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| Author | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel (eds) | ||||
| Title | 18th ISCRAM Conference Proceedings | Type | Conference Volume | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | |||
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| Abstract | The theme of ISCRAM 2021 is ?Embracing the Interdisciplinary Nature of Crisis Management.? These proceedings highlight the range of interdisciplinary research required to understand the design, behavior, and performance of crisis and emergency management systems. We are pleased to present the included papers, which offer excellent contributions on a wide range of topics related to the use of information systems in crisis response and management. |
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| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-1-949373-61-5 | Medium | |
| Track | Proceedings | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | ISCRAM @ idladmin @ | Serial | 2396 | ||
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| Author | Stefan Schauer; Stefan Rass; Sandra König | ||||
| Title | Simulation-driven Risk Model for Interdependent Critical Infrastructures | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 404-415 | ||
| Keywords | risk model, risk assessment, interdependent critical infrastructures, cross-domain simulation | ||||
| Abstract | Critical infrastructures (CIs) in urban areas or municipalities have evolved into strongly interdependent and highly complex networks. To assess risks in this sophisticated environment, classical risk management approaches require extensions to reflect those interdependencies and include the consequences of cascading effects into the assessment. In this paper, we present a concept for a risk model specifically tailored to those requirements of interdependent CIs. We will show how the interdependencies can be reflected in the risk model in a generic way such that the dependencies among CIs on different levels of abstraction can be described. Furthermore, we will highlight how the simulation of cascading effects can be directly integrated to consistently represent the assessment of those effects in the risk model. In this way, the model supports municipalities' decision makers in improving their risk and resilience management of the CIs under their administration. | ||||
| Address |
AIT Austrian Institute of Technology GmbH; System Security Group, Department of Applied Informatics, Universitaet Klagenfurt; Austrian Institute of Technology | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Enhancing Protection of Critical Infrastructures | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | stefan.schauer@ait.ac.at | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2342 | ||
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| Author | Kenneth Johnson; Javier Cámara; Roopak Sinha; Samaneh Madanian; Dave Parry | ||||
| Title | Towards Self-Adaptive Disaster Management Systems | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 49-61 | ||
| Keywords | disaster management, self-adaptive systems, formal verification, probabilistic model checking, constraint solving | ||||
| Abstract | Disasters often occur without warning and despite extensive preparation, disaster managers must take action to respond to changes critical resource allocations to support existing health-care facilities and emergency triages. A key challenge is to devise sound and verifiable resourcing plans within an evolving disaster scenario. Our main contribution is the development of a conceptual self-adaptive system featuring a monitor-analyse-plan-execute (MAPE) feedback loop to continually adapt resourcing within the disaster-affected region in response to changing usage and requirements. We illustrate the system's use on a case study based on Auckland city (New Zealand). Uncertainty arising from partial knowledge of infrastructure conditions and outcomes of human participant's actions are modelled and automatically analysed using formal verification techniques. The analysis inform plans for routing resources to where they are needed in the region. Our approach is shown to readily support multiple model and verification techniques applicable to a range of disaster scenarios. | ||||
| Address |
Auckland University of Technology; University of York; Auckland University of Technology; AUT university; Auckland University of Technology | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | AI and Intelligent Systems for Crises and Risks | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | kenneth.johnson@aut.ac.nz | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2312 | ||
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| Author | Jose J. Gonzalez; Colin Eden; Eirik Abildsnes; Martin Hauge; Monica Trentin; Luca Ragazzoni; Peter Berggren; Carl-Oscar Jonson; Ahmed A. Abdelgawad | ||||
| Title | Elicitation, analysis and mitigation of systemic pandemic risks | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 581-596 | ||
| Keywords | Systemic risk, Cascading effects, Vicious cycles, Risk system analysis, Risk mitigation | ||||
| Abstract | The Covid-19 pandemic has disrupted the health care system and affected all sectors of society, including critical infrastructures. In turn, the impact on society's infrastructures has impacted back on the health care sector. These interactions have created a system of associated risks and outcomes, where the outcomes of risks are risks themselves and where the resulting consequences are complex vicious cycles. Traditional risks assessment methods cannot cope with interdependent risks. This paper describes a novel risk systemicity approach to elicit and mitigate the systemic risks of a major pandemic. The approach employed the internet-based software strategyfinder[TM] in workshops to elicit relevant risk information from sixteen appropriately selected experts from the health care sector and major sectors impacted by and impacting back on the health care sector. The risk information was processed with powerful analytical tools of strategyfinder to allow the experts to prioritise portfolios of strategies attacking the vicious cycles. | ||||
