Large vision-language models (VLMs) have made great achievements in Earth vision. However, complex disaster scenes with diverse disaster types, geographic regions, and satellite sensors have posed new challenges for VLM applications. To fill this gap, we curate a remote sensing vision-language dataset (DisasterM3) for global-scale disaster assessment and response. DisasterM3 includes 26,988 bi-temporal satellite images and 123k instruction pairs across 5 continents, with three characteristics: 1) Multi-hazard: DisasterM3 involves 36 historical disaster events with significant impacts, which are categorized into 10 common natural and man-made disasters. 2)Multi-sensor: Extreme weather during disasters often hinders optical sensor imaging, making it necessary to combine Synthetic Aperture Radar (SAR) imagery for post-disaster scenes. 3) Multi-task: Based on real-world scenarios, DisasterM3 includes 9 disaster-related visual perception and reasoning tasks, harnessing the full potential of VLM's reasoning ability with progressing from disaster-bearing body recognition to structural damage assessment and object relational reasoning, culminating in the generation of long-form disaster re
Disasters instances have been increasing both in frequency and intensity causing the tragic loss of life and making life harder for the survivors. Disaster relief management plays a crucial role in enhancing the lifestyle of disaster victims by managing the disaster impacts. Disaster relief management is a process with many collaborative sectors where different stakeholders should operate in all major phases of the disaster management progression. In the different phases of the disaster management process, many collaborative government organisations along with nongovernment organisations, leadership, community, and media at different levels need to share the responsibility with disaster victims to achieve effective disaster relief management. Shared responsibility enhances disaster relief management effectiveness and reduces the disaster's impact on the victims. Considering the diverse roles of different stakeholders, there has been a need for a framework that can bind different stakeholders together during disaster management. this paper shows a framework with major stakeholders of disaster relief management and how different stakeholders can take part in an effective disaster rel
Recent natural disasters have highlighted the urgent need for efficient data-driven approaches to disaster management. Machine learning (ML) and deep learning (DL) techniques have shown considerable promise in enhancing the key phases of disaster management including mitigation, preparedness, detection, response, and recovery. A critical enabler of successful ML or DL based applications in remote sensing, however, is the accessibility and quality of annotated datasets. With the growing availability of high-resolution imagery from unmanned aerial vehicles (UAVs) and satellites, computer vision and remote sensing algorithms have become essential tools for rapid detection, situational assessment, and decision-making in disaster scenarios. This survey provides a comprehensive overview of publicly available image-based datasets relevant to ML/DL-based disaster management pipelines. Emphasis is placed on datasets that support computer vision and remote sensing tasks across all phases of disaster events including pre-disaster, during, and post-disaster. The goal of this work is to serve as a centralized reference for researchers and practitioners seeking high-quality datasets for rapid de
Human mobility generation in disaster scenarios plays a vital role in resource allocation, emergency response, and rescue coordination. During disasters such as wildfires and hurricanes, human mobility patterns often deviate from their normal states, which makes the task more challenging. However, existing works usually rely on limited data from a single city or specific disaster, significantly restricting the model's generalization capability in new scenarios. In fact, disasters are highly sudden and unpredictable, and any city may encounter new types of disasters without prior experience. Therefore, we aim to develop a one-for-all model for mobility generation that can generalize to new disaster scenarios. However, building a universal framework faces two key challenges: 1) the diversity of disaster types and 2) the heterogeneity among different cities. In this work, we propose a unified model for human mobility generation in natural disasters (named UniDisMob). To enable cross-disaster generalization, we design physics-informed prompt and physics-guided alignment that leverage the underlying common patterns in mobility changes after different disasters to guide the generation pr
Natural disasters such as earthquakes, torrential rainfall, floods, and volcanic eruptions occur with extremely low frequency and affect limited geographic areas. When individuals face disaster situations, they often experience confusion and lack the domain-specific knowledge and experience necessary to determine appropriate responses and actions. While disaster information is continuously updated, even when utilizing RAG search and large language models for inquiries, obtaining relevant domain knowledge about natural disasters and experiences similar to one's specific situation is not guaranteed. When hallucinations are included in disaster question answering, artificial misinformation may spread and exacerbate confusion. This work introduces a disaster-focused question answering system based on Japanese disaster situations and response experiences. Utilizing the cl-tohoku/bert-base-japanese-v3 + Bi-LSTM + Enhanced Position Heads architecture with LoRA efficiency optimization, we achieved 70.4\% End Position accuracy with only 5.7\% of the total parameters (6.7M/117M). Experimental results demonstrate that the combination of Japanese BERT-base optimization and Bi-LSTM contextual u
Operational disaster response goes beyond damage assessment, requiring responders to integrate multi-sensor signals, reason over road networks, populations and key facilities, plan evacuations, and produce actionable reports. However, prior work largely isolates remote-sensing perception or evaluates generic tool use, leaving the end-to-end workflows of emergency operations underexplored. In this paper, we introduce Disaster Operational Response Agent benchmark (DORA), the first agentic benchmark for end-to-end disaster response: 515 expert-authored tasks across 45 real-world disaster events spanning 10 types, paired with expert-verified, replayable gold trajectories totaling 3,500 tool-call steps. Tasks span five dimensions that cover the operational disaster-response pipeline: disaster perception, spatial relational analysis, rescue and evacuation planning, temporal evolution reasoning, and multi-modal report synthesis. Agents compose calls from a 108-tool MCP library over heterogeneous geospatial data: optical, SAR, and multi-spectral imagery across single-, bi-, and multi-temporal sequences (0.015-10m GSD), complemented by elevation and social vector layers. We comprehensively
