The International Charter ‘Space and Major Disasters’

Disasters have a significant global impact every year and disproportionally effect vulnerable populations. Disaster response efforts are often inequitable leaving already vulnerable populations at risk. These inequities in disaster response represent a social justice concern for congregations and congregational social workers and must be considered in future disaster recovery efforts. As churches and faith organizations provide essential disaster response and recovery services, this article outlines a model for increased inequity in provision of disaster response and recovery efforts. A case illustration using the COVID-19 pandemic is provided for further exploration of the equitable disaster response model in the context of congregations and congregational social work.

Lay Health Workers can play a pivotal role in improving disaster response and recovery because of their potential effectiveness in enhancing the overall health of their communities, increasing disaster preparedness, supplementing the efforts of disaster responders, and building relationships of trust among all interested parties. Such activities build social capital and significantly enhance community resiliency in anticipation of future disasters. Although there are a number of different types of lay health workers, the version with the greatest potential in this area is the Community Health Worker (CHW). Recent research findings confirm that CHWs serving in the communities where they live have been beneficial in emergency management planning and disaster recovery, following both natural and technological disasters. When properly trained, they constitute a proven strategy for timely interventions aimed at reducing long-term collective trauma and building social capital. In this paper, we elaborate the characteristics and roles of CHWs as a specific type of lay health worker; review research on the utility of CHWs in health care generally, as well as in the area of emergency management; describe their potential for building social capital and enhancing community resilience; and provide an overview of essential training needed to prepare them to participate in disaster preparedness, response, and recovery efforts. We conclude with some suggestions for future research.

Natural disasters, including earthquakes, wildfires, floods, and disease outbreaks, have catastrophic consequences for human lives, infrastructure, and economies worldwide. Their increasing frequency and intensity, exacerbated by climate change and urbanization, present significant challenges to disaster management. Traditional approaches often struggle to cope with the scale and complexity of modern disasters. This narrative review examines the potential of artificial intelligence (AI) to transform disaster management, exploring its applications across the disaster cycle. By assessing AI's role in damage assessment, early warning systems, resource allocation, and post-disaster rehabilitation, the review identifies opportunities to enhance disaster response and recovery efforts. Additionally, it addresses challenges related to AI implementation, such as algorithmic bias, data quality, and ethical considerations, to develop equitable and robust AI-driven disaster management strategies. A comprehensive literature review was conducted to identify key research on AI’s role in early warning systems, disaster management, damage control, resource allocation, and post-disaster recovery. AI holds significant potential to improve disaster response and recovery. It can enhance early warning systems, optimize resource allocation, and improve damage assessment. Furthermore, AI can support post-disaster repair and rehabilitation efforts. However, challenges such as data quality, ethical concerns, and algorithmic bias must be addressed to ensure the responsible and effective application of AI in disaster management. By harnessing the power of AI, we can build more resilient societies and reduce the impact of future disasters. To fully realize AI’s potential, it is essential to integrate it into research, development, and ethical frameworks, ensuring its responsible and efficient implementation.

Natural disasters, such as floods, can cause significant damage to both the environment and human life. Rapid and accurate identification of affected areas is crucial for effective disaster response and recovery efforts. In this paper, we aimed to evaluate the performance of state-of-the-art (SOTA) computer vision models for flood image classification, by utilizing a semi-supervised learning approach on a dataset named FloodNet. To achieve this, we trained son 11 state-of-the-art (SOTA) models and modified them to suit the classification task at hand. Furthermore, we also introduced a technique of varying the uncertainty offset λ in the models to analyze its impact on the performance. The models were evaluated using standard classification metrics such as Loss, Accuracy, F1 Score, Precision, Recall, and ROC-AUC. The results of this study provide a quantitative comparison of the performance of different CNN architectures for flood image classification, as well as the impact of different uncertainty offset λ. These findings can aid in the development of more accurate and efficient disaster response and recovery systems, which could help in minimizing the impact of natural disasters.

On July 28, 1997, Colorado State University suffered a devastating water disaster that seriously damaged more than thirty campus buildings, including Morgan Library and more than 500,000 volumes in its basement. The resulting response and recovery efforts took seven years, multimillion dollars, and thousands of staff hours. From the first night, safety and security issues were integral to the disaster response and recovery efforts. This article focuses on security during and immediately after the disaster, our effort to assess the extent of damage to the collection, representing our interests to the insurance company, and our risk-management efforts after the disaster and how they have changed our security policies and procedures.

In the case of a major disaster, information derived from satellite observation is not only highly useful, it may at times be indispensable because of the damage caused by the disaster to ground infrastructure. The International Charter ‘Space and Major Disasters’ (‘the Charter’) has been one of the primary sources of satellite data for the past 11 years to cover events like floods, fires, tsunamis, ocean storms, earthquakes, volcanic eruptions and oil spills. With the growing membership of the Charter, an increasingly large number of sensors are now available, which can be planned with the required temporal frequency and spectral range to cover a disaster event. Some of the type Charter activation cases are reported in this article to demonstrate the innovative use of multi-satellite imagery for disaster response.

