Commercial Multirotor UAV Campaign on Data Acquisition for Disaster Management

Sidoarjo Regency is one of the areas in East Java Province which has the potential for natural and non-natural disasters. So this disaster needs to be overcome with the role of the Sidoarjo Regency BPBD in efforts to deal with the impact of the disaster. So the Sidoarjo Regency BPBD created the Sidoarjo Response Application 'Sigap' which functions for the community if a disaster occurs or is affected by a disaster in the community. This research uses a qualitative method with a descriptive approach using primary and secondary data obtained through observation, interviews and documentation. The focus of this author's research is the effectiveness of the Sidoarjo Response 'Sigap' application in disaster management in Sidoarjo Regency. The results of this research explain that the Sidoarjo Regency BPBD has carried out its role in efforts to increase the effectiveness of the Sigap Application according to its duties and functions, namely: 1). System Quality 2). Information quality 3). Service Quality. There are obstacles or obstacles to research, namely the activities of village communities, but these obstacles can be minimized through the decision of the District BPBD. Sidoarjo. Supporting Factors are the involvement of all elements of Sidoarjo Regency society in participating in activities provided by agencies related to the Effectiveness of the Sigap Application in Disaster Management in Sidoarjo Regency.

The radar warning and information system for applications in disaster management (RAWIS) is being designed and constructed in a consortium comprising eight partners. It is intended for civil protection applications and will support emergency teams engaged in complex mission contexts. This paper gives an overview of the overall project and addresses some aspects of the sub-projects.

Abstract. Artificial Intelligence (AI) has the potential to play a significant role in disaster risk management – from predicting disasters to optimizing response efforts. However, its application in disaster management also presents significant challenges. These challenges include the need for high-quality and diverse data, compatibility with existing systems and technologies, ethical and social implications, and ongoing research and development. In addition, data privacy and security is a critical issue as the use of AI in disaster management often involves the collection and analysis of sensitive information. Addressing these challenges is crucial to ensuring that AI is developed and used in ways that are fair, equitable, and effective in reducing the impact of disasters. The aim of the paper is to provide an analysis of challenges of artificial intelligence application for disaster risk management.

Advancement in the satellite imaging techniques has produced wider applications in disaster reduction and management. Considering the crucial role of rapid detection of landslides in disaster management, this research paper exploits the capability of sentinel-1A imagery in landslide detection. In image processing and feature detection techniques, machine learning based classification methods have gained popularity by proving its potential in mapping of susceptible landslides. Therefore, the primary objective of this research paper is automatic identification of landslides by using machine learning based classifier Random Forest (RF). To accomplish the objective, two Sentinel-1A GRD images, pre and post-event, covering Srinagar-Rudraprayag region, India has been processed with DEM derived products slope, aspect and elevation for landslide detection along the Alaknanda River and National Highway-58. For validation of the results, Google Earth Historic Imagery has been utilized. From the observation of results, RF method confirms its potential by achieving 95% in accurate detection of landslides.

Artificial intelligence (AI)-based emergency response systems have emerged as critical enablers of smart infrastructure safety, offering enhanced real-time decision-making, risk assessment, and disaster mitigation strategies across various domains. This systematic literature review, encompassing 424 eligible studies, investigates the integration of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimizing emergency response mechanisms. The study comprehensively examines AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control, providing a detailed assessment of technological advancements and challenges in AI adoption. The findings reveal that AI has significantly improved predictive accuracy, automated hazard detection, and emergency resource optimization, leading to faster response times, minimized human error, and enhanced situational awareness in crisis management. AI-driven predictive analytics models have enabled early warning systems for earthquakes, floods, and wildfires, facilitating proactive disaster preparedness and risk mitigation. In real-time emergency response, AI-powered computer vision and sensor-based surveillance technologies have improved incident detection, reducing intervention delays and ensuring more efficient allocation of emergency resources. In the healthcare sector, AI-enhanced diagnostic tools, triage automation, and geospatial analytics for ambulance dispatch have streamlined medical crisis management, improving survival rates and reducing treatment delays. Additionally, AI-integrated industrial safety frameworks, robotic automation, and cybersecurity intelligence systems have strengthened workplace hazard prevention, cyber threat detection, and emergency communication resilience, ensuring safer and more secure operational environments. Despite these advancements, several challenges related to interoperability, regulatory constraints, cybersecurity vulnerabilities, algorithmic biases, and ethical concerns persist, hindering large-scale AI adoption in emergency response systems. This review provides a comprehensive synthesis of AI’s transformative role in modern emergency management, offering insights into technological developments, limitations, and policy considerations necessary to enhance AI-driven crisis response strategies and ensure more effective, scalable, and resilient emergency safety infrastructures worldwide.