Disasters can result in the deaths of many, making quick response times vital. Large Language Models (LLMs) have emerged as valuable in the field. LLMs can be used to process vast amounts of textual information quickly providing situational context during a disaster. However, the question remains whether LLMs should be used for advice and decision making in a disaster. To evaluate the capabilities of LLMs in disaster response knowledge, we introduce a benchmark: DisasterQA created from six online sources. The benchmark covers a wide range of disaster response topics. We evaluated five LLMs each with four different prompting methods on our benchmark, measuring both accuracy and confidence levels through Logprobs. The results indicate that LLMs require improvement on disaster response knowledge. We hope that this benchmark pushes forth further development of LLMs in disaster response, ultimately enabling these models to work alongside. emergency managers in disasters.
Information graphics, such as hazard maps, evacuation diagrams, and pictorial action guides, are widely used in disaster risk communication. These visuals are important because they convey hazard information quickly, reduce reliance on lengthy text, and support decision-making in time-critical situations. However, despite their importance, disaster information graphics do not work equally well for all audiences. In practice, many graphics remain difficult to interpret, and their accessibility for vulnerable populations is still uneven and underexplored. Despite their central role, there has been little empirical work examining how graphics shape disaster communication, what challenges practitioners face in using them, and, most importantly, how inclusive current disaster graphics are in real-world settings. To address this gap, we examine how information graphics are currently produced and used in disaster communication, what issues emerge in practice, and how inclusivity is addressed. We conducted semi-structured interviews with disaster communication practitioners and researchers to examine the role of graphics across preparedness, warning, and response contexts, as well as the b
This study maps the functions of artificial intelligence in disaster (mis)management. It begins with a classification of disasters in terms of their causal parameters, introducing hypothetical cases of independent or hybrid AI-caused disasters. We then overview the role of AI in disaster management and mismanagement, where the latter includes possible ethical repercussions of the use of AI in intelligent disaster management (IDM), as well as ways to prevent or mitigate these issues, which include pre-design a priori, in-design, and post-design methods as well as regulations. We then discuss the governments role in preventing the ethical repercussions of AI use in IDM and identify and asses its deficits and challenges. This discussion is followed by an account of the advantages and disadvantages of pre-design or embedded ethics. Finally, we briefly consider the question of accountability and liability in AI-caused disasters.
This paper investigates the interactions among consumption/savings, investment, and retirement choices with income disaster. We consider low-income people who are exposed to income disaster so that they retire involuntarily when income disaster occurs. The government provides extra income support to low-income retirees who suffer from significant income gaps. We demonstrate that the decision to enter retirement in the event of income disaster depends crucially on the level of income support. In particular, we quantitatively identify a certain income support level below which the optimal decision is to delay retirement. This implies that availability of the government's extra income support can be particularly important for the low-income people to achieve optimal retirement with income disaster.
As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. This paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover from disaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters. We explore the essential technologies
Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. To bridge this gap, we introduce DisastIR, the first comprehensive IR evaluation benchmark specifically tailored for disaster management. DisastIR comprises 9,600 diverse user queries and more than 1.3 million labeled query-passage pairs, covering 48 distinct retrieval tasks derived from six search intents and eight general disaster categories that include 301 specific event types. Our evaluations of 30 state-of-the-art retrieval models demonstrate significant performance variances across tasks, with no single model excelling universally. Furthermore, comparative analyses reveal significant performance gaps between general-domain and disaster management-specific tasks, highlighting the necessity of disaster management-specific benchmarks for guiding IR model selection to support effective decision-making in disaster management
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.
Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recognition of disasters using machine learning, termed DisasterNets. It consists of two stages, space granulation and attribute granulation. The space granulation stage leverages supervised/semi-supervised learning, unsupervised change detection, and domain adaptation with/without source data techniques to handle different disaster mapping scenarios. Furthermore, the disaster database with the corresponding geographic information field properties is built by using the attribute granulation stage. The framework is applied to earthquake-triggered landslide mapping and large-scale flood mapping. The results demonstrate a competitive performance for high-precision, high-efficiency, and cross-scene recognition of disasters. To bridge the gap between disaster mapping and machine learning communities, we will provide an openly accessible tool based on DisasterNets. The framework and tool will be available at https://github.com/HydroPML/DisasterNets.
