A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation

• URL: http://arxiv.org/abs/2506.03360v1
• Date: Tue, 03 Jun 2025 20:07:25 GMT
• Title: A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation
• Authors: Zihui Ma, Lingyao Li, Juan Li, Wenyue Hua, Jingxiao Liu, Qingyuan Feng, Yuki Miura,
• Abstract summary: Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors and delays in official reporting.<n>Social media provides a rich, real-time source of human-centric observations, but its multimodal and unstructured nature presents challenges for traditional analytical methods.<n>We propose a structured Multimodal, Multilingual, and Multidimensional (3M) pipeline that leverages multimodal large language models (MLLMs) to assess disaster impacts.
• Score: 5.5809992003597575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors and delays in official reporting. Social media provides a rich, real-time source of human-centric observations, but its multimodal and unstructured nature presents challenges for traditional analytical methods. In this study, we propose a structured Multimodal, Multilingual, and Multidimensional (3M) pipeline that leverages multimodal large language models (MLLMs) to assess disaster impacts. We evaluate three foundation models across two major earthquake events using both macro- and micro-level analyses. Results show that MLLMs effectively integrate image-text signals and demonstrate a strong correlation with ground-truth seismic data. However, performance varies with language, epicentral distance, and input modality. This work highlights the potential of MLLMs for disaster assessment and provides a foundation for future research in applying MLLMs to real-time crisis contexts. The code and data are released at: https://github.com/missa7481/EMNLP25_earthquake

Related papers

• Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning [71.3533541927459]
  We propose a novel data selection paradigm termed Activation Reasoning Potential (RAP)<n>RAP identifies cognitive samples by estimating each sample's potential to stimulate genuine multi-modal reasoning.<n>Our RAP method consistently achieves superior performance using only 9.3% of the training data, while reducing computational costs by over 43%.
  arXiv  Detail & Related papers  (2025-06-05T08:40:24Z)
• LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment [6.787695140978638]
  This study examines multiple large language models (LLMs) to estimate perceived earthquake impacts.<n>Our framework generates Modified Mercalli Intensity (MMI) predictions at zip code and county scales.<n> Evaluations on the 2014 Napa and 2019 Ridgecrest earthquakes using USGS ''Did You Feel It? (DYFI)'' reports demonstrate significant alignment.
  arXiv  Detail & Related papers  (2025-06-02T22:07:53Z)
• MLLMs are Deeply Affected by Modality Bias [158.64371871084478]
  Recent advances in Multimodal Large Language Models (MLLMs) have shown promising results in integrating diverse modalities such as texts and images.<n>MLLMs are heavily influenced by modality bias, often relying on language while under-utilizing other modalities like visual inputs.<n>This paper argues that MLLMs are deeply affected by modality bias, highlighting its manifestations across various tasks.
  arXiv  Detail & Related papers  (2025-05-24T11:49:31Z)
• MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for Red-Team Jailbreak Attacks [85.3303135160762]
  MIRAGE is a novel framework that exploits narrative-driven context and role immersion to circumvent safety mechanisms in Multimodal Large Language Models.<n>It achieves state-of-the-art performance, improving attack success rates by up to 17.5% over the best baselines.<n>We demonstrate that role immersion and structured semantic reconstruction can activate inherent model biases, facilitating the model's spontaneous violation of ethical safeguards.
  arXiv  Detail & Related papers  (2025-03-24T20:38:42Z)
• Multi-Lingual Cyber Threat Detection in Tweets/X Using ML, DL, and LLM: A Comparative Analysis [0.0]
  Cyber threat detection has become an important area of focus in today's digital age.<n>This study focuses on multi-lingual tweet cyber threat detection using a variety of advanced models.<n>We collected and labeled tweet datasets in four languages English, Chinese, Russian, and Arabic.
  arXiv  Detail & Related papers  (2025-02-04T03:46:24Z)
• Harnessing Large Language Models for Disaster Management: A Survey [57.00123968209682]
  Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields.<n>This study aims to guide the professional community in developing advanced LLMs for disaster management to enhance the resilience against natural disasters.
  arXiv  Detail & Related papers  (2025-01-12T21:00:50Z)
• Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses [76.59021017301127]
  We propose a large-scale traffic crash language dataset, named CrashEvent, summarizing 19,340 real-world crash reports. We further formulate the crash event feature learning as a novel text reasoning problem and further fine-tune various large language models (LLMs) to predict detailed accident outcomes. Our experiments results show that our LLM-based approach not only predicts the severity of accidents but also classifies different types of accidents and predicts injury outcomes.
  arXiv  Detail & Related papers  (2024-06-16T03:10:16Z)
• Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models [8.17728833322492]
  Critical Infrastructure Facilities (CIFs) are vital for the functioning of a community, especially during large-scale emergencies. In this paper, we explore a potential application of Large Language Models (LLMs) to monitor the status of CIFs affected by natural disasters through information disseminated in social media networks. We analyze social media data from two disaster events in two different countries to identify reported impacts to CIFs as well as their impact severity and operational status.
  arXiv  Detail & Related papers  (2024-04-18T19:41:05Z)
• Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models [5.031939163610801]
  We introduce an end-to-end framework to significantly improve the timeliness and accuracy of global earthquake-induced loss forecasting. Our framework integrates a hierarchical casualty extraction model built upon large language models, prompt design, and few-shot learning. We test the framework in real-time on a series of global earthquake events in 2022 and 2022 and show that our framework streamlines casualty data retrieval, achieving speed and accuracy comparable to manual methods by 2021.
  arXiv  Detail & Related papers  (2023-12-04T17:09:58Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)
• A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction [0.0]
  This paper proposes a novel prediction method based on attention mechanism (AM), convolution neural network (CNN), and bi-directional long short-term memory (BiLSTM) models. It can predict the number and maximum magnitude of earthquakes in each area of mainland China-based on the earthquake catalog of the region.
  arXiv  Detail & Related papers  (2021-12-26T20:16:20Z)
• Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping [17.324397643429638]
  We have developed a global multisensor and multitemporal dataset for building damage mapping. The global dataset contains high-resolution optical imagery and high-to-moderate-resolution multiband SAR data. We defined a damage mapping framework for the semantic segmentation of damaged buildings based on a deep convolutional neural network algorithm.
  arXiv  Detail & Related papers  (2020-09-14T05:04:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.