Christian Siemen, Roberto dos Santos Rocha, Roelof P. van den Berg, Bernd Hellingrath, & João Porto de Albuquerque. (2017). Collaboration among Humanitarian Relief Organizations and Volunteer Technical Communities: Identifying Research Opportunities and Challenges through a Systematic Literature Review. 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. 1043–1054). Albi, France: Iscram.
Abstract: Collaboration is the foundation to strengthen disaster preparedness and for effective emergency response actions at all levels. Some studies have highlighted that remote volunteers, i.e., volunteers supported by Web 2.0 technologies, possess the potential to strengthen humanitarian relief organizations by offering information regarding disaster-affected people and infrastructure. Although studies have explored various aspects of this topic, none of those provided an overview of the state-of-the-art of researches on the collaboration among humanitarian organizations and communities of remote volunteers. With the aim of overcoming this gap, a systematic literature review was conducted on the existing research works. Therefore, the main contribution of this work lies in examining the state of research in this field and in identifying potential research gaps. The results show that most of the research works addresses the general domain of disaster management, whereas only few of them address the domain of humanitarian logistics.
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Christine Adler, Werner Sauter, Jona Meyer, Maria Hagl, & Margit Raich. (2015). First Steps in the Development of an Internet-based Learning Platform for Strategic Crisis Managers. 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: Based on interviews with European crisis managers and other stakeholders, we identified specific learning requirements regarding psycho-social support in disaster management. This paper describes the process of developing a learning environment specifically for disaster managers with strategic responsibilities. Focusing on competence development, the underlying concept emphasizes peer-like exchanges and self-directed learning rather than passive, externally organized training methods. For that purpose a web-based learning platform is being developed in combination with competence development modules tailored to the needs of crisis managers. The envisioned learning platform utilizes blended learning and social learning concepts and technologies to facilitate knowledge building, adapted and customized to the needs of the crisis managers. End-user requirements will be individually assessed in order to generate up-to-date content while considering the wider EU-context.
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Christophe Viavattene, Simon McCarthy, Michelle Ferri, Martina Monego, & Maurizio Mazzoleni. (2016). Evaluation of emergency protocols using agent-based approach. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Integrated flood risk management involves a large portfolio of options for mitigating risks that includes hard and soft structural, non-structural, and recovery responses. Non-structural responses include flood warnings, emergency services supported by individuals, collective actions and the use of resistance and resilience measures. Sufficient flood warning time, appropriate actions at desired locations and time are essential for effective and beneficial responses. From this perspective beside the management of the crisis itself, the level of preparedness including the evaluation of plans involving such responses (e.g. emergency protocols) also needs to be sufficient and, thus in the context of various event scenarios.
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Christopher W. Zobel, Milad Baghersad, & Yang Zhang. (2017). Calling 311: evaluating the performance of municipal services after disasters. 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. 164–172). Albi, France: Iscram.
Abstract: As part of a movement towards enabling smart cities, a growing number of urban areas in the USA, such as New York City, Boston, and Houston, have established 311 call centers to receive service requests from their citizens through a variety of platforms. In this paper, for the first time, we propose to leverage the large amount of data provided by these non-emergency service centers to help characterize their operational performance in the context of a natural disaster event. We subsequently develop a metric based on the number of open service requests, which can serve as the basis for comparing the relative performance of different departments across different disasters and in different geographic locations within a given urban area. We then test the applicability and usefulness of the approach using service request data collected from New York City's 311 service center.
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Michael J. Chumer, & Murray Turoff. (2006). Command and control (C2): Adapting the distributed military model for emergency response and emergency management. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 465–476). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: The military use of Command and Control (C2) has been refined over centuries of use and developed through years of combat situations. This C2 model is framed as process, function, and organization, suggesting that emergency response organizations and emergency management structure their non military C2 and subsequent response scenarios within the C2 framework established in this paper.