Climate hazards can escalate into humanitarian disasters. Understanding their trajectories -- considering hazard intensity, human exposure, and societal vulnerability -- is essential for effective anticipatory action. The International Disaster Database (EM-DAT) is the only freely available global resource of humanitarian disaster records. However, it lacks exact geospatial information, limiting its use for climate hazard impact research. Here, we provide geocoding of 9,217 climate-related disasters reported by EM-DAT from 1990 to 2023, along with an open, reproducible framework for updating. Our method remains accurate even when only region names are available and includes quality flags to assess reliability. The augmented EM-DAT enables integration with other geocoded data, supporting more accurate assessment of climate disaster impacts and adaptation deficits.
Natural disasters remain a major challenge for Bangladesh, so real-time monitoring and quick response systems are essential. In this study, we present BanglaMM-Disaster, an end-to-end deep learning-based multimodal framework for disaster classification in Bangla, using both textual and visual data from social media. We constructed a new dataset of 5,037 Bangla social media posts, each consisting of a caption and a corresponding image, annotated into one of nine disaster-related categories. The proposed model integrates transformer-based text encoders, including BanglaBERT, mBERT, and XLM-RoBERTa, with CNN backbones such as ResNet50, DenseNet169, and MobileNetV2, to process the two modalities. Using early fusion, the best model achieves 83.76% accuracy. This surpasses the best text-only baseline by 3.84% and the image-only baseline by 16.91%. Our analysis also shows reduced misclassification across all classes, with noticeable improvements for ambiguous examples. This work fills a key gap in Bangla multimodal disaster analysis and demonstrates the benefits of combining multiple data types for real-time disaster response in low-resource settings.
This paper is a follow-up to our earlier study, Natural Disasters in Canada (2017). We analyze the Canadian Disaster Database (CDD) to examine the frequency and severity of various natural disasters over the past 120 years and to identify emerging trends. We generate annual loss distributions for individual disaster types, as well as an aggregate annual loss distribution across all event types. Our analysis provides evidence that Canada is experiencing warmer and wetter conditions and indicates a substantial likelihood of extreme national-level losses.
Human beings have been affected by disasters from the beginning of life, bringing them many sad memories. In the long struggle against disaster, people have devised a variety of methods to train relevant participants in disaster relief capabilities. However, many traditional training methods, such as disaster exercises may not provide effective training to meet the need of today. Serious games provide an innovative approach to train participants in disaster relief, and a large number of Serious Games for Disaster Relief (SGDRs) have been developed to train disaster planning and rescue capabilities. At the same time, there is no systematics phase description for disaster relief, which cannot effectively guide participants' work and training in disaster relief. Therefore, this paper proposes a comprehensive and professional disaster relief classification framework according to different relief work in each stage of the disaster. Based on this framework, we review the functions and technologies of serious games in each classification, which can offer reliable guidance for researchers to better understand and use SGDRs. In addition, we analyze the serious games in each category, point
In the aftermath of disasters, many institutions worldwide face challenges in monitoring changes in disaster risk, limiting assessment of progress towards the UN Sendai Framework for Disaster Risk Reduction 2015-2030. While numerous efforts have substantially advanced the large-scale modeling of hazard and exposure through Earth observation and data-driven methods, progress remains limited in modeling another equally important yet challenging element of the risk equation: physical vulnerability. To address this gap, we introduce Graph Categorical Structured Variational Autoencoder (GraphCSVAE), a probabilistic data-driven framework for modeling physical vulnerability by integrating deep learning, graph representation, and categorical probabilistic inference, using time-series satellite-derived datasets and expert priors. We introduce a weakly supervised first-order transition matrix to capture changes in the spatiotemporal distribution of vulnerability across two disaster-affected and socioeconomically disadvantaged regions: the cyclone-impacted Khurushkul community in Bangladesh and the mudslide-affected city of Freetown in Sierra Leone. Across both case studies, the framework con
With the increasing frequency and intensity of natural disasters, there is a necessity for advanced technologies that can provide reliable situational awareness and communication. Conventional systems are often inadequate due to unreliable infrastructure, power grid failures, high investment costs and scalability challenges. This paper explores the potential of ad-hoc mesh joint radar and communication (JRC) networks as a scalable, resilient, energy-efficient solution for disaster management that can operate independently of conventional infrastructure. The proposed JRC network enhances disaster response by integrating target detection (such as identifying vital signs, hazardous leaks, and fires) with communication capabilities to ensure efficient information dissemination under intense clutter conditions. Key performance metrics, including data rate, Signal-to-Clutter and Noise Ratio (SCNR), probability of detection, and false alarm rate, are used to assess performance. An optimization approach is proposed to provide an energy-efficient resource allocation scheme. The results show the performance of ad-hoc mesh JRC systems, underscoring their potential to enhance disaster manageme