| Records |
Author  |
Øyvind Hanssen |
| Title |
Improving Trails from GPS Trackers with Unreliable and Limited Communication Channels |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
489-502 |
| Keywords |
GPS tracking, trails, Search and Rescue, APRS |
| Abstract |
In this document we explore position tracking in the context of land based search and rescue operations, where we also may have a limited and unreliable communication channel. This is the case when using APRS (amateur radio tracking) in voluntary SAR services in Norway. We have looked more closely into trails of movements and how to plot these on the map to present informative real-time pictures to the incident commanders. A simple scheme is proposed to improve trails by piggybacking positions at the end of regular transmissions.Experiments show that a significant amount of positions are recovered. In some cases this can recover useful information, though it depends on the actual situation. |
| Address |
Nord University |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
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Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Geospatial Technologies and Geographic Information Science for Crisis Management (GIS) |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
ohanssen@acm.org |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2350 |
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| Author |
Zijun Long; Richard Mccreadie |
| Title |
Automated Crisis Content Categorization for COVID-19 Tweet Streams |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
667-678 |
| Keywords |
COVID-19, Tweet Classification, Crisis Management, Deep Learning |
| Abstract |
Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations. |
| Address |
University of Glasgow; University of Glasgow |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
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Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
2452593L@student.gla.ac.uk |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2363 |
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| Author |
Zeno Franco; Chris Davis; Adina Kalet; Michelle Horng; Johnathan Horng; Christian Hernandez; Karen Dotson; Andrew Yaspan; Ajay Kumar; Bas Lijnse |
| Title |
Augmenting Google Sheets to Improvise Community COVID-19 Mask Distribution |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
359-375 |
| Keywords |
Logistics, face masks, Google Sheets, modular software, community engagement |
| Abstract |
Face mask scarcity in the United States hindered early infection control efforts during the COVID-19 pandemic. Areas with a history of racial segregation and poverty experienced differential COVID-19 death and morbidity rates. Supplying masks equitably and rapidly became an urgent public health priority. A partnership between a local manufacturer with available polypropylene fabric and the Medical College of Wisconsin, which had the capability to assemble and distribute masks, was formed in April, 2020. An improvised logistics framework allowed for rapid distribution more than 250,000 masks, and later facilitated hand-off to other organizations to distribute over 3 million masks. Using an action research framework three phases of the effort are considered, 1) initial deliveries to community clinics, 2) equitable distribution to community agencies while under “safer at home” orders, and 3) depot deliveries and transfer of logistics management as larger agencies recovered. A multi-actor view was used to interrogate the information needs of faculty and staff remotely directing distribution, medical student volunteers delivering masks, and the manufacturer monitorng overall inventory. Logistics information was managed using Google Sheets augmented with a small SQLite component. A phenomenological view, toggling back and forth from the “socio” to the “technical” provides detailed insight into the strengths and limitations of digital solutions for humanitarian logistics, highlighting where paper-based processes remain more efficient. This case study suggests that rather than building bespoke logistics software, supporting relief efforts with non-traditional responders may benefit from extensible components that augment widely used digital tools. |
| Address |
Medical College of Wisconsin; Medical College of Wisconsin; Medical College of Wisconsin; Marquette University; Medical College of Wisconsin; Medical College of Wisconsin; Medical College of Wisconsin; Code for Milwaukee; University of Muenster; Netherlan |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Disaster Public Health & Healthcare Informatics in the Pandemic |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
zfranco@mcw.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2339 |
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| Author |
Zainab Akhtar; Ferda Ofli; Muhammad Imran |
| Title |
Towards Using Remote Sensing and Social Media Data for Flood Mapping |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
536-551 |
| Keywords |
Flood mapping, social media, Satellite imagery, Remote sensing |
| Abstract |
