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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Zahra Ashktorab, Christopher Brown, Manojit Nandi, & Aron Culotta. (2014). Tweedr: Mining twitter to inform disaster response. 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. 354–358). University Park, PA: The Pennsylvania State University.
Abstract: In this paper, we introduce Tweedr, a Twitter-mining tool that extracts actionable information for disaster relief workers during natural disasters. The Tweedr pipeline consists of three main parts: classification, clustering and extraction. In the classification phase, we use a variety of classification methods (sLDA, SVM, and logistic regression) to identify tweets reporting damage or casualties. In the clustering phase, we use filters to merge tweets that are similar to one another; and finally, in the extraction phase, we extract tokens and phrases that report specific information about different classes of infrastructure damage, damage types, and casualties. We empirically validate our approach with tweets collected from 12 different crises in the United States since 2006.
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Tuncay Bayrak. (2007). Performance metrics for disaster monitoring systems. 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. 125–132). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Understanding the performance of disaster monitoring systems is a key to understanding their success, therefore; various qualitative and quantitative measures and metrics can be applied in the characterization and analysis of such systems. Through evaluation studies, problems that impede a disaster monitoring system performance can be identified. The results can be used for system control, design, and capacity planning. Previous studies address technical performance analysis metrics for analyzing monitoring systems leaving out human and organizational dimensions of such systems. Thus, the primary objective of this study is to identify and describe a set of disaster monitoring systems performance analysis metrics that may be employed to evaluate such systems. This study may be valuable to researchers and practitioners involved in disaster and emergency response studies in planning the transportation of vital first-aid supplies and emergency personnel to disaster-affected areas, and in improving chances of survival after a natural disaster.
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Andrea Bellucci, Alessio Malizia, Paloma Díaz, & Ignacio Aedo. (2010). Framing the design space for novel crisis-related mashups: The eStoryS example. 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: Web 2.0 can be viewed as a platform where users can develop their own web applications. It is also characterized by a vast amount of user-generated contents presenting spatial and temporal components, by means of associated metadata. These metadata has been successfully exploited to generate map-based mashups (web applications gathering data from different sources) facing different kind of crisis situations, ranging from natural disasters (earthquakes, wildfires, floods...) to human-made disasters (terrorist attacks, school shootings, conflicts...). The social and collaborative dimensions of the Web 2.0 can be also exploited for managing crisis-related information. We present here a survey of current crisis-related mashups we employed to extract design dimensions and provide a conceptual framework that can be used: A) to understand current systems and; b) to design next generation of crisis-related mashups. We propose the eStoryS system as an example of application developed following the design principles presented in this paper. On the basis of our analysis, we believe that the design dimensions posited here provide useful insights for the design of novel web mashups in the emergency management domain.
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Colleen J Buono, Theodore C. Chan, William G. Griswold, Ricky Huang, Fang Liu, James Killeen, et al. (2008). WIISARD: Wireless internet information system for medical response to disasters. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (126). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Called the Wireless Internet Information System for Medical Response in Disasters, or WIISARD, the use of sophisticated wireless technology to coordinate and enhance care of mass casualties in a terrorist attack or natural disaster is the focus of a federally funded research project at the University of California, San Diego (UCSD). The project brings together broad-based participation from academia, industry, the military, and emergency responders from the City and County of San Diego.
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Mifan Careem, David Bitner, & Ravindra De Silva. (2007). GIS integration in the Sahana disaster management system. 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. 211–218). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Disaster Management often involves using Information and Communications Technology (ICT) to manage large amounts of data efficiently. Data gathered from disasters are often related to geographic locations, such as the affected geographic region, thus requiring special forms of data management software to utilize and manage them efficiently. Geographic Information Systems (GIS) are specialized database systems with software that can analyze and display data using digitized maps and tables for decision making. Preparing and correctly formatting data for use in a GIS is nontrivial, and it is even more challenging during disasters because of tight time constraints and inherent unpredictability of many natural disasters. This paper describes the important role of GIS in disaster management, and discusses the most common characteristics of GIS and their potential use in disaster response. We follow up with a detailed description of the GIS prototype in the Sahana Disaster Management System.
