Alexander Almer, Thomas Schnabel, Johann Raggam, Armin Köfler, Roland Wack, & Richard Feischl. (2015). Airborne multi-sensor management support system for emergency teams in natural disasters. 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 the development of a multi-functional airborne management support system within the frame of the Austrian national safety and security research programme. The objective was to assist crisis management tasks of emergency teams and armed forces in disaster management by providing multi spectral, near real-time airborne image data products. As time, flexibility and reliability as well as objective information are crucial aspects in emergency management, the used components are tailored to meet these requirements. This article includes the individual system components as well as their performance using examples from lab tests and real-life deployments. Based on this, the impact of existing command and control processes as well as the benefits for time critical decision making processes are described based on expertise of the involved end users. In addition, it gives an outlook on future perspectives.
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Anouck Adrot, Samuel Auclair, Julien Coche, Audrey Fertier, Cécile Gracianne, & Aurélie Montarnal. (2022). Using Social Media Data in Emergency Management: A Proposal for a Socio-technical Framework and a Systematic Literature Review. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 470–479). Tarbes, France.
Abstract: Data represents an essential resource to the management of emergencies: organizations have been growingly investing in technologies and resources to lever data as an asset before, during, and after disasters and emergencies. However, research on data usage in emergency management remains fragmented, preventing practitioners and scholars from approaching data comprehensively. To address this gap, this research in progress consists of a systematic review of the literature in a two-steps approach: we first propose a socio-technical framework and use it in an exploratory mapping of the main topics covered by the literature. Our preliminary findings suggest that research on data usage primarily focuses on technological opportunities and affordances and, hence, lacks practical implementation aspects in organizations. The expected contribution is double. First, we contribute to a more comprehensive understanding of data usage in emergency management. Second, we propose future avenues for research on data and resilience.
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Anne Marie Barthe, Sabine Carbonnel, Frédérick Bénaben, & Hervé Pingaud. (2012). Event-driven agility of crisis management collaborative processes. 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: This article aims at presenting a whole approach of Information Systems interoperability management in a crisis management cell. We propose a Mediation Information System (MIS) to help the crisis cell partners to design, run and manage the workflows of the response to a crisis situation. The architecture of the MIS meets the need of low coupling between the partners' Information System components and the need of agility for a such platform. Based on the Service Oriented Architecture (SOA) and the Event Driven Architecture (EDA) principles which, combined to the Complex Event Processing (CEP) principles, it will leads to an easier orchestration, choreography and real-time monitoring of the workflows' activities, and even allows the automated agility of the crisis response on-the-fly-we consider agility as the ability of the processes to remain consistent with the response to the crisis-. © 2012 ISCRAM.
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Anne Marie Barthe, Sébastien Truptil, & Frédérick Bénaben. (2014). Agility of crisis response: Gathering and analyzing data through an event-driven platform. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 250–254). University Park, PA: The Pennsylvania State University.
Abstract: The goal of this article is to introduce a platform (called Agility Service) that gathers and analyses data coming from both crisis response and crisis field by using the principles of Complex Event Processing. As a crisis situation is an unstable phenomenon (by nature or by effect of the applied response), the crisis response may be irrelevant after a while: lack of resources, arrival of a new stakeholder, unreached objectives, over-crisis, etc. Gathering data, analyze and aggregate it to deduce relevant information concerning the current crisis situation, and making this information available to the crisis cell to support decision making: these are the purposes of the described platform. A use case based on the Fukushima's nuclear accident is developed to illustrate the use of the developed prototype.
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Anne Marie Barthe, Frédérick Bénaben, Sébastien Truptil, & Hervé Pingaud. (2013). A flexible network of sensors: Case study. 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. 344–348). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The goal of this article is to introduce a plastic architecture of a survey system dedicated to any kind of geographical area that requires to be observed. The principle of this architecture is to allow to change dynamically the set of sensors that is used to monitor the area and also to provide an analyze system able to deal with this unstable set of sensors. Based on Event-Driven Architecture (EDA) technology, such a system does not provide new features compared with traditional set of static sensors connected through cables to dozens of bulbs lighting when a predefined subset of measures is not in the expected range. However, the introduced architecture provides a completely agile and dynamic system of measurement where neither the network of sensors nor the system of measure interpretation is static.
