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Paper Abstract and Keywords
Presentation 2021-10-22 10:50
Differences in Classification Accuracy of Landslide Hazard using Fixed-point Observation Images due to Network and Image Processing in Deep Learning
Keisuke Tokumoto, Makoto Kobayashi, Koichi Shin, Masahiro Nishi (Hiroshima City Univ.) ICTSSL2021-26
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years,several landslides included by heavy rains have caused a lot of human damage in Hiroshima. Early evacuation is necessary to prevent human damage caused by landslides. In order to encourage early evacuation,we have installed a camera system at the dangerous area of landslides to obtain real-time images there.The real-time image can be viewed on a web page.In addition,it would be good to display a numerical index of danger along with this image. In this paper,we attempted to classify the degree of risk using deep learning for fixed-point images observed at dangerous area.In our trial,we measured and evaluated the accuracy with a more accurate network and image processing,as well as representative values that are closer to the actual danger.The networks of 10 popular models were evaluated for image classification.However,these networks have not been constructed on the assumption that they will be applied to fixed-point observation images.Therefore,the purpose is to consider a network structure suitable for fixed-point images by measuring the accuracy.And we aims to extract the intensity of changes in the water surface by previously processing images used for deep learning using images with different times.The purpose of the representative value is to calculate a value that is closer to the actual danger than the conventional method.These methods were examined using images obtained by actual observation.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Landslide Disaster / Hazard Classification / Image Processing / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 208, ICTSSL2021-26, pp. 48-53, Oct. 2021.
Paper # ICTSSL2021-26 
Date of Issue 2021-10-14 (ICTSSL) 
ISSN Online edition: ISSN 2432-6380
Copyright
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee ICTSSL IEE-SMF IN  
Conference Date 2021-10-21 - 2021-10-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ICTSSL 
Conference Code 2021-10-ICTSSL-SMF-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Differences in Classification Accuracy of Landslide Hazard using Fixed-point Observation Images due to Network and Image Processing in Deep Learning 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Landslide Disaster  
Keyword(3) Hazard Classification  
Keyword(4) Image Processing  
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1st Author's Name Keisuke Tokumoto  
1st Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
2nd Author's Name Makoto Kobayashi  
2nd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
3rd Author's Name Koichi Shin  
3rd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
4th Author's Name Masahiro Nishi  
4th Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
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Speaker Author-1 
Date Time 2021-10-22 10:50:00 
Presentation Time 25 minutes 
Registration for ICTSSL 
Paper # ICTSSL2021-26 
Volume (vol) vol.121 
Number (no) no.208 
Page pp.48-53 
#Pages
Date of Issue 2021-10-14 (ICTSSL) 


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