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Nicolas Di Tada, & Timothy Large. (2010). Emergency information system. 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 describes an information system designed to be deployed in emergencies caused by sudden onset natural disasters. The aim is to streamline the communication flow and collaboration between media, aid workers and government agencies with the affected population, to help the latter get verified, accurate and actionable information that will enable them to make decisions and recover from the disaster. The Emergency Information Service (EIS) system also provides means for affected population and field workers to channel vital data back up into aid response. This tool is part of a free information service run by Thomson Reuters Foundation to help survivors of natural disasters. It will serve the affected populations, local media and relief responders by providing fast, practical and verified information in local languages through the best means available.
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Dipak Singh, Shayan Shams, Joohyun Kim, Seung-jong Park, & Seungwon Yang. (2020). Fighting for Information Credibility: AnEnd-to-End Framework to Identify FakeNews during Natural Disasters. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 90–99). Blacksburg, VA (USA): Virginia Tech.
Abstract: Fast-spreading fake news has become an epidemic in the post-truth world of politics, the stock market, or even during natural disasters. A large amount of unverified information may reach a vast audience quickly via social media. The effect of misinformation (false) and disinformation (deliberately false) is more severe during the critical time of natural disasters such as flooding, hurricanes, or earthquakes. This can lead to disruptions in rescue missions and recovery activities, costing human lives and delaying the time needed for affected communities to return to normal. In this paper, we designed a comprehensive framework which is capable of developing a training set and trains a deep learning model for detecting fake news events occurring during disasters. Our proposed framework includes infrastructure to collect Twitter posts which spread false information. In our model implementation, we utilized the Transfer Learning scheme to transfer knowledge gained from a large and general fake news dataset to relatively smaller fake news events occurring during disasters as a means of overcoming the limited size of our training dataset. Our detection model was able to achieve an accuracy of 91.47\% and F1 score of 90.89 when it was trained with the first 28 hours of Twitter data. Our vision for this study is to help emergency managers during disaster response with our framework so that they may perform their rescue and recovery actions effectively and efficiently without being distracted by false information.
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Tristan Endsley, Yu Wu, & James Reep. (2014). The source of the story: Evaluating the credibility of crisis information sources. 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. 160–164). University Park, PA: The Pennsylvania State University.
Abstract: In a highly connected world, information coming from different media sources and social relationships are more quickly disseminated than ever before. Natural disasters such as Typhoon Haiyan capture attention globally. Investigations of how people respond to the credibility of different sources have implications for policy making and information systems design. In this paper, we studied how different factors (strength of social ties and sources of crisis information) affect perception of credibility of crisis information about natural disasters. Our analysis and findings indicate that for crisis information about natural disasters, people tend to trust traditional media channels, such as printed news, and televised news. The type of social tie also influences the perceived credibility of the crisis information.
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S.H.M. Fakhruddin. (2006). Community based cost effective early warning dissemination network (EWDN). In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 50–51). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Natural disasters are frequent in Bengladesh. Because Bengladesh has a fragile economy that is mostly dependent on agriculture, these events can be disastrous to the economy and people of the country. Adequate warnings to the community and institutions can mitigate the deleterious effects. This paper presents a model for an effective disaster warning and dissemination system (EWDN) that can provide timely and accurate alerts of natural disasters thus reducing loss of life, property and other risks.
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Ferda Ofli, Firoj Alam, & Muhammad Imran. (2020). Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 802–811). Blacksburg, VA (USA): Virginia Tech.
Abstract: Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image).
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Gaëtan Caillaut, Cécile Gracianne, Nathalie Abadie, Guillaume Touya, & Samuel Auclair. (2022). Automated Construction of a French Entity Linking Dataset to Geolocate Social Network Posts in the Context of Natural Disasters. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 654–663). Tarbes, France.