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Bruce D. Campbell, Konrad E. Schroder, & Chris E. Weaver. (2010). RimSim visualization : An interactive tool for post-event sense making of a first response effort. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Upon developing a software agent-based simulator for training roles in emergency response scenarios, the PARVAC team at the University of Washington has pursued building a tool for better investigative review and insight generation on the performance of an emergency response game session team. While our RimSim Response software included the opportunity to re-run a simulated team performance in order to review player and agent behavior, we did not provide our trainees the ability to visually query their performance outside of a sequential review of the emergency response effort. By integrating our RSR visualization components with an existing visual query software package called Improvise, we were able to construct highly-coordinated visualizations of our data model for the ability to apply a sense making approach in the investigation of live player and software agent-based behavior – both as individual players and as combinations of players working on tasks associated with an emergency response scenario. The resultant tool is now our primary visualization tool for discussing first responder team performance and supports the overall RSR objective of training teams to make the most effective, recognition-primed decisions when a real emergency crisis occurs in their community. This paper reviews our visualization tool and demonstrates its use.
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Cornelia Caragea, Nathan McNeese, Anuj Jaiswal, Greg Traylor, Hyun-Woo Kim, Prasenjit Mitra, et al. (2011). Classifying text messages for the haiti earthquake. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In case of emergencies (e.g., earthquakes, flooding), rapid responses are needed in order to address victims' requests for help. Social media used around crises involves self-organizing behavior that can produce accurate results, often in advance of official communications. This allows affected population to send tweets or text messages, and hence, make them heard. The ability to classify tweets and text messages automatically, together with the ability to deliver the relevant information to the appropriate personnel are essential for enabling the personnel to timely and efficiently work to address the most urgent needs, and to understand the emergency situation better. In this study, we developed a reusable information technology infrastructure, called Enhanced Messaging for the Emergency Response Sector (EMERSE), which classifies and aggregates tweets and text messages about the Haiti disaster relief so that non-governmental organizations, relief workers, people in Haiti, and their friends and families can easily access them.
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Congcong Wang, Paul Nulty, & David Lillis. (2021). Transformer-based Multi-task Learning for Disaster Tweet Categorisation. 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. 705–718). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can contribute to the situational awareness of emergency responders, who have a need for them to be categorised according to information types (i.e. the type of aid services the messages are requesting). We introduce a transformer-based multi-task learning (MTL) technique for classifying information types and estimating the priority of these messages. We evaluate the effectiveness of our approach with a variety of metrics by submitting runs to the TREC Incident Streams (IS) track: a research initiative specifically designed for disaster tweet classification and prioritisation. The results demonstrate that our approach achieves competitive performance in most metrics as compared to other participating runs. Subsequently, we find that an ensemble approach combining disparate transformer encoders within our approach helps to improve the overall effectiveness to a significant extent, achieving state-of-the-art performance in almost every metric. We make the code publicly available so that our work can be reproduced and used as a baseline for the community for future work in this domain.
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Kelli de Faria Cordeiro, Maria Luiza M Campos, & Marcos R. S. Borges. (2014). Adaptive integration of information supporting decision making: A case on humanitarian logistic. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 225–229). University Park, PA: The Pennsylvania State University.
Abstract: There is an urgent demand for information systems to gather heterogeneous information about needs, donations and warehouse stocks to provide an integrated view for decision making in humanitarian logistics. The dynamic flow of information, due to the unpredicted events, requires adaptive features. The traditional relational data model is not suitable due to its schema rigidity. As an alternative, Graph Data models complemented by semantic representations, like Linked Open Data on the Web, can be used. Based on both, this research proposes an approach for the adaptive integration of information and an associated architecture. An application example is discussed in a real scenario where relief goods are managed through a dynamic and multi-perspective view.
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Dat T. Nguyen, Firoj Alam, Ferda Ofli, & Muhammad Imran. (2017). Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 499–511). Albi, France: Iscram.
