification</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(White and Liptak, 2024)</td>
    <td>Recovery</td>
    <td>Recovery Plan Generation</td>
    <td>Plan Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(Lakhera, 2024)</td>
    <td>Recovery</td>
    <td>Recovery Plan Generation</td>
    <td>Plan Generation</td>
    <td>Decoder</td>
    <td>No</td>
  </tr>
  <tr>
    <td>(CONTRERAS et al.)</td>
    <td>Recovery</td>
    <td>Recovery Process Tracking</td>
    <td>Sentiment Classification</td>
    <td>Encoder</td>
    <td>No</td>
  </tr>
</table>

Table 2: Summary of publicly available datasets utilized in disaster management. For Application, "DI": Disaster Identification; "DInf": Disaster Information Coordination; "DIC": Disaster Issue Consultation; "DSA": Disaster Situation Assessment; "PAE": Public Awareness Enhancement; "DIA": Disaster Impact Assessment. For Disaster Type, "Mix" denotes the datasets contain various types of disasters.

<table>
  <tr>
    <th>Dataset</th>
    <th>Phase</th>
    <th>Application</th>
    <th>Task</th>
    <th>Disaster Type</th>
    <th>Modality</th>
    <th>Used in</th>
    <th>#Sample</th>
  </tr>
  <tr>
    <td>CrisisLexT6 (Olteanu et al., 2014)</td>
    <td>Response</td>
    <td>DI</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(McDaniel et al., 2024)</td>
    <td>60,082</td>
  </tr>
  <tr>
    <td>CrisisLexT26 (Olteanu et al., 2015)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(McDaniel et al., 2024)</td>
    <td>27,933</td>
  </tr>
  <tr>
    <td>CrisisNLP (Imran et al., 2016)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Taghian Dinani et al., 2023)</td>
    <td>52,656</td>
  </tr>
  <tr>
    <td>SWDM13 (Imran et al., 2013)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(McDaniel et al., 2024)</td>
    <td>1,543</td>
  </tr>
  <tr>
    <td>ISCRAM2013 (Imran et al., 2013)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(McDaniel et al., 2024)</td>
    <td>3,617</td>
  </tr>
  <tr>
    <td>DRD (Alam et al., 2021b)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(McDaniel et al., 2024)</td>
    <td>26,235</td>
  </tr>
</table>
<table>
  <tr>
    <th>Dataset</th>
    <th>Phase</th>
    <th>Application</th>
    <th>Task</th>
    <th>Disaster Type</th>
    <th>Modality</th>
    <th>Used in</th>
    <th>#Sample</th>
  </tr>
  <tr>
    <td>DSM (Alam et al., 2021b)</td>
    <td>Response</td>
    <td>DI</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(McDaniel et al., 2024)</td>
    <td>10,876</td>
  </tr>
  <tr>
    <td>AIDR (Imran et al., 2014)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(McDaniel et al., 2024)</td>
    <td>7,411</td>
  </tr>
  <tr>
    <td>CrisisMMID (Alam et al., 2018)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text, Image</td>
    <td>(Jain et al., 2024)</td>
    <td>16,058</td>
  </tr>
  <tr>
    <td>Multi-Crisis (Sánchez et al., 2023)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Sánchez et al., 2023)</td>
    <td>164,625</td>
  </tr>
  <tr>
    <td>CrisisBench (Alam et al., 2021b)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(McDaniel et al., 2024)</td>
    <td>109,796</td>
  </tr>
  <tr>
    <td>Eyewitness Messages (Zahra et al., 2020)</td>
    <td>Response</td>
    <td>DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Zahra et al., 2020)</td>
    <td>14,000</td>
  </tr>
  <tr>
    <td>TREC Incident Streams (McCredie et al., 2019)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Khattar and Quadri, 2022)</td>
    <td>19,784</td>
  </tr>
  <tr>
    <td>HumAID (Alam et al., 2021a)</td>
    <td>Response</td>
    <td>DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Basit et al., 2023)</td>
    <td>77,000</td>
  </tr>
  <tr>
    <td>EPIC (Stowe et al., 2018)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Adesokan et al., 2023)</td>
    <td>3469</td>
  </tr>
  <tr>
    <td>Did You Feel It (DYFI) (Mousavi et al., 2024)</td>
    <td>Response</td>
    <td>DSA</td>
    <td>Estimation</td>
    <td>Earthquake</td>
    <td>Text</td>
    <td>(Mousavi et al., 2024)</td>
    <td>750,000</td>
  </tr>
  <tr>
    <td>FloodDepth (Akinboyewa et al., 2024)</td>
    <td>Response</td>
    <td>DSA</td>
    <td>Estimation</td>
    <td>Flood</td>
    <td>Text, Image</td>
    <td>(Akinboyewa et al., 2024)</td>
    <td>150</td>
  </tr>
  <tr>
    <td>Behavioral Facilitation (BF) (Yamamoto et al., 2022)</td>
    <td>Response</td>
    <td>DInf</td>
    <td>Estimation</td>
    <td>Earthquake</td>
    <td>Text</td>
    <td>(Yamamoto et al., 2022)</td>
    <td>1,400</td>
  </tr>
  <tr>
    <td>(Fu et al., 2024)</td>
    <td>Preparedness</td>
    <td>PAE</td>
    <td>Extraction</td>
    <td>Flood</td>
    <td>Text</td>
    <td>(Fu et al., 2024)</td>
    <td>633</td>
  </tr>
  <tr>
    <td>(Ma et al., 2023)</td>
    <td>Preparedness</td>
    <td>PAE</td>
    <td>Extraction</td>
    <td>Landslide</td>
    <td>Text</td>
    <td>(Ma et al., 2023)</td>
    <td>5,560</td>
  </tr>
  <tr>
    <td>DisasterMM (Andreadis et al., 2022)</td>
    <td>Response</td>
    <td>DI, DInf</td>
    <td>Classification, Extraction</td>
    <td>Flood</td>
    <td>Text</td>
    <td>(Mehmood et al., 2024)</td>
    <td>6,672</td>
  </tr>
  <tr>
    <td>(Suwaileh et al., 2022)</td>
    <td>Response</td>
    <td>DInf</td>
    <td>Extraction</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Suwaileh et al., 2022)</td>
    <td>22,137</td>
  </tr>
  <tr>
    <td>Re’ SoCIO (Caillaut et al., 2024)</td>
    <td>Response</td>
    <td>DInf</td>
    <td>Extraction</td>
    <td>Flood</td>
    <td>Text</td>
    <td>(Caillaut et al., 2024)</td>
    <td>4,617</td>
  </tr>
  <tr>
    <td>(Nguyen and Rudra, 2022a)</td>
    <td>Response</td>
    <td>DInf</td>
    <td>Extraction</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Nguyen and Rudra, 2022a)</td>
    <td>32</td>
  </tr>
  <tr>
    <td>(Vitiugin and Castillo, 2022)</td>
    <td>Response</td>
    <td>DInf</td>
    <td>Generation</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Vitiugin and Castillo, 2022)</td>
    <td>5,791</td>
  </tr>
  <tr>
    <td>CrisisFACTS (McCredie and Buntain, 2023)</td>
    <td>Response</td>
    <td>DIC</td>
    <td>Generation</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Pereira et al., 2023)</td>
    <td>748,466</td>
  </tr>
  <tr>
    <td>DisasterQA (Rawat, 2024)</td>
    <td>Response</td>
    <td>PAE, DIC</td>
    <td>Generation</td>
    <td>Mix</td>
    <td>Text</td>
    <td>(Rawat, 2024)</td>
    <td>707</td>
  </tr>
  <tr>
    <td>FFD-IQA (Sun et al., 2023)</td>
    <td>Response</td>
    <td>DIC</td>
    <td>Generation</td>
    <td>Flood</td>
    <td>Text, Image</td>
    <td>(Sun et al., 2023)</td>
    <td>22,422</td>
  </tr>
  <tr>
    <td>FloodNet (Rahnamoofar et al., 2021)</td>
    <td>Recovery</td>
    <td>DIA</td>
    <td>Generation</td>
    <td>Flood</td>
    <td>Text, Image</td>
    <td>(Sarkar et al., 2023)</td>
    <td>4,500</td>
  </tr>
</table>