| Address |
Centre for Integrated Emergency Management (CIEM), University of Agder; Strathclyde Business School, Glasgow; University of Agder, Dep. of psychosocial health and Kristiansand municipality, Dep. of research and innovation; Sørlandet Sykehus HF; Center for | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Planning, Foresight and Risk Analysis | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | jose.j.gonzalez@uia.no | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2357 | ||
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| Author | Dimitrios Sainidis; Dimitrios Tsiakmakis; Konstantinos Konstantoudakis; Georgios Albanis; Anastasios Dimou; Petros Daras | ||||
| Title | Single-Handed Gesture UAV Control and Video Feed AR Visualization for First Responders | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 835-848 | ||
| Keywords | First responders, UAV, gesture control, augmented reality | ||||
| Abstract | Unmanned Aerial Vehicles (UAVs) are becoming increasingly widespread in recent years, with numerous applications spanning multiple sectors. UAVs can be of particular benefit to first responders, assisting in both hazard detection and search-and-rescue operations, increasing their situational awareness without endangering human personnel; However, conventional UAV control requires both hands on a remote controller and many hours of training to control efficiently. Additionally, viewing the UAV video-feed on conventional devices (e.g. smartphones) require first responders to glance downwards to look at the screen, increasing the risk of accident. To this end, this work presents a unified system, incorporating single-hand gesture control for UAVs and an augmented reality (AR) visualization of their video feed, while also allowing for backup remote UAV control from any device and multiple-recipient video streaming. A modular architecture allows the upgrade or replacement of individual modules without affecting the whole. The presented system has been tested in the lab, and in field trials by first responders. | ||||
| Address |
Centre for Research & Technology Hellas (CERTH); Centre for Research & Technology Hellas (CERTH); Centre for Research & Technology Hellas (CERTH); Centre for Research & Technology Hellas (CERTH); Centre for Research & Technology Hellas (CERTH); Centre for | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Technologies for First Responders | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | dsainidis@iti.gr | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2377 | ||
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| Author | Hongmin Li; Doina Caragea; Cornelia Caragea | ||||
| Title | Combining Self-training with Deep Learning for Disaster Tweet Classification | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 719-730 | ||
| Keywords | Domain Adaptation, Self-training, Crisis Tweets Classification, BERT, CNN | ||||
| Abstract | Significant progress has been made towards automated classification of disaster or crisis related tweets using machine learning approaches. Deep learning models, such as Convolutional Neural Networks (CNN), domain adaptation approaches based on self-training, and approaches based on pre-trained language models, such as BERT, have been proposed and used independently for disaster tweet classification. In this paper, we propose to combine self-training with CNN and BERT models, respectively, to improve the performance on the task of identifying crisis related tweets in a target disaster where labeled data is assumed to be unavailable, while unlabeled data is available. We evaluate the resulting self-training models on three crisis tweet collections and find that: 1) the pre-trained language model BERTweet is better than the standard BERT model, when fine-tuned for downstream crisis tweets classification; 2) self-training can help improve the performance of the CNN and BERTweet models for larger unlabeled target datasets, but not for smaller datasets. | ||||
| Address |
Department of Computer Science, Kansas State University; Department of Computer Science, Kansas State University; Department of Computer Science, University of Illinois at Chicago | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | hongminli@ksu.edu | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2367 | ||
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| Author | Enrique Caballero; Angel Madridano; Dimitrios Sainidis; Konstantinos Konstantoudakis; Petros Daras; Pablo Flores | ||||
| Title | An automated UAV-assisted 2D mapping system for First Responders | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 890-902 | ||
| Keywords | UAV, drone, 2D Mapping, Swarm, First Responders, Emergency Operations | ||||
| Abstract | Recent advances in the Unmanned Aerial Vehicles (UAVs) sector have allowed such systems to carry a range of sensors, thus increasing their versatility and adaptability to a wider range of tasks and services. Furthermore, the agility of these vehicles allows them to adapt to rapidly changing environments making them an effective tool for emergency situations. A single UAV, or a swarm working in collaboration, can be a handy and helpful tool for First Responders (FRs) during mission planning, mission monitoring, and the tracking of evolving risks. UAVs, with their on-board sensors, can, among other things, capture visual information of the disaster scene in a safe and quick manner, and generate an up-to-date map of the area. This work presents a system for UAV-assisted mapping optimized for FRs, including the generation of routes for the UAVs to follow, data collection and processing, and map generation. | ||||
