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Adam Flizikowski, M. P., Anna Stachowicz, Tomasz Olejniczak, & Rafael Renk. (2015). Text Analysis Tool TWeet lOcator ? TAT2. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Information about location and geographical coordinates in particular, may be very important during a crisis event, especially for search and rescue operations ? but currently geo-tagged tweets are extremely rare. Improved capabilities of capturing additional location from Twitter (up to 4 times improvement) are crucial for response efforts given a vast amount of messages exchanged during a crisis event. That is why authors have designed a tool (Text Analysis TWeet lOcator ? TAT2) that relies on existing open source text analysis tools with additional services to provide additional hints about people location. Validation process, complementing experimentation and test results, included involvement of end-users (i.e. Public Protection and Disaster Relief services and citizens during a realistic crisis exercise showcase. In addition, the integration of TAT2 with external tools has also been validated.
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Erik G. Nilsson, & Ketil Stølen. (2016). The FLUIDE Framework for Specifying Emergency Response User Interfaces Employed to a Search and Rescue Case. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: The FLUIDE Framework supports development of flexible emergency response user interfaces, meeting the special challenges when developing such user interfaces. This paper presents the FLUIDE Framework with particular emphasis on its specifications languages. We demonstrate the FLUIDE Framework by giving examples from the FLUIDE specification of the user interface of an application supporting management of unmanned vehicles in search and rescue operations. We also report the findings from an experiment investigating how easy FLUIDE specifications are to understand for systems developers not knowing FLUIDE.
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Giulio Palomba, Alessandro Farasin, & Claudio Rossi. (2020). Sentinel-1 Flood Delineation with Supervised Machine Learning. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 1072–1083). Blacksburg, VA (USA): Virginia Tech.
Abstract: Floods are one of the major natural hazards in terms of affected people and economic damages. The increasing and often uncontrolled urban sprawl together with climate change effects will make future floods more frequent and impacting. An accurate flood mapping is of paramount importance in order to update hazard and risk maps and to plan prevention measures. In this paper, we propose the use of a supervised machine learning approach for flood delineation from satellite data. We train and evaluate the proposed algorithm using Sentinel-1 acquisition and certified flood delineation maps produced by the Copernicus Emergency Management Service across different geographical regions in Europe, achieving increased performances against previously proposed supervised machine learning approaches for flood mapping.
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Corine H.G. Horsch, Nanja J. J. M. Smets, Mark A. Neerincx, & Raymond H. Cuijpers. (2013). Revealing unexpected effects of rescue robots' team-membership in a virtual environment. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 627–631). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In urban search and rescue (USAR) situations resources are limited and workload is high. Robots that act as team players instead of tools could help in these situations. A Virtual Reality (VR) experiment was set up to test if team performance of a human-robot team increases when the robot act as such a team player. Three robot settings were tested ranging from the robot as a tool to the robot as a team player. Unexpectedly, team performance seemed to be the best for the tool condition. Two side-effects of increasing robot's teammembership could explain this result: Mental workload increased for the humans who had to work with the team-playing robot, whereas the tendency to share information was reduced between these humans. Future research should, thus, focus on team-memberships that improve communication and reduce cognitive workload.
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Iftikhar Ali, Vahid Freeman, Senmao Cao, & Wolfgang Wagner. (2018). Sentinel-1 Based Near-Real Time Flood Mapping Service. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1074–1080). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Globally floods are categorized as one of most devastating natural disasters and annually causing a major loss to human lives and economy. For rapid damage assessment and planning relief activities a large scale spatio-temporal overview is required to assist local authorities. This paper aims to provide an overview of a Sentinel-1 based near-real time flood mapping/monitoring service; which is implemented as an operational service under the framework of I-REACT (Improving Resilience to Emergencies through Advanced Cyber Technologies) project.
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Iva Seto, David Johnstone, & Jennifer Campbell-Meier. (2018). Experts' sensemaking during the 2003 SARS crisis. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 44–55). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: This paper depicts the real-time sensemaking of experts as they worked to combat the first emerging disease of the 21st century: Severe Acute Respiratory Syndrome (SARS). Newspaper data was analysed from the 2003 SARS crisis, with a Canadian perspective, to follow the process of solving the puzzle of this emerging disease. Retrospective sensemaking is a process that is triggered by the unexpected, which leads to actors gathering information (taking action) in order to consider possible interpretations for the unexpected event. Disease outbreaks serve as sensemaking triggers, and actors engage in retrospective sensemaking to find out the factors involved in how the outbreak happened. Prospective sensemaking (future-oriented) is employed when actors work together to plan how to combat the disease. The newspaper data demonstrate that retrospective and prospective sensemaking are tethered: to make plans to combat a disease, actors first require a collectively agreed upon understanding from which they can generate possibilities for a crisis response. This paper contributes to the field by providing concepts for long-duration crisis sensemaking, as the bulk of organisational research focuses on acute crises such as wildfires, or earthquakes.