Ghana's capital, the Greater Accra Metropolitan Area (GAMA) is most vulnerable to flooding due to its high population density. This paper proposes the fusion of satellite imagery, social media, and geospatial data to derive near real-time (NRT) flood maps to understand human activity during a disaster and the extent of infrastructure damage. To that end, the paper presents an automatic thresholding technique for NRT flood mapping using Sentinel-1 images where four different speckle filters are compared using the VV, VH and VV/VH polarization to determine the best polarization(s) for delineating flood extents. The VV and VH bands together on Perona-Malik filtered images achieved the highest accuracy with an F1-score of 81.6%. Moreover, all tweet text and images were found to be located in flooded regions or in very close proximity to a flooded region, thus allowing crisis responders to better understand vulnerable communities and what humanitarian action is required. |
| Address |
Qatar Computing Research Institute; Qatar Computing Research Institute; Qatar Computing Research Institute |
| Corporate Author |
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Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Geospatial Technologies and Geographic Information Science for Crisis Management (GIS) |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
zakhtar@hbku.edu.qa |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2354 |
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| Author |
Yudi Chen; Angel Umana; Chaowei Yang; Wenying Ji |
| Title |
Condition Sensing for Electricity Infrastructures in Disasters by Mining Public Topics from Social Media |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
598-608 |
| Keywords |
social media, infrastructure resilience, human behaviors, disaster response |
| Abstract |
Timely and reliable sensing of infrastructure conditions is critical in disaster management for planning effective infrastructure restorations. Social media, a near real-time information source, has been widely used in the disaster domain for building timely, general situational awareness, such as urgent public needs and donations. However, the employment of social media for sensing electricity infrastructure conditions has yet been explored. This study aims to address the research gap to sense electricity infrastructure conditions through mining public topics from social media. To achieve this purpose, we proposed a systematic and customized approach wherein (1) electricity-related social media data is extracted by the classifier developed based on Bidirectional Encoder Representations from Transformers (BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams to incorporate the formulaic expressions of infrastructure conditions in social media. Electricity infrastructures in Florida impacted by Hurricane Irma are studied for illustration and demonstration. Results show that the proposed approach is capable of sensing the temporal evolutions and geographic differences of electricity infrastructure conditions. |
| Address |
George Mason University; George Mason University; George Mason University; George Mason University |
| Corporate Author |
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Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
wji2@gmu.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2358 |
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| Author |
Yohann Chasseray; Anne-Marie Barthe-Delanoë; Stéphane Négny; Jean-Marc Le Lann |
| Title |
Automated unsupervised ontology population system applied to crisis management domain |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
968-981 |
| Keywords |
Automated knowledge extraction, Crisis management systems, Ontologies, Experience feedback exploitation, Background knowledge acquisition |
| Abstract |
As crisis are complex systems, providing an accurate response to an ongoing crisis is not possible without ensuring situational awareness. The ongoing works around knowledge management and ontologies provide relevant and machine readable structures towards situational awareness and context understanding. Many metamodels, that can be derived into ontologies, supporting the collect and organization of crucial information for Decision Support Systems have been designed and are now used on specific cases. The next challenge into crisis management is to provide tools that can process an automated population of these metamodels/ontologies. The aim of this paper is to present a strategy to extract concept-instance relations in order to feed crisis management ontologies. The presented system is based on a previously proposed generic metamodel for information extraction and is applied in this paper to three different case studies representing three different crisis namely Ebola sanitarian crisis, Fukushima nuclear crisis and Hurricane Katrina natural disaster. |
| Address |
Laboratoire de Génie Chimique, Universitéde Toulouse, CNRS, INPT, UPS, Toulouse,France; Centre Génie Industriel, Université deToulouse, IMT Mines Albi, France; Laboratoire de Génie Chimique, Universitéde Toulouse, CNRS, INPT, UPS, Toulouse,France; Laborat |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Visions for Future Crisis Management |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
yohann.chasseray@inp-toulouse.fr |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2389 |
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| Author |
Ylenia Casali; Nazli Yonca Aydin; Tina Comes |
| Title |
Zooming into Socio-economic Inequalities: Using Urban Analytics to Track Vulnerabilities – A Case Study of Helsinki |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
1028-1041 |
| Keywords |
inequality, socio-economic patterns, vulnerability, PCA, GIS, urban analytics, Helsinki |
| Abstract |
