Image classification to support emergency situation awareness

Lagerstrom, Ryan, Arzhaeva, Yulia, Szul, Piotr, Obst, Oliver, Power, Robert, Robinson, Bella, & Bednarz, Tomasz (2016) Image classification to support emergency situation awareness. Frontiers in Robotics and AI, 3, Article 54.

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Abstract

Recent advances in image classification methods, along with the availability of associated tools, have seen their use become widespread in many domains. This paper presents a novel application of current image classification approaches in the area of Emergency Situation Awareness. We discuss image classification based on low-level features as well as methods built on top of pretrained classifiers. The performance of the classifiers is assessed in terms of accuracy along with consideration to computational aspects given the size of the image database. Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW, where images associated with Tweets during the emergency were used to train and test classification approaches. Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies. We show that these methodologies can classify images into fire and not fire-related classes with an accuracy of 86%.

Impact and interest:

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ID Code: 98961
Item Type: Journal Article
Refereed: Yes
Keywords: emergency awareness, image classification, social media, classification, image processing, emergency response, machine learning, situation awareness
DOI: 10.3389/frobt.2016.00054
ISSN: 2296-9144
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute for Future Environments
Current > Schools > School of Mathematical Sciences
Copyright Owner: Copyright 2016 Lagerstrom, Arzhaeva, Szul, Obst, Power, Robinson and Bednarz
Copyright Statement: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Deposited On: 21 Sep 2016 22:27
Last Modified: 22 Sep 2016 23:34

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