Abstract: In this document we explore position tracking in the context of land based search and rescue operations, where we also may have a limited and unreliable communication channel. This is the case when using APRS (amateur radio tracking) in voluntary SAR services in Norway. We have looked more closely into trails of movements and how to plot these on the map to present informative real-time pictures to the incident commanders. A simple scheme is proposed to improve trails by piggybacking positions at the end of regular transmissions.Experiments show that a significant amount of positions are recovered. In some cases this can recover useful information, though it depends on the actual situation.

Abstract: Face mask scarcity in the United States hindered early infection control efforts during the COVID-19 pandemic. Areas with a history of racial segregation and poverty experienced differential COVID-19 death and morbidity rates. Supplying masks equitably and rapidly became an urgent public health priority. A partnership between a local manufacturer with available polypropylene fabric and the Medical College of Wisconsin, which had the capability to assemble and distribute masks, was formed in April, 2020. An improvised logistics framework allowed for rapid distribution more than 250,000 masks, and later facilitated hand-off to other organizations to distribute over 3 million masks. Using an action research framework three phases of the effort are considered, 1) initial deliveries to community clinics, 2) equitable distribution to community agencies while under “safer at home” orders, and 3) depot deliveries and transfer of logistics management as larger agencies recovered. A multi-actor view was used to interrogate the information needs of faculty and staff remotely directing distribution, medical student volunteers delivering masks, and the manufacturer monitorng overall inventory. Logistics information was managed using Google Sheets augmented with a small SQLite component. A phenomenological view, toggling back and forth from the “socio” to the “technical” provides detailed insight into the strengths and limitations of digital solutions for humanitarian logistics, highlighting where paper-based processes remain more efficient. This case study suggests that rather than building bespoke logistics software, supporting relief efforts with non-traditional responders may benefit from extensible components that augment widely used digital tools.

Abstract: Timely and reliable sensing of infrastructure conditions is critical in disaster management for planning effective infrastructure restorations. Social media, a near real-time information source, has been widely used in the disaster domain for building timely, general situational awareness, such as urgent public needs and donations. However, the employment of social media for sensing electricity infrastructure conditions has yet been explored. This study aims to address the research gap to sense electricity infrastructure conditions through mining public topics from social media. To achieve this purpose, we proposed a systematic and customized approach wherein (1) electricity-related social media data is extracted by the classifier developed based on Bidirectional Encoder Representations from Transformers (BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams to incorporate the formulaic expressions of infrastructure conditions in social media. Electricity infrastructures in Florida impacted by Hurricane Irma are studied for illustration and demonstration. Results show that the proposed approach is capable of sensing the temporal evolutions and geographic differences of electricity infrastructure conditions.

Abstract: The Covid19 crisis has highlighted once more that socio-economic inequalities are a main driver of vulnerability. Especially in densely populated urban areas, however, these inequalities can drastically change even within neighbourhoods. To better prepare for urban crises, more granular techniques are needed to assess these vulnerabilities, and identify the main drivers that exacerbate inequality. Machine learning techniques enable us to extract this information from spatially geo-located datasets. In this paper, we present a prototypical study on how Principal Component Analysis (PCA) to analyse the distribution of labour and residential characteristics in the urban area of Helsinki, Finland. The main goals are twofold: 1) identify patterns of socio-economic activities, and 2) study spatial inequalities. Our analyses use a grid of 250x250 meters that covers the whole city of Helsinki, thereby providing a higher granularity than the neighbourhood-scale. The study yields four main findings. First, the descriptive statistical analysis detects inequalities in the labour and residential distributions. Second, relationships between the socio-economic variables exist in the geographic space. Third, the first two Principal Components (PCs) can extract most of the information about the socio-economic dataset. Fourth, the spatial analyses of the PCs identify differences between the Eastern and Western areas of Helsinki, which persist since the 1990s. Future studies will include further datasets related to the distribution of urban services and socio-technical indicators.

Abstract: During disasters public health organizations increasingly face challenges in acquiring and transforming real-time data into knowledge about the dynamic public health needs. Resources on the internet can provide valuable information for extracting knowledge that can help improve decisions which will ultimately result in targeted and efficient health services. Digital content such as online articles, blogs, and social media are some of such information sources that could be leveraged to improve the health care systems during disasters. To efficiently and accurately identify relevant disaster health information, extraction tools require a common vocabulary that is aligned to the health domain so that the knowledge from these unstructured digital sources can be accurately structured and organized. In this paper, we study the degree to which the Unified Medical Language System (UMLS) contains relevant disaster, public health, and medical concepts for which public health information in disaster domain could be extracted from digital sources.

Abstract: Many cities, especially those in developing countries, are not well prepared for the devastating disaster of exceptional rain-induced waterlogging caused by extreme rainfall. This paper proposes a waterlogging scenario prediction and crisis management method for such kind of extreme rainfall conditions based on high-precision waterlogging simulation. A typical urban region in Beijing, China is selected as the study area in this paper. High-precision and full-scale data in the study area requested for the waterlogging simulation are introduced. The simulation results show that the study area is still vulnerable to extreme rainfall and the subsequent waterlogging. The waterlogging situation is much more severe with the increase of the return period of rainfall. This study offers a good reference for the relevant government departments to make effective policy and take pointed response to the waterlogging problem.