| Address |
Drone Hopper; Drone Hopper; Centre for Research & Technology, CERTH; Centre for Research & Technology, CERTH; Centre for Research & Technology, CERTH; Drone Hopper | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Technologies for First Responders | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | e.caballero@drone-hopper.com | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2381 | ||
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| Author | Antonio De Nicola; Maria Luisa Villani; Francesco Costantino; Andrea Falegnami; Riccardo Patriarca | ||||
| Title | Knowledge Fusion for Distributed Situational Awareness driven by the WAx Conceptual Framework | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 79-85 | ||
| Keywords | distributed situational awareness, knowledge fusion, WAx framework, crisis management, cyber-socio-technical systems | ||||
| Abstract | Large crisis scenarios involve several actors, acting at the blunt-end of the process, such as rescue team directors, and at the sharp-end, such as firefighters. All of them have different perspectives on the crisis situation, which could be either coherent, alternative or complementary. This heterogeneity of perceptions hinders situational awareness, which is defined as the achievement of an overall picture on the above-mentioned crisis situation. We define knowledge fusion as the process of integrating multiple knowledge entities to produce actionable knowledge, which is consistent, accurate, and useful for the purpose of the analysis. Hence, we present a conceptual modelling approach to gather and integrate knowledge related to large crisis scenarios from locally-distributed sources that can make it actionable. The approach builds on the WAx framework for cyber-socio-technical systems and aims at classifying and coping with the different knowledge entities generated by the involved operators. The conceptual outcomes of the approach are then discussed in terms of open research challenges for knowledge fusion in crisis scenarios. | ||||
| Address |
ENEA; ENEA – CR Casaccia; Sapienza University of Rome; Sapienza University of Rome; Sapienza University of Rome | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | AI and Intelligent Systems for Crises and Risks | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | antonio.denicola@enea.it | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2315 | ||
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| Author | Valentin Barriere; Guillaume Jacquet | ||||
| Title | How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 766-771 | ||
| Keywords | Transformers, Contextual keywords, Humanitarian Computing, Tweets analysis | ||||
| 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. | ||||
| Address |
European Commission's Joint Research Center; European Commission's Joint Research Center | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | valbarrierepro@gmail.com | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2371 | ||
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| Author | Valerio Lorini; Carlos Castillo; Steve Peterson; Paola Rufolo; Hemant Purohit; Diego Pajarito; João Porto de Albuquerque; Cody Buntain | ||||
| Title | Social Media for Emergency Management: Opportunities and Challenges at the Intersection of Research and Practice | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 772-777 | ||
| Keywords | Crisis Informatics, Social Media, Workshop Report, Disaster Management | ||||
| Abstract | This paper summarizes key opportunities and challenges identified during the workshop “Social Media for Disaster Risk Management: Researchers Meet Practitioners” which took place online in November 2020. It constitutes a work-in-progress towards identifying new directions for research and development of systems that can better serve the information needs of emergency managers. Practitioners widely recognize the potential of accessing timely information from social media. Nevertheless, the discussion outlined some critical challenges for improving its adoption during crises. In particular, validating such information and integrating it with authoritative information and into more traditional information systems for emergency managers requires further work, and the negative impacts of misinformation and disinformation need to be prevented. | ||||
| Address |
European Commission, Joint Research Centre (JRC), Ispra, Italy; Universitat Pompeu Fabra, Barcelona, Spain; Community Emergency Response Team, Montgomery County, Maryland, USA; European Commission, Joint Research Centre, Ispra, Italy; George Mason Univers | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | valerio.lorini@ec.europa.eu | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2372 | ||
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| Author | Tiina Ristmae; Dimitra Dionysiou; Miltiadis Koutsokeras; Athanasios Douklias; Eleftherios Ouzounoglou; Angelos Amditis; Anaxagoras Fotopoulos; George Diles; Pantelis Linardatos; Konstantinos Smanis; Pantelis Lappas; Marios Moutzouris; Manolis Tsogas; Dani | ||||
| Title | The CURSOR Search and Rescue (SaR) Kit: an innovative solution for improving the efficiency of Urban SaR Operations | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 867-880 | ||
| Keywords | Urban Search and Rescue, Victim detection, Rescue robotics, Sensors, Situational awareness | ||||
| 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. | ||||