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Jeff Maunder. (2018). The Geospatial Intelligence Continuum during Sudden Onset Disaster Response. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 246–253). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: This document will discuss the current methodologies used by New Zealand DART and USAR teams to collect manage, analyse and report on information gathered during the initial and subsequent phases of deployments to a sudden onset disaster (SOD). This will include some of the experiences that have formed the current methodology and the outcomes of disaster events with new methodologies applied. It will further identify and discuss the current systems and processes in place and how they have come about, and then identify a range of opportunities and issues that exist within the Geospatial Intelligence environment to be more effective, both in systems and the development of partnerships to enhance the usability and intuitive nature of these systems and methods. Finally, the discussion will look to identify a future state for responders to SOD's and the ability and outcomes of proposed and imagined future systems, leveraging off the current Esri packages to provide a starting platform and a desired end state.
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Koki Asami, Shono Fujita, Kei Hiroi, & Michinori Hatayama. (2022). Data Augmentation with Synthesized Damaged Roof Images Generated by GAN. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 256–265). Tarbes, France.
Abstract: The lack of availability of large and diverse labeled datasets is one of the most critical issues in the use of machine learning in disaster prevention. Natural disasters are rare occurrences, which makes it difficult to collect sufficient disaster data for training machine learning models. The imbalance between disaster and non-disaster data affects the performance of machine learning algorithms. This study proposes a generative adversarial network (GAN)- based data augmentation, which generates realistic synthesized disaster data to expand the disaster dataset. The effect of the proposed augmentation was validated in the roof damage rate classification task, which improved the recall score by 11.4% on average for classes with small raw data and a high ratio of conventional augmentations such as rotation of image, and the overall recall score improved by 3.9%.
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Linlin Ge, Alex Ng, & Zheyuan Du. (2018). Time Series Satellite InSAR Technique for Disaster Prevention? In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 200–212). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Interferometric synthetic aperture radar (InSAR) has been widely used for mapping terrain and monitoring ground deformation. For example, the advanced time series InSAR (TS-InSAR) technique has been increasingly used to measure mm-level urban deformation. Subsidence from underground tunnel excavation has been known for more than a decade in Guangzhou and Foshan in Southern China, but past studies have only monitored the subsidence patterns as far as 2011 using InSAR. In this study, the deformation occurring during the most recent time-period between 2011 and 2017 has been measured using COSMO-SkyMed (CSK). We found that significant surface displacement rates occurred in the study area varying from -35 mm/year to 10 mm/year. A comparison between temporal and spatial patterns of deformations from our TS-InSAR measurements and different land use types in Guangzhou shows that there is no clear relationship between them. A detailed analysis on the sinkhole collapsed in early 2018 has been conducted, suggesting that surface loading may be a controlling factor of the subsidence, especially along the road and highway. Continuous monitoring of the deforming areas is important in order to minimise the risk of land subsidence and prevention of disasters.
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Masahiro Watanabe, Yu Ozawa, Kenichi Takahashi, Eri Takane, Tetsuya Kimura, Soichiro Suzuki, et al. (2021). Hardware Design and Tests of SMURF V1 Platform for Searching Survivors in Debris Cones. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 849–866). Blacksburg, VA (USA): Virginia Tech.
Abstract: When a large-scale disaster such as earthquake occurs, a huge number of victims will be trapped under debris in a wide area. Rescue activities in debris are technically not easy and endanger the first responders. There are several methods for improving safety and efficiency of rescue operation, but their availability is limited to a certain area or short operating time. Our project called CURSOR is developing tools to comprehensively search victims using a large number of ground-based robots entering debris transported by aerial drones. Here we show the development of the exploration robot collecting information with several sensors. The robot system was designed based on the requirements and performance was evaluated by ruggedization tests and mobility tests. No critical problem was found in the durability, and the mobility showed as the same as the ordinary wheel. To improve the mobility, we are planning to apply a proposed unique track mechanism.
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Samuel Auclair, Pierre Gehl, Mickael Delatre, Christophe Debray, & Philippe Méresse. (2022). In-depth Analysis of Practitioners' Perceptions about Seismic Early Warning Prior to Aftershocks: The Point of View of the USAR Community. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 740–754). Tarbes, France.
Abstract: Urban Search and Rescue (USAR) teams are particularly exposed to the risk of collapse of buildings due to aftershocks, making concept of earthquake early warning (EEW) particularly interesting. In addition to scientific advances in EEW, it is crucial to understand what are the real expectations and needs of USAR teams, and to what extent EEW solutions could meet them. In this study, we conduct a survey to collect insights from USAR rescuers: it highlights that aftershocks are a major concern for them. In this context, we find that the concept of EEW is very favorably received by the respondents, who consider different types of possible actions upon receipt of an early warning. This study provides a basis for the functional specifications of future solutions of EEW useful to all USAR teams, as well as for the definition of their modalities of engagement on the field.