The Covid19 crisis has highlighted once more that socio-economic inequalities are a main driver of vulnerability. Especially in densely populated urban areas, however, these inequalities can drastically change even within neighbourhoods. To better prepare for urban crises, more granular techniques are needed to assess these vulnerabilities, and identify the main drivers that exacerbate inequality. Machine learning techniques enable us to extract this information from spatially geo-located datasets. In this paper, we present a prototypical study on how Principal Component Analysis (PCA) to analyse the distribution of labour and residential characteristics in the urban area of Helsinki, Finland. The main goals are twofold: 1) identify patterns of socio-economic activities, and 2) study spatial inequalities. Our analyses use a grid of 250x250 meters that covers the whole city of Helsinki, thereby providing a higher granularity than the neighbourhood-scale. The study yields four main findings. First, the descriptive statistical analysis detects inequalities in the labour and residential distributions. Second, relationships between the socio-economic variables exist in the geographic space. Third, the first two Principal Components (PCs) can extract most of the information about the socio-economic dataset. Fourth, the spatial analyses of the PCs identify differences between the Eastern and Western areas of Helsinki, which persist since the 1990s. Future studies will include further datasets related to the distribution of urban services and socio-technical indicators. |
| Address |
TU Delft; TU Delft; TU Delft |
| Corporate Author |
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Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
978-1-949373-61-5 |
ISBN |
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Medium |
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| Track |
Other |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
Y.Casali@tudelft.nl |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2394 |
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| Author |
Yitong Li; Duoduo Liao; Jundong Li; Wenying Ji |
| Title |
Automated Generation of Disaster Response Networks through Information Extraction |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
431-438 |
| Keywords |
Disaster response, Stakeholder collaboration, Natural language processing, Network generation |
| Abstract |
Following a disaster, maintaining and restoring community lifelines require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating stakeholder collaboration networks (SCNs), this research proposes a systematic approach for reliably extracting stakeholder collaboration information from texts and automatically generating SCNs. In the proposed approach, stakeholders and their interactions are automatically extracted from texts through a natural language processing technique--Named Entity Recognition. Once extracted, the collaboration information is stored into structured datasets to automate the generation of SCNs. A case study on stakeholder collaboration in response to Hurricane Harvey is used to demonstrate the feasibility and applicability of the proposed approach. Overall, the proposed approach achieves the reliable and automated generation of SCNs from texts, which largely reduces practitioners' interpretation loads and eases the data collection process. |
| Address |
George Mason University; George Mason University; University of Virginia; George Mason University |
| Corporate Author |
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Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
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Edition |
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| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Enhancing Resilient Response in Inter-organizational Contexts |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
wji2@gmu.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2344 |
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| Author |
Yasas Senarath; Jennifer Chan; Hemant Purohit; Ozlem Uzuner |
| Title |
Evaluating the Relevance of UMLS Knowledge Base for Public Health Informatics during Disasters |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
97-105 |
| Keywords |
Public Health, Disaster Informatics, Health Informatics, UMLS, Metathesaurus |
| Abstract |
During disasters public health organizations increasingly face challenges in acquiring and transforming real-time data into knowledge about the dynamic public health needs. Resources on the internet can provide valuable information for extracting knowledge that can help improve decisions which will ultimately result in targeted and efficient health services. Digital content such as online articles, blogs, and social media are some of such information sources that could be leveraged to improve the health care systems during disasters. To efficiently and accurately identify relevant disaster health information, extraction tools require a common vocabulary that is aligned to the health domain so that the knowledge from these unstructured digital sources can be accurately structured and organized. In this paper, we study the degree to which the Unified Medical Language System (UMLS) contains relevant disaster, public health, and medical concepts for which public health information in disaster domain could be extracted from digital sources. |
| Address |
George Mason University; Northwestern University; George Mason University; George Mason University |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
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| Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
ywijesu@gmu.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2317 |
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| Author |