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Marline Claessens, Nicolas Lewyckyj, Jane Biesemans, & Jurgen Everaerts. (2005). Pegasus, a UAV project for disaster management. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 233–236). Brussels: Royal Flemish Academy of Belgium.
Abstract: The Flemish Institute for Technological Research (Vito) in Belgium has initiated in 2000 the PEGASUS (Policy support for European Governments by Acquisition of information from Satellite and UAV-borne Sensors) project which envisages the development of a solar powered UAV (Unmanned Aerial Vehicle) containing several types of instruments for remote sensing and flying at an altitude of about 20 km. The aircraft can be deployed rapidly in crisis situations and provide disaster managers with ~1 m resolution images (or better if required) of the affected area. High quality data shall be received in less than half an hour from a mobile ground station that is in direct contact with the UAV, which can operate as long as requested by the user. The PEGASUS HALE-UAV is a flexible and cost-effective tool that will allow officials and local authorities to dispose quickly over relevant geographical information in an emergency situation. The first demonstration flight of the PEGASUS HALE-UAV shall take place in the summer of 2005 over Flanders.
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Timothy Clark, & Rich Curran. (2013). Geospatial site suitability modeling for US department of defense humanitarian assistance projects. 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. 463–467). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The purpose of this paper is to outline the requirement for data-driven methods for determining optimal geographic locations of United States Department of Defense (DOD) Humanitarian Assistance (HA) resources, including disaster mitigation and preparedness projects. HA project managers and tactical implementers charged with cost-efficient deployment of HA resources are challenged to produce measurable effects, in addition to contributing to broader Joint and Interagency-informed security assistance strategies. To address these issues, our ongoing research advocates geospatial multi-criteria site suitability decision support capabilities that leverage 1) existing geospatial resource location-allocation methodology as applied in government, retail, and commercial sectors; 2) user-generated criteria and objective preferences applied in widely-used decision frameworks; 3) assessments of the feasibility of obtaining data at a geographic scale where DOD tactical/operational level users can benefit from the model outputs; and 4) social science theory related to the HA domain criteria that form the foundation of potential decision models.
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Tina Comes, Frank Schätter, & Frank Schultmann. (2013). Building robust supply networks for effective and efficient disaster response. 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. 230–240). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The effective and efficient distribution of relief goods is a key challenge in disaster management. Typically, adhoc supply networks (SNs) need to be built, in which various actors with different interests collaborate. Although information is sparse and highly uncertain, time for SN design is short, and important strategic decisions (e.g., location of facilities), whose revision requires investing substantial time, effort and resources, must be made promptly. This paper presents an iterative approach for the design of robust SNs that combines (i) an optimisation model to identify promising alternatives to be analysed in detail, (ii) a scenario-based approach to analyse the weaknesses of these alternatives and generate alternative solutions for comparison and benchmarking, and (iii) a decision support module for detailed comparisons and consensus building. By following the iterative approach, successively robust SNs are created to enable effective and efficient disaster response. We illustrate our approach by an example from the Haiti 2010 earthquake.
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Tina Comes, & Bartel A. Van De Walle. (2014). Measuring disaster resilience: The impact of hurricane sandy on critical infrastructure systems. 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. 195–204). University Park, PA: The Pennsylvania State University.
Abstract: Modern critical infrastructure (CI) systems are tightly coupled, resulting in unprecedented complexity and difficulty to predict, limit and control the consequences of disruptions caused by hazards. Therefore, a paradigm shift in disaster risk management is needed: instead of focusing on predicting events, resilience needs to be improved as a basis for adequate response to any event. This paper starts from a definition of CI resilience that provides a basis for quantitative and qualitative decision support. For the quantitative modelling approach, which aims at measuring the resilience of individual CIs, we focus on two CIs of fundamental importance for disaster response: transportation and power supply. The qualitative framework details relations between CIs. The results of this research are illustrated by a case study that analyses the impact of Hurricane Sandy. The findings highlight the need for a framework that combines qualitative and quantitative information from heterogeneous sources to improve disaster resilience.
