mlsanityData to 13 Oct 2026

Harnessing Large Language Models for Disaster Management: A Survey

arXiv 2501.06932 ↗First posted 12 Jan 2025cs.CL cs.CY cs.LGcited by 20

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Abstract (arXiv)

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

Key facts. Cited by 20 papers in the map (data through 13 Oct 2026). Builds on 50 of its 183 references that are in the map. First posted on arXiv 12 Jan 2025.

Builds on

50 of 183 references.

References · 50

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  2. 2020Language Models are Few-Shot Learnerscited by 32,320
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38 more
  1. 2020Multimodal Categorization of Crisis Events in Social Mediacited by 34
  2. 2023DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarioscited by 34
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  7. 2020Detecting natural disasters, damage, and incidents in the wildcited by 23
  8. 2024LLM-Assisted Crisis Management: Building Advanced LLM Platforms for Effective Emergency Response and Public Collaborationcited by 21
  9. 2024WildfireGPT: Tailored Large Language Model for Wildfire Analysiscited by 15
  10. 2024A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies: a benchmark dataset and methodcited by 12
  11. 2024RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agentscited by 11
  12. 2023DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bankcited by 10
  13. 2023Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenariocited by 10
  14. 2023FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLMcited by 9
  15. 2024DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Responsecited by 9
  16. 2020I-AID: Identifying Actionable Information from Disaster-related Tweetscited by 6
  17. 2024Decision support system for Forest fire management using Ontology with Big Data and LLMscited by 6
  18. 2024Selective Exploration and Information Gathering in Search and Rescue Using Hierarchical Learning Guided by Natural Language Inputcited by 6
  19. 2021Transformer-based Multi-task Learning for Disaster Tweet Categorisationcited by 5
  20. 2022Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classificationcited by 5
  21. 2024Large Language Models in Fire Engineering: An Examination of Technical Questions Against Domain Knowledgecited by 5
  22. 2022NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasterscited by 4
  23. 2024Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Responsecited by 4
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  35. 2023AI-assisted Protective Action: Study of ChatGPT as an Information Source for a Population Facing Climate Hazardscited by 1
  36. 2023Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagerycited by 1
  37. 2023Flood Event Extraction from News Media to Support Satellite-Based Flood Insurancecited by 1
  38. 2024Multi-Query Focused Disaster Summarization via Instruction-Based Promptingcited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 10; 2026: 10.

20 papers.

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  1. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 24
  2. 2025From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMscited by 12
  3. 2025Learning from Diverse Reasoning Paths with Routing and Collaborationcited by 12
  4. 2025A Survey of Scaling in Large Language Model Reasoningcited by 10
  5. 2025Generative AI for Autonomous Driving: Frontiers and Opportunitiescited by 7
  6. 2025DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Managementcited by 3
  7. 2025A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluationcited by 3
  8. 2025CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operationcited by 3
  9. 2025From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learningcited by 1
  10. 2026Large Language Models for Geolocation Extraction in Humanitarian Crisis Responsecited by 1
  11. 2026RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagerycited by 1
  12. 2025A Dynamic Fusion Model for Consistent Crisis Responsecited by 0
8 more
  1. 2026LLM-Powered Automatic Translation and Urgency in Crisis Scenarioscited by 0
  2. 2026Design and evaluation of an agentic workflow for crisis-related synthetic tweet datasetscited by 0
  3. 2026Leveraging LLMs and Social Media to Understand User Perception of Smartphone-Based Earthquake Early Warningscited by 0
  4. 2026LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classificationcited by 0
  5. 2026RAPIDMap: Rapid Multi-Agent Pipeline for Interpretable Disaster Mapping from Satellite and Street-view Imagerycited by 0
  6. 2026Verifiable Disaster Storylines and Causal Knowledge Graphs: A Citation-Grounded Pipeline from Heterogeneous Humanitarian Sourcescited by 0
  7. 2026Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoningcited by 0
  8. 2026From UNDRR Reports to Event Records: Schema-Constrained LLM Extraction of Georeferenced Disasterscited by 0