Abstract: During natural disasters, automatic information extraction from Twitter posts is a valuable way to get a better overview of the field situation. This information has to be geolocated to support effective actions, but for the vast majority of tweets, spatial information has to be extracted from texts content. Despite the remarkable advances of the Natural Language Processing field, this task is still challenging for current state-of-the-art models because they are not necessarily trained on Twitter data and because high quality annotated data are still lacking for low resources languages. This research in progress address this gap describing an analytic pipeline able to automatically extract geolocatable entities from texts and to annotate them by aligning them with the entities present in Wikipedia/Wikidata resources. We present a new dataset for Entity Linking on French texts as preliminary results, and discuss research perspectives for enhancements over current state-of-the-art modeling for this task.
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Gerhard Backfried, Christian Schmidt, & Gerald Quirchmayr. (2015). Cross-Media Linking in Times of Disaster. 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: Many possible links and connections can be observed between the different types of media used for communication during a crisis. These links can be detected and assembled to provide a more complete picture of events. They can be categorized according to the type of destination which yields important information for the gathering process as well as concerning general patterns of how platforms are connected. Tweets, posts and comments thus become parts of larger, linked sets of documents forming compound-documents. These documents stretch across media borders and platforms and provide context and broader information for individual entries. In the current paper we describe some of the links and linking behavior encountered during the floods in Central Europe of 2013 from the perspective of Twitter and Facebook.
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Norman C. Hester, Jim Wilkinson, Stephen Patrick Horton, & Theresa I. Jefferson. (2008). Integration of information systems for post earthquake research response. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 362–367). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Natural disasters occur infrequently, limiting our ability to develop an inclusive knowledge base concerning such events. The ability to study, interpret, and document findings immediately following a damaging seismic event, is a critical step in furthering our understanding of events, allowing for effective awareness, mitigation, response, and recovery efforts. In the central United States, a Post Earthquake Technical Information Clearinghouse (PETIC) Plan has been developed to coordinate research activities, and to facilitate collaboration between the emergency management and research communities. Because a damaging earthquake in the central U.S. will impact several states, a Multi-State Technical Information Clearinghouse (MSTIC) Coordination Plan to link state technical information clearinghouses (STICs) is proposed. This paper describes beginning efforts to define the role and functions of a MSTIC as well as formalize plans with emergency management agencies to facilitate collaboration and coordination between STICs, the MSTIC.
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Jens Kersten, Anna Kruspe, Matti Wiegmann, & Friederike Klan. (2019). Robust filtering of crisis-related tweets. 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: Social media enables fast information exchange and status reporting during crises. Filtering is usually required to
identify the small fraction of social media stream data related to events. Since deep learning has recently shown to
be a reliable approach for filtering and analyzing Twitter messages, a Convolutional Neural Network is examined for
filtering crisis-related tweets in this work. The goal is to understand how to obtain accurate and robust filtering
models and how model accuracies tend to behave in case of new events. In contrast to other works, the application
to real data streams is also investigated. Motivated by the observation that machine learning model accuracies
highly depend on the used data, a new comprehensive and balanced compilation of existing data sets is proposed.
Experimental results with this data set provide valuable insights. Preliminary results from filtering a data stream
recorded during hurricane Florence in September 2018 confirm our results.
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Dennis J. King. (2005). Humanitarian knowledge 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. 291–295). Brussels: Royal Flemish Academy of Belgium.
Abstract: International complex humanitarian emergencies present numerous challenges to aid organizations trying to manage data, information and knowledge about the situation or event. Humanitarian aid organizations should be able to identify what critical information they need, where to find it, what are the major gaps, and how best to share, present and disseminate this information. These challenges can be addressed through improved knowledge management. The faster and more efficiently humanitarian aid organizations are able to identify, collect, distill, analyze and manage the vast corpus of what they need to know, the more effectively they can plan for and respond to natural disasters and complex emergencies and the more lives are potentially saved.