Yan Wang; Qi Wang; John Taylor |
| Title |
Loss of Resilience in Human Mobility across Severe Tropical Cyclones of Different Magnitudes |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
755-765 |
| Keywords |
Disaster Resilience, Geo-social networking, Human mobility, Tropical Cyclones |
| Abstract |
Severe tropical cyclones impose threats on highly populated coastal urban areas, thereby, understanding and predicting human movements plays a critical role in evaluating disaster resilience of human society. However, limited research has focused on tropical cyclones and their influence on human mobility resilience. This preliminary study examined the strength and duration of human mobility perturbation across five significant tropical storms and their affected eight urban areas using Twitter data. The results suggest that tropical cyclones can significantly perturb human movements by changing travel frequencies and displacement probability distributions. While the power-law still best described the pattern of human movements, the changes in the radii of gyration were significant and resulted in perturbation and loss of resilience in human mobility. The findings deepen the understanding about human-environment interactions under extreme events, improve our ability to predict human movements using social media data, and help policymakers improve disaster evacuation and response. |
| Address |
University of Florida; Northeastern University; Georgia Institute of Technology |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
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Abbreviated Series Title |
|
| Series Volume |
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Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
yanw@ufl.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2370 |
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| Author |
Xiaoyong Ni; Hong Huang; Wenxuan Dong; Chao Chen; Boni Su; Anying Chen |
| Title |
Scenario Prediction and Crisis Management for Rain-induced Waterlogging Based on High-precision Simulation |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
159-173 |
| Keywords |
Rain-induced waterlogging, Scenario prediction, High-precision simulation, Crisis management |
| Abstract |
Many cities, especially those in developing countries, are not well prepared for the devastating disaster of exceptional rain-induced waterlogging caused by extreme rainfall. This paper proposes a waterlogging scenario prediction and crisis management method for such kind of extreme rainfall conditions based on high-precision waterlogging simulation. A typical urban region in Beijing, China is selected as the study area in this paper. High-precision and full-scale data in the study area requested for the waterlogging simulation are introduced. The simulation results show that the study area is still vulnerable to extreme rainfall and the subsequent waterlogging. The waterlogging situation is much more severe with the increase of the return period of rainfall. This study offers a good reference for the relevant government departments to make effective policy and take pointed response to the waterlogging problem. |
| Address |
Tsinghua University; Tsinghua University; Tsinghua University; Beijing Water Authority; Electric Power Planning & Engineering Institute; Tsinghua University |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Analytical Modeling and Simulation |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
nxy15@mails.tsinghua.edu.cn |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2322 |
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| Author |
Xiaojing Guo; Xinzhi Wang; Luyao Kou; Hui Zhang |
| Title |
A Question Answering System Applied to Disasters |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
2-16 |
| Keywords |
Emergency Management, Disaster, Natural Language Processing, Deep Learning |
| Abstract |
In emergency management, identifying disaster information accurately and promptly out of numerous documents like news articles, announcements, and reports is important for decision makers to accomplish their mission efficiently. This paper studies the application of the question answering system which can automatically locate answers in the documents by natural language processing to improve the efficiency and accuracy of disaster knowledge extraction. Firstly, an improved question answering model was constructed based on the advantages of the existing neural network models. Secondly, the English question answering dataset pertinent to disasters and the Chinese question answering dataset were constructed. Finally, the improved neural network model was trained on the datasets and tested by calculating the F1 and EM scores which indicated that a higher question answering accuracy was achieved. The improved system has a deeper understanding of the semantic information and can be used to construct the disaster knowledge graph. |
| Address |
Institute of Public Safety Research, Tsinghua University; School of Computer Engineering and Science, Shanghai University; Institute of Public Safety Research, Tsinghua University; Institute of Public Safety Research, Tsinghua University |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
gxj19@mails.tsinghua.edu.cn |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2308 |
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| |
|
| |
| Author |
Victor A. Bañuls; Andrzej M. Skulimowski; José Antonio Román Begines |
| Title |