Abstract. This research aims to investigate the application of computer vision and machine learning for the automatic detection of wall collapse damage in historic buildings caused by natural and man-made disasters. Given the complexities involved in inspecting damaged buildings, particularly in post-disaster scenarios, this research aims to establish a foundation for creating an automated assessment process. Our findings demonstrate the successful automatic detection of various shapes of wall collapse on damaged buildings from the Beirut explosion of 2020, as well as from other damaged buildings obtained from the internet, thereby highlighting the transferability of our method. This research paves the way for the development of a more robust machine learning model capable of detecting a broader range of damages, which can significantly enhance the efficiency and accuracy of post-disaster assessment of historic structures. The paper presents a novel approach for damage detection and quantification, which underscores the potential of structural health monitoring in improving disaster response and recovery efforts.

In August 2006, a year after Hurricane Katrina, the first acute inpatient public psychiatric unit for adults was opened in New Orleans to serve patients referred from local emergency departments. This article describes the clinical and administrative experiences of providing inpatient care in post-Katrina New Orleans, including the increased demand for programs to treat patients with co-occurring disorders, the expanded scope of practice for psychiatrists to include primary care, and ongoing staff shortages in a traumatized and displaced workforce. Lessons learned in regard to disaster planning and recovery are also discussed.

Community-led disaster risk management is a method that stresses community participation and involvement in disaster prevention, response and recovery. This approach acknowledges that communities possess unique knowledge, resources and capacities that can be used to improve disaster management. Community-led disaster risk management, which involves communities in decision-making processes and provides them with the appropriate tools and training, can improve local resilience and the overall efficacy of disaster response and recovery activities. This approach also recognizes the need to address social, economic and environmental issues that contribute to vulnerability and increase the risk of disaster. Community-led disaster risk management, sometimes known as the "bottom-up approach," brings together local community skills to effectively address and mitigate disaster risks. This method improves local resilience and increases overall disaster response and recovery efforts by actively engaging communities in decision-making processes and providing them with the appropriate tools and training. Furthermore, by addressing social, economic and environmental elements that contribute to vulnerability, community-led disaster risk management helps to reduce the risk of disasters. This bottom-up strategy enables communities to take proactive steps toward catastrophe preparedness, response and recovery, resulting in more sustainable and resilient communities. Previous studies have highlighted that involving communities in policy development and decision-making processes enhances sustainability, resilience and local engagement in policy implementation. As shown in many cases, local stakeholders including Government agencies at the national, state and local levels, local champions and leaders, as well as non-governmental organizations (NGOs), often collaborate closely to develop comprehensive and effective disaster management plans.

Introduction The International Charter on Space and Major Disasters (the Charter) has been signed in October 2000 by CNES, CSA and ESA. Since then, ISRO, NOAA and CONAE have joined the Charter. The objective of this global initiative is to provide free of charge access to space system products to authorities concerned with catastrophic events in the form of data and information products obtained from the various space borne sensors for situations where human life is in danger or when substantial damage on infrastructure or resources are expected. Using guidelines put together by application experts from the various space agencies, the 24-hours-a-day stand-by officer is able to efficiently task the available sensors to obtain the most relevant data in the shortest delays. The Charter is the first multi-agency, multi-mission, multisensor operational service available to the international community. The members participate on a voluntary basis with no exchange of funds. The interoperability of the tasking system has proven its efficiency. As of March 1, 2004, the Charter has been Charter team. The Charter web site provides more details on its functions and operations [1]. Charter Operation The operation flow chart of the Charter is presented on Figure 1. To activate the Charter, an Authorized User needs to prepare a User Request Form providing details on the event. This request is received by the On-Duty Operator (the ODO), located in ESA's offices in Frascati, Italy. The ODO then contacts the Emergency On-Call Officer (the ECO) who is a representative of one of the Participating Agencies. The ECO 24-hour a day support task rotates from one agency to the other on a weekly basis. The ECO has a thorough knowledge of the technology made available by the Participating Agencies and of the requirements for the various classes of disasters. The ECO then has to task the appropriate sensors according to the type of events in the shortest delays. Meanwhile, the Executive Secretariat of the Charter has to look for a Project Manager (PM) who is an expert knowledgeable of the area and of the type of emergency and who can take over the project until its conclusion. Once the PM is confirmed, the ECO passes over the Dossier and the responsibilities of the project. The PM has to interact with the