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Ana Laugé, Josune Hernantes, & Jose Mari Sarriegi. (2013). Disaster impact assessment:A holistic framework. 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. 730–734). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: One of the important aspects of the crisis management consists in a comprehensive analysis of the impacts generated and their associated cost. The capacity to carry out an efficient holistic management, through the development of preventive measures and response programs relies on a proper estimation of impacts which helps to mitigate the harshness or can even avoid impacts in face of future crises. The aim of this paper is to analyse existing methodologies for natural disasters' impact evaluation, the identification of the different impact categories as well as the explanation of a natural disasters impact framework, which includes a list of indicators for a correct impact assessment. The framework also analyses the evolution of impacts, that is, how immediate impacts can also generate delayed impacts.
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Maryam Shahbazi, Christian Ehnis, Majid Shahbazi, & Deborah Bunker. (2018). Tweeting from the Shadows: Social Media Convergence Behaviour During the 2017 Iran-Iraq Earthquake. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 416–427). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Official policies, socioeconomic and demographic factors influence how individuals cope with, and respond to natural disasters. Understanding the impact of these factors in social media crisis communications studies is difficult. This paper focuses on convergence behaviour during social media crisis communication in an environment where the access to commercial social media platforms is highly restricted. This study is designed as a case which analyses 41,745 Tweets communicated during an earthquake event and for the two weeks after. This research aims to understand how different communities use social media services for communication during extreme events. The content of the Tweets shows users' attitudes toward government policies as well as the social difficulties of ethnic groups reflecting on the use of social media in crises communication. The results indicate a “political effect” on this online crisis communication. This behaviour was not expected and has been underreported in the current body of knowledge.
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Mohd Khairul Azmi Hassan, & Yun-Heh Chen-Burger. (2016). Communication and Tracking Ontology Development for Civilians Earthquake Disaster Assistance. 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: One of the most important components of recovery and speedy response during and immediately after an earthquake disaster is a communication and tracking which possibly capable of discovering affected peoples and connects them with their families, friends, and communities with first responders and/or to support computational systems. With the capabilities of current mobile technologies, we believed that it can be a smart earthquake disaster tools aid to help people in this situation. Ontologies are becoming crucial parts to facilitate an effective communication and coordination across different parties and domains in providing assistance during earthquake disasters, especially where affected locations are remote, affected population is large and centralized coordination is poor. Several existing competing methodologies give guidelines as how ontology may be built, there are no single right ways of building an ontology and no standard of Disaster Relief Ontology exist, although separated related ontologies may be combined to create an initial version. This article discusses the on-going development of an ontology for a Communication and Tracking System (CTS), based on existing related ontologies, that is aimed to be used by mobile phone applications to support earthquake disaster relief at the real-time.
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Sung Pil Moon, Yikun Liu, Steven O. Entezari, Afarin Pirzadeh, Andrew Pappas, & Mark Pfaff. (2013). Top health trends: An information visualization tool for awareness of local health trends. 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. 177–187). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: We developed an intelligent information visualization tool to enable public health officials to detect healthrelated trends in any geographic area of interest, based on Twitter data. Monitoring emergent events such as natural disasters, disease outbreaks, and terrorism is vital for protecting public health. Our goal is to support situation awareness (SA) for personnel responsible for early detection and response to public health threats. To achieve this goal, our application identifies the most frequently tweeted illnesses in a ranked chart and map for a selected geographic area. Automated processes mine and filter health-related tweets, visualize changes in rankings over time, and present other keywords frequently associated with each illness. User-centered visualization techniques of monitoring, inspecting, exploring, comparing and forecasting supports all the three stages of SA. An evaluation conducted with experts in health-related domains provided significant insights about awareness of localized health trends and their practical use in their daily work.