Abstract: The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly. In addition to the textual content, people post overwhelming amounts of imagery data on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in the computer vision field, automatic processing of the crisis-related social media imagery data remains a challenging task. It is because a majority of which consists of redundant and irrelevant content. In this paper, we present an image processing pipeline that comprises de-duplication and relevancy filtering mechanisms to collect and filter social media image content in real-time during a crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources.
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Tom De Groeve, & Patrick Riva. (2009). Early flood detection and mapping for humanitarian response. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Space-based river monitoring can provide a systematic, timely and impartial way to detect floods of humanitarian concern. This paper presents a new processing method for such data, resulting in daily flood magnitude time series for any arbitrary observation point on Earth, with lag times as short as 4h. Compared with previous work, this method uses image processing techniques and reduces the time to obtain a 6 year time series for an observation site from months to minutes, with more accurate results and global coverage. This results in a daily update of major floods in the world, with an objective measure for their magnitude, useful for early humanitarian response. Because of its full coverage, the grid-based technique also allows the automatic creation of low-resolution flood maps only hours after the satellite passes, independent of cloud coverage.
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Anton Donner, Thomas Greiner-Mai, & Christine Adler. (2012). Challenge patient dispatching in mass casualty incidents. 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: Efficient management of mass casualty incidents is complex, since regular emergency medical services structures have to be switched to a temporary “disaster mode” involving additional operational and tactical structures. Most of the relevant decisions have to be taken on-site in a provisional and chaotic environment. Data gathering about affected persons is one side of the coin; the other side is on-site patient dispatching requiring information exchange with the regular emergency call center and destination hospitals. In this paper we extend a previous conference contribution about the research project e-Triage to the aspect of patient data and on-site patient dispatching. Our considerations reflect the situation in Germany, which deserves from our point of view substantial harmonization. © 2012 ISCRAM.
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Firoj Alam, Ferda Ofli, & Muhammad Imran. (2019). CrisisDPS: Crisis Data Processing Services. 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: Over the last few years, extensive research has been conducted to develop technologies to support humanitarian aid
tasks. However, many technologies are still limited as they require both manual and automatic approaches, and
more importantly, are not ready to be integrated into the disaster response workflows. To tackle this limitation, we
develop automatic data processing services that are freely and publicly available, and made to be simple, efficient,
and accessible to non-experts. Our services take textual messages (e.g., tweets, Facebook posts, SMS) as input to
determine (i) which disaster type the message belongs to, (ii) whether it is informative or not, and (iii) what type of
humanitarian information it conveys. We built our services upon machine learning classifiers that are obtained from
large-scale comparative experiments utilizing both classical and deep learning algorithms. Our services outperform
state-of-the-art publicly available tools in terms of classification accuracy.
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Firoj Alam, Ferda Ofli, Muhammad Imran, & Michael Aupetit. (2018). A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 553–572). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.
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Siska Fitrianie, & Leon J.M. Rothkrantz. (2007). An automated crisis online dispatcher. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 525–536). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: An experimental automated dialogue system that plays the role of a crisis hotline dispatcher is currently developed. Besides controlling the communication flow, this system is able to retrieve information about crisis situations from user's input. It offers a natural user interaction by the ability to perceive and respond to human emotions. The system has an emotion recognizer that is able to recognize the emotional loading from user's linguistic content. The recognizer uses a database that contains selected keywords on a 2D “arousal” and “valence” scale. The output of the system provides not only the information about the user's emotional state but also an indication of the urgency of his/her information regarding to crisis. The dialogue system is able to start a user friendly dialogue, taking care of the content, context and emotional loading of user's utterances.
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Jörn Franke, Adam Widera, François Charoy, Bernd Hellingrath, & Cédric Ulmer. (2011). Reference process models and systems for inter-organizational ad-hoc coordination – Supply chain management in humanitarian operations. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In this work we present a general framework for process-oriented coordination and collaboration in humanitarian operations. Process management has been proven useful in many business domains, but humanitarian operations and disaster response management in general require different process management approaches. Related work has only recently introduced traditional process management approaches for emergency management. These traditional approaches have several limitations with respect to the domain of humanitarian operations and disaster management. Our approach points to design, run-time and monitoring of inter-organizational humanitarian logistics processes. It consists of two parts: A reference model for humanitarian logistics tasks and a system for ad-hoc process management of these tasks. We discuss how they can be integrated to provide additional benefits.