Disaster Resilience Modeling of Municipal Water Supply Infrastructures in the Context of Atmospheric Threats |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
198-207 |
| Keywords |
Disaster Modeling, Urban Resilience, Water Supply Infrastructures, Climate Change, Scenarios |
| Abstract |
The resilience of water supply infrastructure (WSI) is of utmost importance as threats to predominantly, although not exclusively, urban WSI may accompany virtually all kinds of natural disasters. In this paper, we present some of the challenges posed by climate change in modeling emergencies in WSIs. Climate change is a global phenomenon that significantly impacts global lifestyle. It is expected that increase in global temperatures causes sea levels to rise, increases the number of extreme weather events such as floods, droughts, and storms while highly impacting WSI. In this respect, the challenge is to be prepared for the unexpended by modeling various complex scenarios. Only with a multidisciplinary approach at the global, regional, national, and local levels, can success be achieved. We discuss some of the specific challenges posed by climate change in modeling emergencies in WSIs with a case study modeled using EMERTIC. EMERTIC is a software based on AI and scenarios, that is aimed at supporting decision making at different stages of the Emergency Management cycle. |
| Address |
Universidad Pablo de Olavide; AGH University of Science and Technology; EMASESA |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Analytical Modeling and Simulation |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
vabansil@upo.es |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2325 |
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| |
|
| |
| Author |
Valerio Lorini; Carlos Castillo; Steve Peterson; Paola Rufolo; Hemant Purohit; Diego Pajarito; João Porto de Albuquerque; Cody Buntain |
| Title |
Social Media for Emergency Management: Opportunities and Challenges at the Intersection of Research and Practice |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
772-777 |
| Keywords |
Crisis Informatics, Social Media, Workshop Report, Disaster Management |
| Abstract |
This paper summarizes key opportunities and challenges identified during the workshop “Social Media for Disaster Risk Management: Researchers Meet Practitioners” which took place online in November 2020. It constitutes a work-in-progress towards identifying new directions for research and development of systems that can better serve the information needs of emergency managers. Practitioners widely recognize the potential of accessing timely information from social media. Nevertheless, the discussion outlined some critical challenges for improving its adoption during crises. In particular, validating such information and integrating it with authoritative information and into more traditional information systems for emergency managers requires further work, and the negative impacts of misinformation and disinformation need to be prevented. |
| Address |
European Commission, Joint Research Centre (JRC), Ispra, Italy; Universitat Pompeu Fabra, Barcelona, Spain; Community Emergency Response Team, Montgomery County, Maryland, USA; European Commission, Joint Research Centre, Ispra, Italy; George Mason Univers |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
valerio.lorini@ec.europa.eu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2372 |
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| |
| Author |
Valentin Barriere; Guillaume Jacquet |
| Title |
How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
766-771 |
| Keywords |
Transformers, Contextual keywords, Humanitarian Computing, Tweets analysis |
| Abstract |
In a classification task, dealing with text snippets and metadata usually requires to deal with multimodal approaches. When those metadata are textual, it is tempting to use them intrinsically with a pre-trained transformer, in order to leverage the semantic information encoded inside the model. This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. Based on additional experiments of the model weights and behavior, it identifies how the proposed neural network approach is partially over-fitting the particularities of the Crisis Benchmark, to better highlight how the model is still undoubtedly learning to use and take advantage of the metadata's textual semantics. |
| Address |
European Commission's Joint Research Center; European Commission's Joint Research Center |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
valbarrierepro@gmail.com |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2371 |
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| |
| Author |
Usman Anjum; Vladimir Zadorozhny; Prashant Krishnamurthy |
| Title |
TBAM: Towards An Agent-Based Model to Enrich Twitter Data |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
146-158 |
| Keywords |
Agent-Based Model, Twitter, Modeling and Simulation, Event Detection |
| Abstract |
Twitter is widely being used by researchers to understand human behavior, e.g. how people behave when an event occurs and how it changes their microblogging pattern. The changing microblogging behavior can have an important application in the form of detecting events. However, the Twitter data that is available has limitations in it has incomplete and noisy information and has irregular samples. In this paper we create a model, calledTwitter Behavior Agent-Based Model (TBAM)to simulate Twitter pattern and behavior using Agent-Based Modeling(ABM). The generated data can be used in place or to complement the real-world data and improve the accuracy of event detection. We confirm the validity of our model by comparing it with real data collected from Twitter |