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Dilek Ozceylan, & Erman Coskun. (2008). Defining critical success factors for National Emergency Management Model and supporting the model with information systems. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 376–383). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Natural or man-made disasters frequently occur in different countries and disaster types and consequences might differ immensely depending on that country's unique characteristics. While probability of a man-made disaster occurrence will be high for technologically advanced countries as a result of using technology in almost every aspect of daily life, probability of natural disaster occurrence will be dependent on geological, geographical, and climate related factors. Based on their different risk types and levels, each nation should create their own National Emergency Management Model (NEMM) and because of country specific conditions each plan must be unique. Thus, for each country NEMM should be focusing on different factors which are important and should show that country the importance list of factors. As a result, countries may better distribute their limited resources to reach optimum emergency management plan and execution. In this study, our goal is to three fold. Our first goal is to come up with full list of categories and factors which are important for a successful National Emergency Management Model. In order to achieve this goal, we determined our categories and factors based on our analysis of previous disasters and literature review. The second goal is to determine the importance level of each category and defining critical success factors for different countries. For this purpose, we are planning to use experts from different countries. This part of study is still underway. Finally, we analyze how information systems might be utilized for each category and factors to support a better National Emergency Management Model. This is a first step of a multi-step research.
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Sofie Pilemalm, & Niklas Hallberg. (2008). Exploring service-oriented C2 support for emergency response for local communities. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 159–166). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The increased impact of natural disasters and terror attacks on our society has augmented the awareness of needs for advances in emergency response. For local communities it is vital to make use of existing resources. Service-oriented technology provides new possibilities for the enhancement of command and control (C2) systems. However, to gain full use of the technology, it must harmonize with the supported organizations and their work procedures. This paper explores the possibilities and usefulness of service oriented C2-systems for emergency response at the local community level. The study was performed in five steps: (1) literature study, (2) interviews, (3) scenario design, (4) prototyping service-oriented C2 systems, and (5) a scenario based evaluation. The results show that service-oriented C2 systems would support and enhance emergency response at the local community level. Still, several issues that need to be further considered remain, not the least the ability of quality assurance of services.
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Raquel Gimenez, Leire Labaka, Jose Mari Sarriegi, & Josune Hernantes. (2015). Development of a Virtual Community of Practice on 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 research identifies from literature principles of successful Virtual Communities of Practice (VCoPs) and explains how they have been fulfilled in the development of a VCoP that aims at contributing to knowledge sharing on natural disasters. The developed VCoP involves 70 experts in dealing with natural disasters from different hierarchical levels, organizations and nationalities of Europe. The VCoP has been developed within a European project from the 7th framework program. During the project three workshops were arranged for the members of the VCoP to know each other and to develop a living document. The living document is a web based tool used by the VCoP to share documents and insights, and it helps VCoP members networking. This paper provides direction for developing a VCoP to exchange lessons learned reports among crisis managers and first responders, and it identifies barriers that hinder the use of the living document.
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Eduard Santamaria, Florian Segor, & Igor Tchouchenkov. (2013). Rapid aerial mapping with multiple heterogeneous unmanned vehicles. 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. 592–596). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In this article, work in progress on a system for rapid aerial mapping is presented. We believe that a tool able to quickly generate an up-to-date high resolution aerial view, e.g. shortly after a natural disaster or a big incident occurs, can be a highly valuable asset to help first responders in the decision making. The presented work focuses on the path planning capabilities of the system, together with the area partitioning and workload distribution among a team of multi-rotor unmanned aircraft. Sensor footprint and range of the involved aircraft may differ. The presented approach is based on an approximate cellular decomposition of the area of interest. The results of this work will be integrated into an existing system which already provides a mobile ground control station able to supervise and control multiple sensor carriers.
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Gayane Shalunts, Gerhard Backfried, & Prinz Prinz. (2014). Sentiment analysis of German social media data for natural disasters. 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. 752–756). University Park, PA: The Pennsylvania State University.
Abstract: Analysis of social media and traditional media provides significant information to first responders in times of natural disasters. Sentiment analysis, particularly of social media originating from the affected population, forms an integral part of multifaceted media analysis. The current paper extends an existing methodology to the domain of natural disasters, broadens the support of multiple languages and introduces a new manner of classification. The performance of the approach is evaluated on a recently collected dataset manually annotated by three human annotators as a reference. The experiments show a high agreement rate between the approach taken and the annotators. Furthermore, the paper presents the initial application of the resulting technology and models to sentiment analysis of social media data in German, covering data collected during the Central European floods of 2013.
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