Harnessing Large Language Models for Disaster Management: A Survey

TL;DR

This survey reviews how large language models are applied in natural disaster management, categorizing existing works, datasets, challenges, and opportunities to guide future development in enhancing disaster resilience.

Contribution

It provides the first comprehensive taxonomy and systematic analysis of LLM applications in disaster management, addressing a significant research gap.

Findings

Developed a taxonomy categorizing LLM applications by disaster phases and scenarios

Compiled public datasets used in disaster LLM research

Identified key challenges and future opportunities in the field

Abstract

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.