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

Date
2020-01-07
Authors
Johnson, Matthew
Murthy, Dhiraj
Roberstson, Brett
Smith, Roth
Stephens, Keri
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
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.
Description
Keywords
Information and Communication Technologies for Crisis and Emergency Management, classification, deep learning, disasters, social media, twitter
Citation
Extent
8 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 53rd Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.