| Address |
University of Pittsburgh; University of Pittsburgh; University of Pittsburgh |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Analytical Modeling and Simulation |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
usa3@pitt.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2321 |
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| |
| Author |
Tobias Meuser; Lars Baumgärtner; Patrick Lieser |
| Title |
Pandemic Skylines: Digital Twins for More Realism in Epidemic Simulations |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
133-145 |
| Keywords |
Simulation, Disaster Communication, Pandemic |
| Abstract |
In the recent months, many measures have been taken by governments to fight the COVID-19 pandemic. Due to the unknown properties of the disease and a lack of experience with handling pandemics, the effectiveness of measures taken was often hard to evaluate the effectiveness of measures, leading to inefficient measures and late execution of efficient measures. Many models have been proposed to evaluate the performance of these measures on the spreading of a pandemic, but these models are commonly vastly simplified and, thus, limited in expressiveness. To extend the expressiveness of the models, we developed a epidemic simulation inside of a flexible and scalable city simulation game to analyse the counter measures to a pandemic in this city and spot common places of infection on a microscopic level. The configurability of our developed epidemic simulation will also be useful for potential future pandemics. |
| Address |
TU Darmstadt – KOM; TU Darmstadt – STG; TU Darmstadt – KOM |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Analytical Modeling and Simulation |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
tobias.meuser@kom.tu-darmstadt.de |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2320 |
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| |
| Author |
Tobias Hellmund; Jürgen Moßgraber; Manfred Schenk; Philipp Hertweck; Hylke van der Schaaf; Hans Springer |
| Title |
The Design and Implementation of ZEUS: Novel Support in Managing Large-Scale Evacuations |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
1003-1014 |
| Keywords |
Management of Large-Scale Evacuations, Emergency Accommodation Management, Evacuation Management |
| Abstract |
This paper introduces ZEUS, a novel software tool for the management of large-scale evacuations. The tasks ZEUS supports were derived from two Standard Operating Procedures, developed on demand of the German federal states. To this date, the authors are not aware of another software tool that gives technical support to the management and control of large-scale evacuations as ZEUS does. It comprises functionalities to (pre-)plan a large-scale evacuation, as well as functions for the management of the flow of evacuees during an evacuation situation. This paper describes how the requirements of ZEUS were derived from the two named planning frameworks and how use-cases were developed to meet the requirements; these use-cases were conceptualized as different steps of a workflow. In an evaluation, the paper gives credit how ZEUS can provide technical support for the evaluation of large-scale evacuations. ZEUS will undergo a two-staged review process: first, a controlled theoretical scenario is tested and, upon successful completion, a practical test on a large scale will be executed. |
| Address |
Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Ministry of Interior, Digitization, and Migration Baden-Württemberg |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Other |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
tobias.hellmund@iosb.fraunhofer.de |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2392 |
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| |
| Author |
Tinghao Zhang; Lida Huang; Tao Chen; Shuo Bai |
| Title |
GIS Based Emergency Management Framework for Large-scale Events: A Case Study of the Torch Relay Activity |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
503-514 |
| Keywords |
Arcgis, large-scale event, emergency management, epidemic prevention |
| Abstract |
Due to the high popular concentration of large-scale events, once an emergency (like a stampede) occurs, it will often cause severe casualties. Moreover, since the widespread of the COVID-19, the prevention of the novel coronavirus should also be considered during mass gatherings. How to reduce the probability and potential consequence of emergencies is of great significance. This research designs an emergency management framework using ArcGIS-based geographic information technology for large-scale events. To verify the effectiveness of our framework, we take the Winter Olympic torch relay in university as an example. The paper is mainly divided into two parts, emergency resource allocation and the emergency prevention model. The former part focuses on the site selection of emergency sentries and emergency hospitals during the torch relay. In the latter part, an emergency prevention model is designed for two significant emergencies: stampede and epidemic. |
| Address |
Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua University Hefei |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Geospatial Technologies and Geographic Information Science for Crisis Management (GIS) |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
zth19@mails.tsinghua.edu.cn |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2351 |
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| Author |