| Address |
Federal Agency for Technical Relief (THW) – Headquarters Staff Unit Research & Innovation Management; Institute of Communication and Computer Systems – National Technical University of Athens; Institute of Communication and Computer Systems – National Tec | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Technologies for First Responders | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Tiina.Ristmaee@thw.de | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2379 | ||
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| Author | Tobias Hellmund; Jürgen Moßgraber; Manfred Schenk; Philipp Hertweck; Hylke van der Schaaf; Hans Springer | ||||
| Title | The Design and Implementation of ZEUS: Novel Support in Managing Large-Scale Evacuations | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 1003-1014 | ||
| Keywords | Management of Large-Scale Evacuations, Emergency Accommodation Management, Evacuation Management | ||||
| Abstract | This paper introduces ZEUS, a novel software tool for the management of large-scale evacuations. The tasks ZEUS supports were derived from two Standard Operating Procedures, developed on demand of the German federal states. To this date, the authors are not aware of another software tool that gives technical support to the management and control of large-scale evacuations as ZEUS does. It comprises functionalities to (pre-)plan a large-scale evacuation, as well as functions for the management of the flow of evacuees during an evacuation situation. This paper describes how the requirements of ZEUS were derived from the two named planning frameworks and how use-cases were developed to meet the requirements; these use-cases were conceptualized as different steps of a workflow. In an evaluation, the paper gives credit how ZEUS can provide technical support for the evaluation of large-scale evacuations. ZEUS will undergo a two-staged review process: first, a controlled theoretical scenario is tested and, upon successful completion, a practical test on a large scale will be executed. | ||||
| Address |
Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Ministry of Interior, Digitization, and Migration Baden-Württemberg | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Other | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | tobias.hellmund@iosb.fraunhofer.de | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2392 | ||
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| Author | Yudi Chen; Angel Umana; Chaowei Yang; Wenying Ji | ||||
| Title | Condition Sensing for Electricity Infrastructures in Disasters by Mining Public Topics from Social Media | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 598-608 | ||
| Keywords | social media, infrastructure resilience, human behaviors, disaster response | ||||
| 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. | ||||
| Address |
George Mason University; George Mason University; George Mason University; George Mason University | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | wji2@gmu.edu | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2358 | ||
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| Author | Yitong Li; Duoduo Liao; Jundong Li; Wenying Ji | ||||
| Title | Automated Generation of Disaster Response Networks through Information Extraction | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 431-438 | ||
| Keywords | Disaster response, Stakeholder collaboration, Natural language processing, Network generation | ||||
| Abstract | Following a disaster, maintaining and restoring community lifelines require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating stakeholder collaboration networks (SCNs), this research proposes a systematic approach for reliably extracting stakeholder collaboration information from texts and automatically generating SCNs. In the proposed approach, stakeholders and their interactions are automatically extracted from texts through a natural language processing technique--Named Entity Recognition. Once extracted, the collaboration information is stored into structured datasets to automate the generation of SCNs. A case study on stakeholder collaboration in response to Hurricane Harvey is used to demonstrate the feasibility and applicability of the proposed approach. Overall, the proposed approach achieves the reliable and automated generation of SCNs from texts, which largely reduces practitioners' interpretation loads and eases the data collection process. | ||||
| Address |
George Mason University; George Mason University; University of Virginia; George Mason University | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Enhancing Resilient Response in Inter-organizational Contexts | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | wji2@gmu.edu | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2344 | ||
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| Author | Yasas Senarath; Jennifer Chan; Hemant Purohit; Ozlem Uzuner | ||||
| Title | Evaluating the Relevance of UMLS Knowledge Base for Public Health Informatics during Disasters | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 97-105 | ||
| Keywords | Public Health, Disaster Informatics, Health Informatics, UMLS, Metathesaurus | ||||
| 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. | ||||
| Address |
George Mason University; Northwestern University; George Mason University; George Mason University | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | AI and Intelligent Systems for Crises and Risks | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | ywijesu@gmu.edu | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2317 | ||
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| Author | Jens Kersten; Jan Bongard; Friederike Klan | ||||
