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Author
Koki Asami
;
Shono Fujita
;
Kei Hiroi
;
Michinori Hatayama
Title
Data Augmentation with Synthesized Damaged Roof Images Generated by GAN
Type
Conference Article
Year
2022
Publication
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management
Abbreviated Journal
Iscram 2022
Volume
Issue
Pages
256-265
Keywords
disaster response
;
generative adversarial networks
;
data augmentation
;
damage classification
Abstract
The lack of availability of large and diverse labeled datasets is one of the most critical issues in the use of machine learning in disaster prevention. Natural disasters are rare occurrences, which makes it difficult to collect sufficient disaster data for training machine learning models. The imbalance between disaster and non-disaster data affects the performance of machine learning algorithms. This study proposes a generative adversarial network (GAN)- based data augmentation, which generates realistic synthesized disaster data to expand the disaster dataset. The effect of the proposed augmentation was validated in the roof damage rate classification task, which improved the recall score by 11.4% on average for classes with small raw data and a high ratio of conventional augmentations such as rotation of image, and the overall recall score improved by 3.9%.
Address
Kyoto University; Kyoto University; Kyoto University; Kyoto University
Corporate Author
Thesis
Publisher
Place of Publication
Tarbes, France
Editor
Rob Grace; Hossein Baharmand
Language
English
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
2411-3387
ISBN
978-82-8427-099-9
Medium
Track
AI and Intelligent Systems for Crises and Risks
Expedition
Conference
Notes
Approved
no
Call Number
ISCRAM @ idladmin @
Serial
2415
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