The role of GIS-enabled mobile applications in disaster management: A case analysis of cyclone Gaja in India

This paper discusses the use of spatial data for risk and natural disaster management. The importance of remote-sensing (RS), Geographic Information System (GIS) and Global Navigation Satellite System (GNSS) data is stressed by comparing studies of the use of these technologies for natural disaster management. Spatial data sharing is discussed in the context of the establishment of Spatial Data Infrastructures (SDIs) for natural disasters. Some examples of SDI application in disaster management are analyzed, and the need for participation from organizations and governments to facilitate the exchange of information and to improve preventive and emergency plans is reinforced. Additionally, the potential involvement of citizens in the risk and disaster management process by providing voluntary data collected from volunteered geographic information (VGI) applications is explored. A model relating all of the spatial data-sharing aspects discussed in the article was suggested to elucidate the importance of the issues raised.

This study investigates the awareness and adoption of cloud computing for disaster-resilient library management systems at the Federal University of Education (FUE), Zaria, Nigeria.Using a descriptive survey design, data were collected from 50 library staff (100% response rate) through a validated structured questionnaire (Cronbach's alpha = 0.82).Results reveal low-to-moderate awareness levels, with 40% of respondents slightly aware and only 20% very aware of cloud computing applications in disaster management (M = 2.60, SD = 0.95).Adoption remains critically limited, with 70% reporting no implementation, 20% partial adoption, and merely 10% full adoption (M = 1.40,SD = 0.67).Chi-square analysis revealed significant associations between awareness levels and adoption status ( = 18.45, p < 0.01), indicating that higher awareness correlates with greater adoption likelihood.Key barriers include high costs (36%), lack of technical expertise (26%), and data security concerns (18%) (M = 3.70, SD = 1.15).Applying the Technology Acceptance Model (TAM), perceived usefulness ( = 0.58, p < 0.01) and perceived ease of use ( = 0.45, p < 0.05) significantly predicted adoption intentions.These findings underscore the urgent need for strategic interventions-including training programs, infrastructure investment, and policy development-to bridge awareness-adoption gaps and enhance library disaster resilience in resource-constrained settings.

Disasters increasingly challenge urban resilience, demanding advanced computational approaches for effective information management and response coordination. This interdisciplinary review systematically assesses Large Language Model (LLM) applications in disaster management, analyzing 70 LLM-focused studies within the broader landscape of AI-driven disaster management. Our analysis establishes a phase-based framework spanning detection, tracking, analysis, and action, and reveals three critical gaps in current disaster management solutions: limited advancement beyond disaster response to include preparedness, recovery, and mitigation phases; insufficient integration across diverse stakeholder groups and available resources; and inadequate transformation of situation awareness data into actionable insights. Leveraging cross-modal semantic reasoning, knowledge graph-constrained entity extraction, and advanced code generation, LLMs are well positioned to overcome information ambiguity and verification challenges often encountered in rapidly evolving disaster contexts. These capabilities also enable automation in disaster investigation and communication, effectively orchestrating diverse analytical tools and resources. To harness these advantages and promote further progress, we introduce the “3M” framework for intelligent disaster information management: multi-modal data fusion for integrated assessment, multi-source information validation for robust truth-finding, and multi-agent collaboration in physical–virtual disaster systems. This framework provides a systematic foundation for advancing next-generation LLM-driven disaster management research and practice in increasingly complex contexts • First systematic review of LLM applications in disaster management (2020-2024). • Synthesizes LLM functions for disaster detection, tracking, analysis, and action. • Identifies critical gaps through integrated disaster information management theory. • Proposes “3M” framework enabling future LLM integrated disaster management.

Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc. In particular, the endless patterns of background changes require detectors to have a high generalization against unseen environment variations, making this task significantly challenging. Recent deep learning-based methods develop novel network architectures or optimization strategies with paired-training examples, which do not handle the generalization issue explicitly and require huge manual pixel-level annotation efforts. In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. Different from general augmentation techniques for classification, we propose the background-mixed augmentation that is specifically designed for change detection by augmenting examples under the guidance of a set of background changing images and letting deep CD models see diverse environment variations. Moreover, we propose the augmented &amp; real data consistency loss that encourages the generalization increase significantly. Our method as a general framework can enhance a wide range of existing deep learning-based detectors. We conduct extensive experiments in two public datasets and enhance four state-of-the-art methods, demonstrating the advantages of our method. We release the code at https://github.com/tsingqguo/bgmix.

Over the years, various hydro-meteorological disasters have impacted many countries and civilians, thus decreasing the losses caused by these disasters has become important for disaster management. While bridges often are essential to connect the affected disaster areas, this study focuses on bridge management associated with typhoons and rain-storms. Recently, although numerous information technology-based applications are available, three problems (i.e. asynchronous information records, unsteady information transmission, and inaccessible information analysis) still influence responses on disaster sites. Therefore, this study integrated several mobile information techniques to propose a mobile disaster response system. The applied mobile GPS- and GIS-based data reports simplify information generation, AR-based assistance enhances information representation, and mobile 3D graphics accelerates information processes. Based on the tests, this system not only improved the recognized problems but also strengthened the cooperation between construction engineers and workers and disaster managers. Overall, this study is a useful reference for similar applications in bridge management and disaster management.

Disaster risk reduction is a totalizing framework that was established from the conception of protecting communities from hazards and minimizing their vulnerability to the risks of disaster. One of the many aspects of disaster risk reduction is through the participatory involvement of the local community. The paper will discuss the application of systematic disaster management in managing emergency and post emergency situations by adopting the role of civil society in the context of community based approaches. These approaches are proven to be adaptive and comprehensive enough in progressing through the hard times during and after the event of disaster. The paper will outline comprehensive and integrated methods from previous experiences in assisting the process of internalizing the concept of disaster risk reduction and strategic planning for disaster management. It is hoped that from the shared experience discussed in this paper will contribute to a more resilient community that are well informed and prepared on the culture of disaster risk reduction and disaster management practices.

In the recent years, hundreds to thousands of people have been killed in many calamities across Asian region, such as tsunami, floods, earthquake and so on. These natural disasters have become great challenges, particularly to those developing countries as many are lacking of an efficient disaster management system and disaster recovery plan that will help in times of need. Among the common challenges in managing emergency relief response is the coordination of logistics distribution on the field. Often there is hoarding of assistance from a number of institutions at a particular location or village but not well distributed to other areas, thus led to waste and inefficiencies. As a result, many of the aids supplied could not reach its intended target. Therefore, it is the aim of this study to propose a coordination framework for logistics distribution in disaster management, and to overcome communication problems among and between rescue, volunteers and humanitarian teams, particularly in logistics distribution. The proposed framework will be implemented in the form of a prototype for mobile application.

Small (< 25 kg) aerial drones have expanded the remote sensing toolkit for disaster management activities. Here, we provide a critical review of drone-based remote sensing of natural hazard-related disasters to highlight research trends, biases, and expose new opportunities. We performed a systematic literature search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, resulting in 635 relevant articles from which we derived statistics relating to geography, drone hardware, disaster management application, and drone remote sensing data type and analysis method. Key findings include a bias towards: (i) mass movement hazards (38%); (ii) small (< 1 km2) (76%) and rural (79%) study areas in high-income countries and territories (64%); (iii) image-based observations of features from the natural environment (77%); and (iv) support of mitigation-related vulnerability assessment and risk modeling (54%) and environmental recovery (23%). We recommend that future studies focus on: (i) earthquakes, floods, and cyclones and other windstorms due to higher loss of life and economic impacts; (ii) larger and urban study areas in low, lower-middle, and upper-middle income countries and territories to support vulnerable populations; (iii) under-demonstrated (and especially response-related) disaster management activities, which generally require observations of built features from urban environments; and (iv) data standards for integrating drone-based remote sensing with international disaster management methodologies.

Interactions between stakeholders providing their services in disaster management might involve negotiations for optimal selection of service providers. In this paper we propose a conceptual framework for designing cooperative multi-issue one-to-many service negotiations. The framework allows us to define: negotiation protocols, negotiation subject, properties of negotiation subject issues, deal spaces, and utility functions of participant agents. We also consider a simple example of how this framework can be instantiated to particular negotiation in a disaster management information system.