Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63810

DisasterNet: Evaluating the Performance of Transfer Learning to Classify Hurricane-Related Images Posted on Twitter

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Title:DisasterNet: Evaluating the Performance of Transfer Learning to Classify Hurricane-Related Images Posted on Twitter
Authors:Johnson, Matthew
Murthy, Dhiraj
Roberstson, Brett
Smith, Roth
Stephens, Keri
Keywords:Information and Communication Technologies for Crisis and Emergency Management
classification
deep learning
disasters
social media
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Date Issued:07 Jan 2020
Abstract:Social media platforms are increasingly used during disasters. In the U.S., victims consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our transfer learning framework classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification, but also that real-time classification of social media images using a small training set is possible.
Pages/Duration:8 pages
URI:http://hdl.handle.net/10125/63810
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.071
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Information and Communication Technologies for Crisis and Emergency Management


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