| Title | Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 744-754 | ||
| Keywords | Information Overload Reduction, Semantic Clustering, Crisis Informatics, Twitter Stream | ||||
| Abstract | Twitter is an immediate and almost ubiquitous platform and therefore can be a valuable source of information during disasters. Current methods for identifying and classifying crisis-related content are often based on single tweets, i.e., already known information from the past is neglected. In this paper, the combination of tweet-wise pre-trained neural networks and unsupervised semantic clustering is proposed and investigated. The intention is to (1) enhance the generalization capability of pre-trained models, (2) to be able to handle massive amounts of stream data, (3) to reduce information overload by identifying potentially crisis-related content, and (4) to obtain a semantically aggregated data representation that allows for further automated, manual and visual analyses. Latent representations of each tweet based on pre-trained sentence embedding models are used for both, clustering and tweet classification. For a fast, robust and time-continuous processing, subsequent time periods are clustered individually according to a Chinese restaurant process. Clusters without any tweet classified as crisis-related are pruned. Data aggregation over time is ensured by merging semantically similar clusters. A comparison of our hybrid method to a similar clustering approach, as well as first quantitative and qualitative results from experiments with two different labeled data sets demonstrate the great potential for crisis-related Twitter stream analyses. | ||||
| Address |
German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Departmen | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | jens.kersten@dlr.de | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2369 | ||
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| Author | Alexander Gabriel; Babette Tecklenburg; Yann Guillouet; Frank Sill Torres | ||||
| Title | Threat analysis of offshore wind farms by Bayesian networks – a new modeling approach | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 174-185 | ||
| Keywords | Threat analysis, Bayesian networks, process modeling, Critical infrastructurs | ||||
| Abstract | As a result of the ongoing commitment to climate protection in more and more countries and the corresponding expansion of renewable energies, the importance of renewables for the security of electricity supply is also increasing. Wind energy generated in offshore wind farms already accounts for a significant share of the energy mix and will continue to grow in the future. Therefore, approaches and models for security assessment and protection against threats are also needed for these infrastructures. Due to the special characteristics and geographical location of offshore wind farms, they are confronted with particular challenges. In this context, this contribution outlines how an approach for threat analysis of offshore wind farms is to be developed within the framework of the new research project “ARROWS” of the German Aerospace Center. The authors first explain the structure of offshore wind farms and then present a possible modeling approach using Qualitative function models and Bayesian networks. | ||||
| Address |
German Aerospace Center – Institute for the Protection of Maritime Infrastructures; German Aerospace Center – Institute for the Protection of Maritime Infrastructures; German Aerospace Center – Institute for the Protection of Maritime Infrastructures; Ger | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Analytical Modeling and Simulation | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Alexander.Gabriel@dlr.de | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2323 | ||
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| Author | Jelle Groenendaal; Ira Helsloot | ||||
| Title | Why Technology Not Always Adds Value to Crisis Managers During Crisis: The Case of The Dutch Nation-Wide Crisis Management System LCMS | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 936-945 | ||
| Keywords | Crisis, NDM, Technology, LCMS | ||||
| Abstract | Technology undeniably plays an important role in supporting crisis managers to respond to crisis. However, when improperly designed or used, technology can be ineffective or even be detrimental to the crisis response. Therefore, in this paper we bring together insights from the scientific literature and identify 5 principles for the design and use of technology to aid crisis managers effectively. These principles might seem trivial but there are several examples of technology used in practice that show the opposite. To illustrate this, we use as a case study the Dutch nation-wide crisis management system LCMS which is used in the Netherlands by all safety regions and other public organizations to maintain and share a common operational picture supporting large-scale crisis management collaboration. We explain why crisis evaluations and research time and again show that LCMS has failed to add value for crisis managers during crisis by using the identified principles. | ||||
| Address |
Hague University of Applied Sciences; Radboud University Nijmegen | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Usability and Universal Design of ICT for Emergency Management | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | j.groenendaal@hhs.nl | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2386 | ||
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| Author | Oussema Ben Amara; Daouda Kamissoko; Frédérick Benaben; Ygal Fijalkow | ||||
| Title | Hardware architecture for the evaluation of BCP robustness indicators through massive data collection and interpretation | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 71-78 | ||