Reviewer paper summaries: The paper surveys works using LLMs (pre-trained language models like BERT included) for disaster management application. It groups different works based on application categories and also put together a list of relatively easily accessible datasets.

This paper provides a survey on the use of large language models (LLMs) in disaster management. It introduces a comprehensive taxonomy to categorize related works based on disaster phases and application scenarios. Additionally, the paper offers an in-depth overview of the existing datasets relevant to disaster management.

**Comments after Author Discussion**

I thank the authors for their responses to my comments. After reading the responses and other reviews, I have decided to keep the scores unchanged.
Summary of strengths: * The listing of easy-accessible disaster-related datasets and salient works under different application categories and methodologies, all in one palce, can be very helpful for the community.

* The paper is well-written, well-motivated, and provides a clear focus on the topic.
* It introduces a detailed and useful taxonomy for categorizing works based on disaster phases and application scenarios.
* The paper also highlights the model architectures employed for tasks across different disaster phases.
Summary of weaknesses: Beyond the positive of grouping together different works, there does not seem to be much else that is particularly innovative - for example, it terms of identifying some key blockers, new insights, or suggesting new future works. The key challenges identified are somewhat generic and may not come as surprising or insightful to anyone working in the field.

Tackles a lot of papers with one-two lines describing them vaguely. So the main content of the paper can feel a bit shallow - going over multiple papers with some one-two line details.

* The paper feels verbose. Incorporating more figures or tables could enhance clarity and user engagement.
* While the taxonomy effectively organizes related works, the paper largely lists them without critically analyzing their strengths and limitations. A discussion on the pros and cons of existing approaches would have added depth.
Comments, suggestions and typos: n/a

* Consider relocating some tables or figures (or parts of them) from the Appendix to the main body to improve readability. Additional pages in the final version could be used for this purpose.
Overall assessment scores: 3.5

3.5
Metareview score: 3
Metareview: This survey paper provides an overview on over 150 papers using LLMs (including BERTs) for disaster management. The survey introduces a taxonomy to categorize related work based on the disaster phases and application scenarios, and then discusses a list of related datasets.
Summary of reasons to publish: 1. The survey introduces a useful taxonomy for LLM-based disaster management which help the community understand the field in one center place. 
2. The discussion on the related datasets is also useful to tell how the different application scenarios look like.