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Application of Image Analytics for Disaster Response in Smart Cities

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Title:Application of Image Analytics for Disaster Response in Smart Cities
Authors:Chaudhuri, Neha
Bose, Indranil
Keywords:Disaster Information, Technology, and Resilience in Digital Government
Digital Government
convolutional neural networks, disaster management, image analytics, smart cities, TensorFlow
Date Issued:08 Jan 2019
Abstract:Post-disaster, city planners need to effectively plan response activities and assign rescue teams to specific disaster zones quickly. We address the problem of lack of accurate information of the disaster zones and existence of human survivors in debris using image analytics from smart city data. Innovative usage of smart city infrastructure is proposed as a potential solution to this issue. We collected images from earthquake-hit smart urban environments and implemented a CNN model for classification of these images to identify human body parts out of the debris. TensorFlow backend (using Keras) was utilized for this classification. We were able to achieve 83.2% accuracy from our model. The novel application of image data from smart city infrastructure and the resultant findings from our model has significant implications for effective disaster response operations, especially in smart cities.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59740
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.367
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Disaster Information, Technology, and Resilience in Digital Government


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