| Keywords | Business Continuity Plan, Social sciences, Risk Management, Robustness, Embedded Hardware | ||||
| Abstract | Recently, the concept of robustness measurement has become clearly important especially with the rise of risky events such as natural disasters and mortal pandemics. In this context, this paper proposes an overview of a hardware architecture for massive data collection in the aim of evaluating robustness indicators. This paper essentially addresses the theoretical and general problems that the scientific research is seeking to address in this area, offers a literature review of what already exists and, based on preliminary diagnosis of what the literature has, presents a new approach and some of the targeted findings with a focus on the leading aspects, having a primary objective of explaining the multiple aspects of this research work. | ||||
| Address |
IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse; INU Champollion, University of Toulouse | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | AI and Intelligent Systems for Crises and Risks | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | oussema.ben_amara@mines-albi.fr | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2314 | ||
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| Author | Julien Coche; Jess Kropczynski; Aurélie Montarnal; Andrea Tapia; Frédérick Bénaben | ||||
| Title | Actionability in a Situation Awareness world: Implications for social media processing system design | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 994-1001 | ||
| Keywords | Actionable Information, Situation Awareness, Social Media, Crisis Management | ||||
| Abstract | The field of crisis informatics now has a decade-long history of designing tools that leverage social media to support decision-makers situation awareness. Despite this history, there remains few examples of these tools adopted by practitioners. Recent fieldwork with public safety answering points and first responders has led to an awareness of the need for tools that gather actionable information, rather than situational awareness alone. This paper contributes to an ongoing discussion about these concepts by proposing a model that embeds the concept of actionable information into Endsley's model of situation awareness. We also extend the insights of this model to the design implications of future information processing systems. | ||||
| Address |
IMT Mines Albi; University of Cincinnati; Ecole des Mines d'Albi Carmaux; The Pennsylvania State University; Ecole des Mines d'Albi-Carmaux | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Visions for Future Crisis Management | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | coche.emac@gmail.com | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2391 | ||
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| Author | Shalini Priya; Manish Bhanu; Sourav Kumar Dandapat; Joydeep Chandra | ||||
| Title | Mirroring Hierarchical Attention in Adversary for Crisis Task Identification: COVID-19, Hurricane Irma | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 609-620 | ||
| Keywords | Covid-19, Hurricane, Adversarial, Hierarchical attention, Support, Infrastructure Damage | ||||
| 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. | ||||
| Address |
Indian Institute of Technology Patna; Indian Institute of Technology Patna; Indian Institute of Technology Patna; Indian Institute of Technology Patna | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | shalini.pcs16@iitp.ac.in | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2359 | ||
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| Author | Xiaojing Guo; Xinzhi Wang; Luyao Kou; Hui Zhang | ||||
| Title | A Question Answering System Applied to Disasters | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 2-16 | ||
| Keywords | Emergency Management, Disaster, Natural Language Processing, Deep Learning | ||||
| Abstract | In emergency management, identifying disaster information accurately and promptly out of numerous documents like news articles, announcements, and reports is important for decision makers to accomplish their mission efficiently. This paper studies the application of the question answering system which can automatically locate answers in the documents by natural language processing to improve the efficiency and accuracy of disaster knowledge extraction. Firstly, an improved question answering model was constructed based on the advantages of the existing neural network models. Secondly, the English question answering dataset pertinent to disasters and the Chinese question answering dataset were constructed. Finally, the improved neural network model was trained on the datasets and tested by calculating the F1 and EM scores which indicated that a higher question answering accuracy was achieved. The improved system has a deeper understanding of the semantic information and can be used to construct the disaster knowledge graph. | ||||
| Address |
Institute of Public Safety Research, Tsinghua University; School of Computer Engineering and Science, Shanghai University; Institute of Public Safety Research, Tsinghua University; Institute of Public Safety Research, Tsinghua University | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | AI and Intelligent Systems for Crises and Risks | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | gxj19@mails.tsinghua.edu.cn | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2308 | ||
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| Author | Yohann Chasseray; Anne-Marie Barthe-Delanoë; Stéphane Négny; Jean-Marc Le Lann | ||||
| Title | Automated unsupervised ontology population system applied to crisis management domain | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 968-981 | ||