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Tina Comes, Claudine Conrado, Michael Hiete, Michiel Kamermans, Gregor Pavlin, & Niek Wijngaards. (2010). An intelligent decision support system for decision making under uncertainty in distributed reasoning frameworks. 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: This paper presents an intelligent system facilitating better-informed decision making under severe uncertainty as found in emergency management. The construction of decision-relevant scenarios, being coherent and plausible descriptions of a situation and its future development, is used as a rationale for collecting, organizing, filtering and processing information for decision making. The development of scenarios is geared to assessing decision alternatives, thus avoiding time-consuming analysis and processing of irrelevant information. The scenarios are constructed in a distributed setting allowing for a flexible adaptation of reasoning (principles and processes) to the problem at hand and the information available. This approach ensures that each decision can be founded on a coherent set of scenarios, which was constructed using the best expertise available within a limited timeframe. Our theoretical framework is demonstrated in a distributed decision support system by orchestrating both automated systems and human experts into workflows tailored to each specific problem.
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Tina Comes, Niek Wijngaards, & Frank Schultmann. (2012). Efficient scenario updating in emergency management. 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: Emergency managers need to assess, combine and process large volumes of information with varying degrees of (un)certainty. To keep track of the uncertainties and to facilitate gaining an understanding of the situation, the information is combined into scenarios: stories about the situation and its development. As the situation evolves, typically more information becomes available and already acknowledged information is changed or revised. Meanwhile, decision-makers need to keep track of the scenarios including an assessment whether the infor-mation constituting the scenario is still valid and relevant for their purposes. Standard techniques to support sce-nario updating usually involve complete scenario re-construction. This is far too time-consuming in emergency management. Our approach uses a graph theoretical scenario formalisation to enable efficient scenario updating. MCDA techniques are employed to decide whether information changes are sufficiently important to warrant scenario updating. A brief analysis of the use-case demonstrates a large gain in efficiency. © 2012 ISCRAM.
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Louise K. Comfort, Brian A. Chalfant, Jee Eun Song, Mengyao Chen, & Brian Colella. (2014). Managing information processes in disaster events: The impact of superstorm sandy on business organizations. 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. 230–239). University Park, PA: The Pennsylvania State University.
Abstract: Building community resilience to natural disasters represents a major policy priority for the United States as hazards impact vulnerable urban regions with increasing frequency and severity. Applying network analysis techniques, we examine the dynamics of emergency response to Superstorm Sandy, which struck the United States east coast in late October 2012 and caused over $72 billion in damages. Drawing on a variety of data sources and analytical techniques, we document the storm's impact on a system of interacting private, public, and nonprofit organizations. We find that the storm's response network exhibited clear patterns of information gaps and flows among different types of organizations. Our findings suggest a general lack of communication between government agencies and businesses, an area of potential improvement in future regional-scale emergency response systems.
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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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Diana Contreras, Thomas Blaschke, Stefan Kienberger, & Peter Zeil. (2011). Spatial vulnerability indicators: Measuring recovery processes after earthquakes. 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 order to analyze and evaluate any post-disaster phases it is necessary to address the pre-existent vulnerability conditions. The methodology consists of four steps: the first step comprises of a review of vulnerability and recovery indicators; the second step is to identify indicators based on spatial variables; the third step is to find the common variables among the subsets of spatial variables from vulnerability and recovery indicators; and the fourth step more pragmatic, is an investigation of the availability of data. The initial results are the set of vulnerability and recovery indicators. Reducing the set of indicators to the indicators represented in a spatial context and the indicators with common features of vulnerability and recovery indices bears the risk to ignore some important single indicators; nevertheless, the added value of the on-going research is to show the advantages of using indicators based on spatial variables.
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Cornelia Caragea, Adrian Silvescu, & Andrea Tapia. (2016). Identifying Informative Messages in Disasters using Convolutional Neural Networks. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples? ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the ?bag of words? and n-grams as features on several datasets of messages from flooding events.