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Gkika, I., Pattas, D., Konstantoudakis, K., & Zarpalas, D. (2023). Object detection and augmented reality annotations for increased situational awareness in light smoke conditions. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 231–241). Omaha, USA: University of Nebraska at Omaha.
Abstract: Innovative technologies powered by Computer Vision algorithms can aid first responders, increasing their situ ational awareness. However, adverse conditions, such as smoke, can reduce the efficacy of such algorithms by degrading the input images. This paper presents a pipeline of image de-smoking, object detection, and augmented reality display that aims to enhance situational awareness in smoky conditions. A novel smoke-reducing deep learning algorithm is applied as a preprocessing step, before state-of-the-art object detection. The detected objects and persons are highlighted in the user’s augmented reality display. The proposed method is shown to increase detection accuracy and confidence. Testing in realistic environments provides an initial evaluation of the method, both in terms of image processing and of usefulness to first responders.
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Anna Gryszkiewicz, & Fang Chen. (2010). Design requirements for information sharing in a crisis management command and control centre. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Good support for information sharing and processing is essential for successful crisis management. A crisis manager handles information from many different sources and collaborates with many different actors. This study is therefore focusing on specifying some needs and requirements for information support systems for crisis management command and control centres. The study is based on case studies and interviews in Sweden with the aim to understand how information processing best can be supported from a crisis manager's perspective. The needs and requirements found in the study can be used in future system design or improvement.
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Heather M. Fuchs, Norbert Steigenberger, & Thomas Lübcke. (2015). Intuition or deliberation ? How do professionals make decisions in action? 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: Despite intense research on decision-making in action, we still know little about when decision-makers rely on deliberate vs. intuitive decision-making in decision situations under complexity and uncertainty. This paper studies decision-making modes (deliberate vs. intuitive) in complex task environments contingent on perceived complexity, experience, and decision style preference. We find that relatively inexperienced decision-makers respond to increases in subjective complexity with an increase in deliberation and tend to follow their decision style preference. Experienced decision-makers are less guided by their decision preference and respond to increases in subjective complexity only minimally. Our paper contributes to a developing stream of research linking decision-making with intra-personal and environmental properties and fosters our understanding of the conditions under which decision-makers rely on intuitive vs. deliberate decision modes. In doing so, we go one step further towards a comprehensive theory of decision-making in action.
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Rajesh M. Hegde, B.S. Manoj, Bashkar D. Rao, & Ramesh R. Rao. (2006). Emotion detection from speech signals and its applications in supporting enhanced QoS in emergency response. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 82–91). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Networking in the event of disasters requires new hybrid wireless architectures such as Wireless Mesh Networks (WMNs). Provisioning Quality of Service (QoS) in such networks which are quickly deployed during emergencies demand radical solutions. In this paper, we provide a new QoS approach for voice calls over a wireless mesh networks during emergency situations. According to our scheme, the contention and back-off parameters are modified based on the emotion content in the voice streams. This paper also looks at methods for detecting emotion from an incoming voice call using the speech signal. The issues of interest in such situations are whether the caller is in a state of extreme panic, moderate panic, or in a normal state of behavior. The communication network behavior should be modified to provide differentiated QoS for calls based on the degree of emotion. We use several features extracted from the speech signal like the range of pitch variation, energy in the critical bark band, range of the first three formant variations, and speaking rate among others to discriminate between the three emotional states. At the back end the Gaussian mixture modeling techniques is used to model the three emotional states of the speaker. Since a large number of features increase the computational complexity and time, a feature selection technique is employed based on the Bhattacharya distance, to select the set of features that give maximum discrimination between the classes. These set of features are employed to simulate an emotion recognition system. The results indicate a promising emotion detection rate for the three emotions. We also present the early results on detecting the emotion content in the speech and using this in the MAC layer differentiated QoS provisioning scheme. Our scheme provides an end-to-end delay performance improvement for panicked calls as high as 60% compared to normal calls.
