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Author (down) Jürgen Moßgraber; Désirée Hilbring; Hylke van der Schaaf; Philipp Hertweck; Efstratios Kontopoulos; Panagiotis Mitzias; Ioannis Kompatsiaris; Stefanos Vrochidis; Anastasios Karakostas pdf  isbn
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  Title The sensor to decision chain in crisis management Type Conference Article
  Year 2018 Publication ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2018  
  Volume Issue Pages 754-763  
  Keywords Sensors, Internet of Things, Knowledge Base, Ontology Visualization, Decision Support, Early Warning  
  Abstract In every disaster and crisis, incident time is the enemy, and getting accurate information about the scope, extent, and impact of the disaster is critical to creating and orchestrating an effective disaster response and recovery effort. Decision Support Systems for disaster and crisis situations need to solve the problem of facilitating the broad variety of sensors available today. This includes the research domain of the Internet of Things and data coming from social media. All this data needs to be aggregated and fused, the semantics of the data needs to be understood and the results must be presented to the decision makers in an accessible way. Furthermore, the interaction and integration with risk and crisis management systems are necessary for a better analysis of the situation and faster reaction times. This paper provides an insight into the sensor to decision chain and proposes solutions and technologies for each step.  
  Address  
  Corporate Author Thesis  
  Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszeski  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-0-692-12760-5 Medium  
  Track Universal Design of ICT in Emergency Management Expedition Conference ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 2148  
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Author (down) Gerasimos Antzoulatos; Panagiotis Giannakeris; Ilias Koulalis; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris pdf  isbn
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  Title A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 75-89  
  Keywords Crisis Management, Real-Time Fire Severity Assessment, Image Recognition, Object Detection, Semantic Segmentation.  
  Abstract Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of risk-based decision support systems which encapsulate the technological achievements in Geographical Information Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand, the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of large amounts of multimedia in social media or other online repositories has increased the interest in the image understanding domain. Recent computer vision techniques endeavour to solve several societal problems with security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist online in social media or news articles a great deal of them might include the existence of a crisis or emergency event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a) an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion level so as to showcase our method's applicability.  
  Address Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH); Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH); Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH)  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-8 ISBN 2411-3394 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes gantzoulatos@iti.gr Approved no  
  Call Number Serial 2209  
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Author (down) Efstratios Kontopoulos; Panagiotis Mitzias; Jürgen Moßgraber; Philipp Hertweck; Hylke van der Schaaf; Désirée Hilbring; Francesca Lombardo; Daniele Norbiato; Michele Ferri; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris pdf  isbn
openurl 
  Title Ontology-based Representation of Crisis Management Procedures for Climate Events Type Conference Article
  Year 2018 Publication ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2018  
  Volume Issue Pages 1064-1073  
  Keywords Crisis management, ontology, semantic integration, decision support, description logics  
  Abstract One of the most critical challenges faced by authorities during the management of a climate-related crisis is the overwhelming flow of heterogeneous information coming from humans and deployed sensors (e.g. cameras, temperature measurements, etc.), which has to be processed in order to filter meaningful items and provide crisis decision support. Towards addressing this challenge, ontologies can provide a semantically unified representation of the domain, along with superior capabilities in querying and information retrieval. Nevertheless, the recently proposed ontologies only cover subsets of the relevant concepts. This paper proposes a more “all-around” lightweight ontology for climate crisis management, which greatly facilitates decision support and merges several pertinent aspects: representation of a crisis, climate parameters that may cause climate crises, sensor analysis, crisis incidents and related impacts, first responder unit allocations. The ontology could constitute the backbone of the decision support systems for crisis management.  
  Address  
  Corporate Author Thesis  
  Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszeski  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-0-692-12760-5 Medium  
  Track 1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT) Expedition Conference ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 2178  
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Author (down) Anastasia Moumtzidou; Marios Bakratsas; Stelios Andreadis; Anastasios Karakostas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris pdf  isbn
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  Title Flood detection with Sentinel-2 satellite images in crisis management systems Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 1049-1059  
  Keywords Floods, Change Detection, Bi-temporal Analysis, Sentinel-2, Deep Neural Networks.  
  Abstract The increasing amount of falling rain may cause several problems especially in urban areas, which drainage system can often not handle this large amount in a short time. Confirming a flooded scene in a timely manner can help the authorities to take further actions to counter the crisis event or to get prepared for future relevant incidents. This paper studies the detection of flood events comparing two successive in time Sentinel-2 images, a method that can be extended for detecting floods in a time-series. For the flood detection, fine-tuned pre-trained Deep Convolutional Neural Networks are used, testing as input different sets of three water sensitive satellite bands. The proposed approach is evaluated against different change detection baseline methods, based on remote sensing. Experiments showed that the proposed method with the augmentation technique applied, improved significantly the performance of the neural network, resulting to an F-Score of 62% compared to 22% of the traditional remote sensing techniques. The proposed method supports the crisis management authority to better estimate and evaluate the flood impact.  
  Address Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece;  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-95 ISBN 2411-3481 Medium  
  Track Using Artificial Intelligence to exploit Satellite Data in Risk and Crisis Management Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes moumtzid@iti.gr Approved no  
  Call Number Serial 2296  
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