Rob Grace, & Michelle Potts. (2022). Opportunities for Multisensor Integration in Public-Safety Answering Points. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 895–904). Tarbes, France.
Abstract: Public-Safety Answering Points (PSAPs) coordinate emergency response by gathering critical information from 911 callers for dispatch to first responders. However, PSAPs fail to gather this information if 911 callers are unable, unwilling, or unavailable to report key details about an emergency. To address this problem, early-adopter PSAPs employ communication specialists to gather multimedia information from multiple sensing tools, including automated alarms, cameras, government databases, location systems, open-source websites, social media, and alternative communication channels such as text-to-911. Using preliminary usage data from an early- adopter PSAP, this study identifies 11 breakdowns in 911 call taking that create opportunities for multisensor integration. This study then characterizes use cases for multisensor tools based on usage patterns observed across five incident types. These findings highlight multisensor integration as a critical area for crisis informatics research.
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Zainab Akhtar, Ferda Ofli, & Muhammad Imran. (2021). Towards Using Remote Sensing and Social Media Data for Flood Mapping. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 536–551). Blacksburg, VA (USA): Virginia Tech.
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.
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Artur Ricardo Bizon, Luciana P. de Araújo Kohler, Adilson Luiz Nicoletti, Fernanda Dal Bosco, Murilo Schramm da Silva, & Thales Bohn Pessatti. (2020). Integration statistical systems for land cover mapping in Southern Brazil. 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. 498–505). Blacksburg, VA (USA): Virginia Tech.
Abstract: The remote sensing is a way to optimize the process of land cover classification allowing that this process will be by high definition images of satellite. For the research it was used the Google Earth Engine with JavaScript programming language to classify the images, identifying the areas with forest or reforest. It was identified that classifiers Random Forest and Logistic Regression have a high performance in classify the images. From them it was developed functions to process automatically of new images with purpose of classify them in relation to land cover.
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Mohamad Rukieh. (2007). The effects of lineaments and epicentres on risk reduction in arabian rift zone. 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. 227–234). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper describes the relationship between lineaments, which determined on space images, and the epicenters and their effects on spatial planning for risk reduction. Several studies have shown that most of the epicenters occur along these lineaments or their zones, or in the block regions which are bordered by these lineaments, or where these lineaments and different tectonic deformation are intersected. This paper presents a case study on the Arabian Rift Zone which is based on the linkages among lineaments, faults, and earthquakes that occurred in the region during 1910-93. Also, this study will show that most of these earthquakes were occurred along the main and secondary rift faults or in their zone, including the faults found in sea that helped in determining the courses of these earthquakes in the sea bottom. This confirms the importance of remote sensing techniques for providing space images of different scales in seismic studies.
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Muhammad Tauhidur Rahman, & Tarek Rashed. (2007). Towards a geospatial approach to post-disaster environmental impact assessment. 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. 219–226). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Natural disasters often leave profound impacts on the environment. Existing disaster impact assessment methods fall short in facilitating the relief work and in conducting cross-sectional comparison of various facets of such impacts. The development of a standardized index for measuring/monitoring the environmental impacts of disasters is necessary to address this gap. This paper proposes a conceptual framework to study the environmental impacts via remote sensing/GIS based geospatial analytical approach by developing a post-disaster environmental severity index. It considers physical, social and built-in components of the environment and identifies several key indicators of disaster impacts. Through statistical decomposition of a large number of environmental impact indicators, the study proposes a composite post-disaster environmental severity index (PDESI). Mapping of the proposed index would help identification of areas and component of the environment that are severely affected by a disaster, and formulation of disaster mitigation and damage recovery plans accordingly.
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