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Erman Coskun, & Dilek Ozceylan. (2011). Complexity in emergency management and disaster response information systems (EMDRIS). 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: Today emergencies seem more complex than ever. Process of managing these emergencies also becomes more complex because of increasing number of involved parties, increasing number of people affected, and increasing amount of resources. This complexity, inherent in emergency management, brings lots of challenges to decision makers and emergency responders. Information systems and technologies are utilized in different areas of emergency management. However complexity increases exponentially in emergency situations and it requires more sophisticated IS and IT and it makes response and management more challenging. Thus analyzing the root causes of emergency management information systems complexity is crucial for improving emergency response effectiveness. This paper frames the issue of information systems complexity by focusing on the types of complexities involved in emergency management phases and explaining each complexity type. We propose 6 different complexity types: Human Complexity, Technologic Complexity, Event Complexity, Interaction Complexity, Decision Making Complexity, and Cultural Complexity.
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Cossentino, M., Guastella, D. A., Lopes, S., & Sabatucci, L. (2023). Adaptive Execution of Workflows in Emergency Response. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 784–796). Omaha, USA: University of Nebraska at Omaha.
Abstract: In emergencies, preparation is of paramount importance but it is not sufficient. As we know, emergency agencies develop extensive (text) plans to deal with accidents that could occur in their territories; their personnel train to enact such procedures, but, despite that, the unpredictable conditions that occur during an emergency require the ability to adapt the plan promptly. This paper deals with the last mile of a process we defined for enabling the adaptive execution of such emergency plans. In previous works, we discussed how to convert a free-text plan into a structured-text form, represent this plan using standard modelling notations, and extract goals that plans prescribe to be fulfilled. In this paper, we propose an approach for executing these plans with a workflow execution engine enriched by the capability to support runtime adaptation.
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Martine Couturier, & Edith Wilkinson. (2005). Open advanced system for improved crisis management (OASIS). In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 283–286). Brussels: Royal Flemish Academy of Belgium.
Abstract: The OASIS Project addresses the Strategic objective 2.3.2.9, “Improving Risk Management”, of the second call for tender of the European Commission FP6 Information Society Technologies program. The objective of OASIS is to define and develop an Information Technology (IT) framework based on an open and flexible architecture and using standards that will be the basis of a European Emergency Management system. OASIS is intended to facilitate the cooperation between the information systems used by civil protection organisations, in a local, regional, national or international environment. This Disaster and Emergency Management system aims to support the response operations in the case of large scale as well as local emergencies.
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Cruz, J. A. dela, Hendrickx, I., & Larson, M. (2023). Towards XAI for Information Extraction on Online Media Data for Disaster Risk Management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 478–486). Omaha, USA: University of Nebraska at Omaha.
Abstract: Disaster risk management practitioners have the responsibility to make decisions at every phase of the disaster risk management cycle: mitigation, preparedness, response and recovery. The decisions they make affect human life. In this paper, we consider the current state of the use of AI in information extraction (IE) for disaster risk management (DRM), which makes it possible to leverage disaster information in social media. We consolidate the challenges and concerns of using AI for DRM into three main areas: limitations of DRM data, limitations of AI modeling and DRM domain-specific concerns, i.e., bias, privacy and security, transparency and accountability, and hype and inflated expectations. Then, we present a systematic discussion of how explainable AI (XAI) can address the challenges and concerns of using AI for IE in DRM.
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Steven Curnin, & Christine Owen. (2013). A typology to facilitate multi-agency coordination. 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. 115–119). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Multi-agency coordination in emergency management presents many challenges. Agencies that normally operate independently have to assemble into a unified supra organization to achieve a common goal. To achieve successful multi-agency coordination organizations need to span organizational boundaries and provide linkages with multiple agencies. This requires interorganizational compatibility of information and communication systems. Necessary for this success are the stakeholders responsible for facilitating these organizational boundary spanning activities. This paper proposes that the preliminary research findings can create a typology of dimensions crucial to facilitating multi-agency emergency management coordination. It is envisaged that the typology will culminate into a diagnostic tool that will enable stakeholders to examine the breakdowns and successes of multi-agency emergency management coordination.