| Keywords | Automated knowledge extraction, Crisis management systems, Ontologies, Experience feedback exploitation, Background knowledge acquisition | ||||
| Abstract | As crisis are complex systems, providing an accurate response to an ongoing crisis is not possible without ensuring situational awareness. The ongoing works around knowledge management and ontologies provide relevant and machine readable structures towards situational awareness and context understanding. Many metamodels, that can be derived into ontologies, supporting the collect and organization of crucial information for Decision Support Systems have been designed and are now used on specific cases. The next challenge into crisis management is to provide tools that can process an automated population of these metamodels/ontologies. The aim of this paper is to present a strategy to extract concept-instance relations in order to feed crisis management ontologies. The presented system is based on a previously proposed generic metamodel for information extraction and is applied in this paper to three different case studies representing three different crisis namely Ebola sanitarian crisis, Fukushima nuclear crisis and Hurricane Katrina natural disaster. | ||||
| Address |
Laboratoire de Génie Chimique, Universitéde Toulouse, CNRS, INPT, UPS, Toulouse,France; Centre Génie Industriel, Université deToulouse, IMT Mines Albi, France; Laboratoire de Génie Chimique, Universitéde Toulouse, CNRS, INPT, UPS, Toulouse,France; Laborat | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Visions for Future Crisis Management | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | yohann.chasseray@inp-toulouse.fr | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2389 | ||
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| Author | Sofie Pilemalm; Jaziar Radianti; Bjørn Erik Munkvold; Tim A. Majchrzak; Kristine Steen-Tveit | ||||
| Title | Turning Common Operational Picture Data into Double-loop Learning from Crises – can Vision Meet Reality? | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 417-430 | ||
| Keywords | Common operational picture, situation awareness, double-loop learning, crisis management, map-based evaluation | ||||
| Abstract | This study proposes a framework for double-loop learning from crises, using common operational pictures (COP). In most crises, a COP is of outmost importance to gain a common understanding among inter-organizational response. A COP is typically expressed through a map visualization. While the technologies to support COP progress rapidly, the corresponding practice of evaluating the COP and situational awareness is not yet established. Tools that enable responders to learn after the crisis, look back in time on the COP devel-opment and detect the barriers that prevent the COP establishment, still seem absent. Double-loop learning is an organizational practice to learn from previous actions widely adopted in the safety domain, and lately used in crisis management. This paper addresses the perceived gap by presenting the technical, organizational and structural requirements derived from document analysis, observation, and a workshop with multiple crisis management stakeholders, and integrating them to an initial framework. | ||||
| Address |
Linköping university; University of Agder; University of Agder; University of Agder; University of Agder | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Enhancing Resilient Response in Inter-organizational Contexts | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | sofie.pilemalm@liu.se | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2343 | ||
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| Author | Nilani Algiriyage; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston; Minura Punchihewa; Santhoopa Jayawardhana | ||||
| Title | Towards Real-time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 40-48 | ||
| Keywords | Computer Vision, Traffic Flow, YOLOv4, CCTV Big Data | ||||
| Abstract | Traffic emergencies and resulting delays cause a significant impact on the economy and society. Traffic flow estimation is one of the early steps in urban planning and managing traffic infrastructure. Traditionally, traffic flow rates were commonly measured using underground inductive loops, pneumatic road tubes, and temporary manual counts. However, these approaches can not be used in large areas due to high costs, road surface degradation and implementation difficulties. Recent advancement of computer vision techniques in combination with freely available closed-circuit television (CCTV) datasets has provided opportunities for vehicle detection and classification. This study addresses the problem of estimating traffic flow using low-quality video data from a surveillance camera. Therefore, we have trained the novel YOLOv4 algorithm for five object classes (car, truck, van, bike, and bus). Also, we introduce an algorithm to count the vehicles using the SORT tracker based on movement direction such as ``northbound'' and ``southbound'' to obtain the traffic flow rates. The experimental results, for a CCTV footage in Christchurch, New Zealand shows the effectiveness of the proposed approach. In future research, we expect to train on large and more diverse datasets that cover various weather and lighting conditions. | ||||
| Address |
Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington; University of Kelaniya; Univerity of Kelaniya | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | AI and Intelligent Systems for Crises and Risks | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | rangika.nilani@gmail.com | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2311 | ||
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