Tina Mioch; Reinier Sterkenburg; Tatjana Beuker; Mark A. Neerincx |
| Title |
Actionable Situation Awareness: Supporting Team Decisions in Hazardous Situations |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
62-70 |
| Keywords |
Situation Awareness, Actionability, Decision support, Chemical hazard |
| Abstract |
Situation Awareness (SA) has been recognized and studied as an important requirement for an effective task performance of first responders. The integration of increasingly advanced sensor, network and artificial intelligence technology into the work processes affects the building, maintenance and sharing of SA. Connecting SA to decision support models provides new possibilities for the development of actionable SA (aSA), entailing information that guides the momentary decision-making processes of the concerning actors. In the European ASSISTANCE project, we are developing an aSA module that displays information about gas distributions, its current and predicted future states (e.g., entailing risks of breathing-in of toxic gases), with references to effective decision-making patterns for this situation. The aSA model is continuously updated based on sensor data. This paper gives an overview of this aSA module for chemical hazard prediction and corresponding display, and presents initial team design patterns that will be integrated into this display to support its actionability. |
| Address |
Tno; Tno; Tno; Tno |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
tina.mioch@tno.nl |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2313 |
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| Author |
Tiina Ristmae; Dimitra Dionysiou; Miltiadis Koutsokeras; Athanasios Douklias; Eleftherios Ouzounoglou; Angelos Amditis; Anaxagoras Fotopoulos; George Diles; Pantelis Linardatos; Konstantinos Smanis; Pantelis Lappas; Marios Moutzouris; Manolis Tsogas; Dani |
| Title |
The CURSOR Search and Rescue (SaR) Kit: an innovative solution for improving the efficiency of Urban SaR Operations |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
867-880 |
| Keywords |
Urban Search and Rescue, Victim detection, Rescue robotics, Sensors, Situational awareness |
| Abstract |
CURSOR (Coordinated Use of miniaturized Robotic equipment and advanced Sensors for search and rescue OpeRations) is an ongoing European H2020 project with the main objective to enhance the efficiency and safety of Urban Search and Rescue (USaR) operations on disaster sites. CURSOR's approach relies on the integration of multiple mature and emerging technologies offering complementary capabilities to an USaR system, so as to address several challenges and capability gaps currently encountered during first responder missions. The project's research and development are structured around an earthquake master scenario. CURSOR aspires to advance the state-of the-art in several key aspects, including reduced time for victim detection, increased victim localization accuracy, enhanced real-time worksite information management, improved situational awareness and rescue team safety. |
| Address |
Federal Agency for Technical Relief (THW) – Headquarters Staff Unit Research & Innovation Management; Institute of Communication and Computer Systems – National Technical University of Athens; Institute of Communication and Computer Systems – National Tec |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Technologies for First Responders |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
Tiina.Ristmaee@thw.de |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2379 |
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| Author |
Tiberiu Sosea; Iustin Sirbu; Cornelia Caragea; Doina Caragea; Traian Rebedea |
| Title |
Using the Image-Text Relationship to Improve Multimodal Disaster Tweet Classification |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
691-704 |
| Keywords |
Multi-modal disaster tweet classification, Image-text coherence relationship prediction, ViLBERT |
| Abstract |
In this paper, we show that the text-image relationship of disaster tweets can be used to improve the classification of tweets from emergency situations. To this end, we introduce DisRel, a dataset which contains 4,600 multimodal tweets, collected during the disasters that hit the USA in 2017, and manually annotated with coherence image-text relationships, such as Similar and Complementary. We explore multiple models to detect these relationships and perform a comprehensive analysis into the robustness of these methods. Based on these models, we build a simple feature augmentation approach that can leverage the text-image relationship. We test our methods on 2 tasks in CrisisMMD: Humanitarian Categories and Damage Assessment, and observe an increase in the performance of the relationship-aware methods. |
| Address |
University of Illinois at Chicago; University Politehnica of Bucharest; University of Illinois at Chicago; Kansas State University; University Politehnica of Bucharest |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
tsosea2@uic.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2365 |
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| Author |
Therese Habig; Richard Lüke; Simon Gehlhar; Torben Sauerland; Daniel Tappe |
| Title |
A Consolidated Understanding of Disaster Community Technologies |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
778-791 |
| Keywords |
Disaster Community Technologies, social media and crowdsourcing, categorization and classification schema, knowledge base |
| Abstract |