PurposeThe purpose of this paper is to provide a nuanced understanding of the complex factors driving the decision of commercial property owners (investors and developers) to stay and rebuild after a major disaster. The study examines what happens in the post-disaster rebuild of a central business district (CBD) from the perspective of commercial property owners in Christchurch, New Zealand.Design/methodology/approachAn interpretive approach is adopted to understand what it takes to rebuild in a post-disaster environment through the lens and experiences of property owners. The study has observed the significant physical changes that have occurred in the Christchurch CBD as a result of the earthquakes. For this study, qualitative data were obtained through semi-structured interviews from 20 purposively identified property owners rebuilding the Christchurch CBD. The interview findings were subjected to a thematic analysis used to provide a factual way of characterising the viewpoints of those interviewed.FindingsThe findings have highlighted that the decision-making behaviour of property owners in Christchurch’s CBD post-disaster rebuild has been driven significantly by an emotional attachments to people and place.Practical implicationsThe global trend in increasing destruction from natural disasters has raised the need for more efficient and effective post-disaster responses and activities. The paper has developed a knowledge base required to inform public policy and advice all those involved in the rebuilding of cities after a major disaster.Originality/valueThis paper contributes to the property literature and debates about the decision-making behaviour of commercial property owners who are engaged in rebuilding after a major natural disaster. The qualitative methodology used presents a novel approach to property research. The findings challenge the underlying premises of much of the mainstream property literature on normative investment behaviour and decision making.

Community resilience has grown in importance in national disaster response and recovery efforts. However, measurement of community resilience, particularly the content and quality of relationships aimed at improving resilience, is lacking. To address this gap, we used a social network survey to measure the number, type, and quality of relationships among organizations participating in 16 coalitions brought together to address community resilience in the Los Angeles Community Disaster Resilience project. These coalitions were randomized to one of two approaches (community resilience or preparedness). Resilience coalitions received training and support to develop these partnerships and implement new activities. Both coalition types received expert facilitation by a public health nurse or community educator. We also measured the activities each coalition engaged in and the extent to which partners participated in these activities at two time points. We found that the community resilience coalitions were initially larger and had lower trust among members than the preparedness communities. Over time, these trust differences dissipated. While both coalitions grew, the resilience community coalitions maintained their size difference throughout the project. We also found differences in the types of activities implemented by the resilience communities; these differences were directly related to the trainings provided. This information is useful to organizations seeking guidance on expanding the network of community-based organizations that participate in community resilience activities.

The increasingly frequent occurrence of major natural disasters can pose a serious threat to national stability and the safety of people's lives, and cause serious economic losses. How to quickly and accurately dispatch emergency materials to all disaster areas across regions in post-disaster has attracted wide attention from the government and academia. In response to the characteristic of high uncertainty in emergency rescue for major natural disasters, and considering differentiated disaster severity levels in different disaster areas, the entropy weight method is used to determine the urgency coefficient of emergency material demand for disaster areas. This study aims to minimize the emergency materials dispatching time and cost, also maximize the dispatching fairness for disaster areas. The triangular fuzzy number method is used to represent the uncertain variables mentioned above, so that a cross-regional emergency materials intelligent dispatching model in major natural disasters (CREMIDM-MND) is constructed. The extremely heavy rainstorm disaster in Henan Province of China in 2021 is selected as a typical case. Based on objective disaster data obtained from official websites, this study applies the constructed model to real disaster case and calculates the results by MATLAB. The ant colony algorithm is further used to optimize the transportation route based on the calculation results of the emergency material dispatching for disaster areas, and finally forms the intelligent emergency materials dispatching scheme that meets the multiple objectives. The research results indicate that compared to the actual situation, CREMIDM-MND can help decision-maker to develop a cross-regional emergency materials intelligent dispatching scheme in time, thereby effectively improving the government's emergency rescue performance in major natural disasters. Moreover, some managerial insights related to cross-regional emergency materials dispatching practice problem in major natural disasters are presented.

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature Engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts.

This study aimed to provide an in-depth analysis of women's experiences following a major forest fire. In qualitative research, sample size is not statistically determined, as generalization is not the primary goal. A small number of participants can yield rich data. The interview form included 3 demographic questions and 10 open-ended items aligned with the study's objectives. Data analysis was conducted using MAXQDA version 24.1.0. Thematic analysis identified 3 main themes: Emotional Response, Fire Intervention, and Gender, comprising 8 categories. Participants commonly reported intense fear and anxiety, accompanied by physical symptoms such as tension and elevated blood pressure. Primary concerns involved the loss of pets, resources, security, and shelter. Women were found to be deeply affected emotionally and psychologically by the fire experience. The study revealed that women were equally engaged in firefighting efforts as men and also played a crucial caregiving role, offering support to affected individuals. These dual roles reflect both their resilience and the emotional burden they carried. The findings underscore the significant psychological impact of wildfires on women and highlight the necessity of integrating gender-sensitive approaches in disaster response and recovery efforts.