Abstract: The resilience of water supply infrastructure (WSI) is of utmost importance as threats to predominantly, although not exclusively, urban WSI may accompany virtually all kinds of natural disasters. In this paper, we present some of the challenges posed by climate change in modeling emergencies in WSIs. Climate change is a global phenomenon that significantly impacts global lifestyle. It is expected that increase in global temperatures causes sea levels to rise, increases the number of extreme weather events such as floods, droughts, and storms while highly impacting WSI. In this respect, the challenge is to be prepared for the unexpended by modeling various complex scenarios. Only with a multidisciplinary approach at the global, regional, national, and local levels, can success be achieved. We discuss some of the specific challenges posed by climate change in modeling emergencies in WSIs with a case study modeled using EMERTIC. EMERTIC is a software based on AI and scenarios, that is aimed at supporting decision making at different stages of the Emergency Management cycle.

Abstract: In a classification task, dealing with text snippets and metadata usually requires to deal with multimodal approaches. When those metadata are textual, it is tempting to use them intrinsically with a pre-trained transformer, in order to leverage the semantic information encoded inside the model. This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. Based on additional experiments of the model weights and behavior, it identifies how the proposed neural network approach is partially over-fitting the particularities of the Crisis Benchmark, to better highlight how the model is still undoubtedly learning to use and take advantage of the metadata's textual semantics.

Abstract: In the recent months, many measures have been taken by governments to fight the COVID-19 pandemic. Due to the unknown properties of the disease and a lack of experience with handling pandemics, the effectiveness of measures taken was often hard to evaluate the effectiveness of measures, leading to inefficient measures and late execution of efficient measures. Many models have been proposed to evaluate the performance of these measures on the spreading of a pandemic, but these models are commonly vastly simplified and, thus, limited in expressiveness. To extend the expressiveness of the models, we developed a epidemic simulation inside of a flexible and scalable city simulation game to analyse the counter measures to a pandemic in this city and spot common places of infection on a microscopic level. The configurability of our developed epidemic simulation will also be useful for potential future pandemics.

Abstract: Due to the high popular concentration of large-scale events, once an emergency (like a stampede) occurs, it will often cause severe casualties. Moreover, since the widespread of the COVID-19, the prevention of the novel coronavirus should also be considered during mass gatherings. How to reduce the probability and potential consequence of emergencies is of great significance. This research designs an emergency management framework using ArcGIS-based geographic information technology for large-scale events. To verify the effectiveness of our framework, we take the Winter Olympic torch relay in university as an example. The paper is mainly divided into two parts, emergency resource allocation and the emergency prevention model. The former part focuses on the site selection of emergency sentries and emergency hospitals during the torch relay. In the latter part, an emergency prevention model is designed for two significant emergencies: stampede and epidemic.

Abstract: CURSOR (Coordinated Use of miniaturized Robotic equipment and advanced Sensors for search and rescue OpeRations) is an ongoing European H2020 project with the main objective to enhance the efficiency and safety of Urban Search and Rescue (USaR) operations on disaster sites. CURSOR's approach relies on the integration of multiple mature and emerging technologies offering complementary capabilities to an USaR system, so as to address several challenges and capability gaps currently encountered during first responder missions. The project's research and development are structured around an earthquake master scenario. CURSOR aspires to advance the state-of the-art in several key aspects, including reduced time for victim detection, increased victim localization accuracy, enhanced real-time worksite information management, improved situational awareness and rescue team safety.

Abstract: Since the beginning of this millennium, there has been an increasing use of social media and crowdsourcing (SMCS) technologies in disaster situations (Reuter & Kaufhold, 2018). Disaster management organizations and corresponding research are increasingly working on ways of integrating SMCS into the processes of crisis management. In a changing technological landscape to address disasters, and with increasing diversity of stakeholders in disasters, the purpose of this research is to provide an overview of technologies for SMCS within disasters to improve community resilience. The identified and analyzed technologies are summarized under the term “Disaster Community Technologies” (DCT). The paper presents a classification schema (the “DCT-schema”) for those technologies. The goal is to generate an overview of DCT in a rapidly evolving environment and to provide the practical benefit for different stakeholders to identify the right one from the overview.

Abstract: Situational awareness (SA), the ability to perceive, comprehend and predict situation around you and it is a key in attending any incident as critical foundation for successful decision-making. Because incidents are solitary events, development and assessment of SA presents a significant challenge. In this article we analyze the authoring process of twenty-two scenarios implemented in the XVR on-scene virtual simulation software used to assess rescue incident commanders' (ICs) SA. To allow the scenarios to be used by different assessors, the Collaborative Authoring Process Model for Virtual Simulation Scenarios (CAPM) was developed. In Estonia, 473 assessments were recorded in Effective Command database and analysed by all three levels of SA as recommended by Endsley (2000). Introduction of CAPM resulted in scenarios being re-used by different assessors for authentic SA measuring. In the last sections of this article, we introduce our suggestions to improve virtual scenario design and SA research.