Since the beginning of this millennium, there has been an increasing use of social media and crowdsourcing (SMCS) technologies in disaster situations (Reuter & Kaufhold, 2018). Disaster management organizations and corresponding research are increasingly working on ways of integrating SMCS into the processes of crisis management. In a changing technological landscape to address disasters, and with increasing diversity of stakeholders in disasters, the purpose of this research is to provide an overview of technologies for SMCS within disasters to improve community resilience. The identified and analyzed technologies are summarized under the term “Disaster Community Technologies” (DCT). The paper presents a classification schema (the “DCT-schema”) for those technologies. The goal is to generate an overview of DCT in a rapidly evolving environment and to provide the practical benefit for different stakeholders to identify the right one from the overview. |
| Address |
safety innovation center; safety innovation center; safety innovation center; safety innovation center; safety innovation center |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
habig@safetyinnovation.center |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2373 |
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| Author |
Stella van Esch; Marc van den Homberg; Kees Boersma |
| Title |
Looking Beyond the Data: an Assessment of the Emerging Data Ecosystem of Nepal's Flood Early Warning Systems |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
282-293 |
| Keywords |
Floods, early warning systems, Nepal, data ecosystem, social shaping of technology |
| Abstract |
Increasingly, data-driven instruments are used in disaster risk reduction to foster more efficient, effective, and evidence-based decision-making. This data revolution brings along opportunities and challenges, which are sometimes related to the data itself, but more often seem related to the environment in which the data is put to use. To provide insight into such an emerging data ecosystem, this paper uses a qualitative case study to assess the use of data in flood early warning systems (EWS) in Nepal. In response to the research question 'How does the data ecosystem impact the opportunities and challenges regarding data use in flood early warning systems in Nepal?', this paper discusses the importance of considering the broader context instead of regarding data as an entity unto itself. It shows how actors, policies and other contextual factors impact the effectiveness of data use by either presenting opportunities, like the establishment of a national disaster data repository, or challenges, like inadequate human resources for working with data. |
| Address |
Vrije Universiteit Amsterdam; 510 – an initiative of The Netherlands Red Cross; Vrije Universiteit Amsterdam |
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Thesis |
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| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
978-1-949373-61-5 |
ISBN |
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Medium |
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| Track |
Data and Resilience: Opportunities and Challenges |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
s.r.van.esch@student.vu.nl |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2333 |
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| Author |
Stella Polikarpus; Tobias Ley; Katrin Poom-Valickis |
| Title |
Collaborative Authoring of Virtual Simulation Scenarios for Assessing Situational Awareness |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
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Issue |
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Pages |
229-237 |
| Keywords |
situational awareness (SA), virtual simulation, virtual simulation scenario, process model, Effective Command Behavioral Marker Framework |
| Abstract |
Situational awareness (SA), the ability to perceive, comprehend and predict situation around you and it is a key in attending any incident as critical foundation for successful decision-making. Because incidents are solitary events, development and assessment of SA presents a significant challenge. In this article we analyze the authoring process of twenty-two scenarios implemented in the XVR on-scene virtual simulation software used to assess rescue incident commanders' (ICs) SA. To allow the scenarios to be used by different assessors, the Collaborative Authoring Process Model for Virtual Simulation Scenarios (CAPM) was developed. In Estonia, 473 assessments were recorded in Effective Command database and analysed by all three levels of SA as recommended by Endsley (2000). Introduction of CAPM resulted in scenarios being re-used by different assessors for authentic SA measuring. In the last sections of this article, we introduce our suggestions to improve virtual scenario design and SA research. |
| Address |
Tallinn University; Tallinn University; Tallinn University |
| Corporate Author |
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Thesis |
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| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
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| ISSN |
978-1-949373-61-5 |
ISBN |
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Medium |
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| Track |
Command & Control Studies |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
stella.polikarpus@gmail.com |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2328 |
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