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Starr Roxanne Hiltz, & Linda Plotnick. (2013). Dealing with information overload when using social media for emergency management: Emerging solutions. 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. 823–827). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Several recent studies point the way to enabling emergency response managers to be able to find relevant posts and incorporate them into their sensemaking and decision making processes. Among the approaches that have improved the ability to find the most relevant information are the social conventions of creating topic groups and tags and of “retweeting;” the use of trained volunteers to filter and summarize posts for responders; automated notifications of trending topics; natural language processing of posts; techniques for identifying posts from the disaster site; and the use of GIS and crisis maps to visually represent the distribution of incidents.
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Hristo Tanev, Vanni Zavarella, & Josef Steinberger. (2017). Monitoring disaster impact: detecting micro-events and eyewitness reports in mainstream and social media. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 592–602). Albi, France: Iscram.
Abstract: This paper approaches the problem of monitoring the impact of the disasters by mining web sources for the events, caused by these disasters. We refer to these disaster effects as “micro-events”. Micro-events typically following a large disaster include casualties, damage on infrastructures, vehicles, services and resource supply, as well as relief operations. We present natural language grammar learning algorithms which form the basis for building micro-event detection systems from data, with no or minor human intervention, and we show how they can be applied to mainstream news and social media for monitoring disaster impact. We also experimented with applying statistical classifiers to distill, from social media situational updates on disasters, eyewitness reports from directly affected people. Finally, we describe a Twitter mining robot, which integrates some of these monitoring techniques and is intended to serve as a multilingual content hub for enhancing situational awareness.
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Hussein Mouzannar, Yara Rizk, & Mariette Awad. (2018). Damage Identification in Social Media Posts using Multimodal Deep Learning. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 529–543). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media has recently become a digital lifeline used to relay information and locate survivors in disaster situations. Currently, officials and volunteers scour social media for any valuable information; however, this approach is implausible as millions of posts are shared by the minute. Our goal is to automate actionable information extraction from social media posts to efficiently direct relief resources. Identifying damage and human casualties allows first responders to efficiently allocate resources and save as many lives as possible. Since social media posts contain text, images and videos, we propose a multimodal deep learning framework to identify damage related information. This framework combines multiple pretrained unimodal convolutional neural networks that extract features from raw text and images independently, before a final classifier labels the posts based on both modalities. Experiments on a home-grown database of labeled social media posts showed promising results and validated the merits of the proposed approach.
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James Hilton, & Nikhil Garg. (2023). Rapid Geospatial Processing for Hazard and Risk Management using the Geostack Framework. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 2–7). Palmerston North, New Zealand: Massey Unversity.
Abstract: Operational predictive and risk modelling of landscape-scale hazards such as floods and fires requires rapid processing of geospatial data, fast model execution and efficient data delivery. However, geospatial data sets required for hazard prediction are usually large, in a variety of different formats and usually require a complex pre-processing toolchain. In this paper we present an overview of the Geostack framework, which has been specifically designed for this task using a newly developed software library. The platform aims to provide a unified interface for spatial and temporal data sets, deliver rapid processing through OpenCL and integrate with web APIs or external graphical user interface systems to display and deliver results. We provide examples of hazard and risk use cases, particularly Spark, a Geostack based system for predicting the spread of wildfires. The framework is open-source and freely available to end users and practitioners in the hazard and geospatial space.
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Simon Jirka, Daniel Nüst, & Benjamin Proß. (2013). Sensor web and web processing standards for crisis management. 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. 376–380). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: This paper introduces the latest state of the interoperable Sensor Web and Web Processing standards specified by the Open Geospatial Consortium. Based on these components it becomes possible to share, integrate and analyse observation data across political and administrative borders as well as across multiple thematic domains. We present the 52°North open source implementations of the OGC SWE and WPS standards and introduce an outlook how this technology could be applied in the field of crisis management. Thus, this paper aims at providing a perspective how currently existing technology can be combined and applied to solve problems in emergency management rather than describing an already finished product. Special consideration will be given to the combination of Sensor Web and Web Processing technology which opens up new possibilities by having near real-time data flows that can be linked on-demand to different processing services.
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