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Daniel Auferbauer, Christoph Ruggenthaler, Gerald Czech, & Ivan Gojmerac. (2019). Taxonomy of Community Interaction in Crises and Disasters. 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: Taxonomies are integral to systems engineering, as they structure our knowledge of a field and so provide the
foundation for technological development. We contribute such taxonomies for the field of Community
Interaction and Engagement in Crisis and Disaster Management, which represents the interface between
members of the public who commit to relief efforts and established organisations that have a pre-defined role in
crisis management. These actors are unified in their purpose to help those in need, but also set apart by their
organisational structures and modes of operation. We classify the actors of Community Interaction and
Engagement, as well as the interactions between them. Our contribution outlines areas where the application of
Information and Communication Technology can offer benefits to Community Interaction and Engagement.
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Daniel Auferbauer, Roman Ganhör, & Hilda Tellioglu. (2019). Opportunistic Affiliation in Spontaneous Volunteer Management. 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: Spontaneous volunteers influence crisis and disaster relief efforts as both an effective aid and a stressing factor for emergency organisations. Managing the negative impacts of spontaneous volunteering has thus become part of command and control deliberations. In this paper, we take a closer look at integrating spontaneous volunteers into the formal response system to mitigate negative impacts.
Working with participants from formal response organisations, we gathered qualitative data regarding the management of spontaneous volunteers during the European migration crisis in 2015.
Through thematic analysis, we extracted topics to systematically describe the interaction between emergency organisations and spontaneous volunteers. As implication thereof, we propose how computer supported systems can be applied to better manage spontaneous volunteers. In our discussion, we focus on the registration process and ad hoc verification of spontaneous volunteers to better integrate them in the formal response process.
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Daniel Link, Bernd Hellingrath, & Jie Ling. (2016). A Human-is-the-Loop Approach for Semi-Automated Content Moderation. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Online social media has been recognized as a valuable information source for disaster management whose volume, velocity and variety exceed manual processing capacity. Current machine learning systems that support the processing of such data generally follow a human-in-the-loop approach, which has several inherent limitations. This work applies the human-is-the-loop concept from visual analytics to semi-automate a manual content moderation workflow, wherein human moderators take the dominant role. The workflow is instantiated with a supervised machine learning system that supports moderators with suggestions regarding the relevance and categorization of content. The instantiated workflow has been evaluated using in-depth interviews with practitioners and serious games. which suggest that it offers good compatibility with work practices in humanitarian assessment as well as improved moderation quality and higher flexibility than common approaches.
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Shideh Dashti, Leysia Palen, Mehdi P. Heris, Kenneth M. Anderson, T. Jennings Anderson, & Scott Anderson. (2014). Supporting disaster reconnaissance with social media data: A design-oriented case study of the 2013 Colorado floods. 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. 632–641). University Park, PA: The Pennsylvania State University.
Abstract: Engineering reconnaissance following an extreme event is critical in identifying the causes of infrastructure failure and minimizing such consequences in similar future events. Typically, however, much of the data about infrastructure performance and the progression of geological phenomena are lost during the event or soon after as efforts move to the recovery phase. A better methodology for reliable and rapid collection of perishable hazards data will enhance scientific inquiry and accelerate the building of disaster-resilient cities. In this paper, we explore ways to support post-event reconnaissance through the strategic collection and reuse of social media data and other remote sources of information, in response to the September 2013 flooding in Colorado. We show how tweets, particularly with postings of visual data and references to location, may be used to directly support geotechnical experts by helping to digitally survey the affected region and to navigate optimal paths through the physical space in preparation for direct observation.
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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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