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Sanfilippo, F., & Rañó, I. (2023). Mimicking the Sense of Smell of Search and Rescue (SAR) Dogs: a Bio-inspired Steering Framework for Quadruped Robots. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 892–901). Omaha, USA: University of Nebraska at Omaha.
Abstract: Due to their sense of smell and ability to explore areas for missing people, dogs are valuable for search and rescue (SAR). Canines can discover humans under water, under snow, and even beneath crumbling structures because they can smell human scent. Building unmanned autonomous quadruped robots with canine agility is an attractive step to fully replicate the capabilities of dogs. Robots with legs are already capable of mimicking some of the physical traits of dogs, such as the capacity to traverse rough terrain. However, they would need to replicate also the level of sensory perception of a dog to successfully perform SAR operations. To achieve this, a navigation strategy that uses a direct sensor-motor coupling by following the principles of the Braitenberg vehicles is adopted in this work. This paper represents one of the first steps towards the connection of bio-inspired sensor-based steering mechanisms and bio-inspired locomotion for quadruped robots.
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Shalini Priya, Manish Bhanu, Sourav Kumar Dandapat, & Joydeep Chandra. (2021). Mirroring Hierarchical Attention in Adversary for Crisis Task Identification: COVID-19, Hurricane Irma. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 609–620). Blacksburg, VA (USA): Virginia Tech.
Abstract: A surge of instant local information on social media serves as the first alarming tone of need, supports, damage information, etc. during crisis. Identifying such signals primarily helps in reducing and suppressing the substantial impacts of the outbreak. Existing approaches rely on pre-trained models with huge historic information as well ason domain correlation. Additionally, existing models are often task specific and need auxiliary feature information.Mitigating these limitations, we introduce Mirrored Hierarchical Contextual Attention in Adversary (MHCoA2) model that is capable to operate under varying tasks of different crisis incidents. MHCoA2 provides attention by capturing contextual correlation among words to enhance task identification without relying on auxiliary information.The use of adversarial components and an additional feature extractor in MHCoA2 enhances its capability to achievehigher performance. MHCoA2 reports an improvement of 5-8% in terms of standard metrics on two real worldcrisis incidents over state-of-the-art.
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Xukun Li, Doina Caragea, Cornelia Caragea, Muhammad Imran, & Ferda Ofli. (2019). Identifying Disaster Damage Images Using a Domain Adaptation Approach. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Approaches for effectively filtering useful situational awareness information posted by eyewitnesses of disasters,
in real time, are greatly needed. While many studies have focused on filtering textual information, the research
on filtering disaster images is more limited. In particular, there are no studies on the applicability of domain
adaptation to filter images from an emergent target disaster, when no labeled data is available for the target disaster.
To fill in this gap, we propose to apply a domain adaptation approach, called domain adversarial neural networks
(DANN), to the task of identifying images that show damage. The DANN approach has VGG-19 as its backbone,
and uses the adversarial training to find a transformation that makes the source and target data indistinguishable.
Experimental results on several pairs of disasters suggest that the DANN model generally gives similar or better
results as compared to the VGG-19 model fine-tuned on the source labeled data.
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Jun Zhuang, John Coles, Peiqiu Guan, Fei He, & Xiaojun Shan. (2012). Strategic interactions in disaster preparedness and relief in the face of man-made and natural disasters. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Society is faced with a growing amount of property damage and casualties from man-made and natural disasters. Developing societal resilience to those disasters is critical but challenging. In particular, societal resilience is jointly determined by federal and local governments, private and non-profit sectors, and private citizens. We present a sequence of games among players such as federal, local, and foreign governments, private citizens, and adaptive adversaries. In particular, the governments and private citizens seek to protect lives, property, and critical infrastructure from both adaptive terrorists and non-adaptive natural disasters. The federal government can provide grants to local governments and foreign aid to foreign governments to protect against both natural and man-made disasters. All levels of government can provide pre-disaster preparation and post-disaster relief to private citizens. Private citizens can also make their own investments. The tradeoffs between protecting against man-made and natural disasters – specifically between preparedness and relief, efficiency and equity – and between private and public investment, will be discussed. © 2012 ISCRAM.
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Øyvind Hanssen. (2015). Position Tracking in Voluntary Search and Rescue Operations. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: This paper describes how enthusiasts from the radio-amateur and red-cross communities developed and applied position tracking to search and rescue services in Norway. This was based on the APRS standard which has been used by radio-amateurs for some time.
The document describes how radio-amateurs designed a tracking device which was robust and simple to use along with a web-based online service, a map server, to display positions along with other geographical information on electronic maps. The software for the tracker and the map server is free and open source. This system has been used in a number of search and rescue missions in Norway since 2009, to support decisions making in the command and control centre.
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