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Paper Abstract and Keywords
Presentation 2022-06-09 17:15
Visualization of decisions from CNN models trained on OpenStreetMap images labeled based on traffic accident data
Kaito Arase, Zhijian Wu, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2022-10 CCS2022-10
Abstract (in Japanese) (See Japanese page) 
(in English) The authors have recently conducted training of Convolutional Neural Networks (CNNs) on OpenStreetMap images each of which is labeled as ``danger'' or ``safe'' based on traffic accident data. Although the trained CNNs determine whether each area is danger or safe from the map image correctly with a high probability, the reason for this and the basis for their decisions are not clear. In this report, we use a method called Grad-CAM to visualize the basis of CNNs' decisions after learning map images as described above. The visualization result of the Grad-CAM depends on the convolutional layer of the CNN it is applied. The closer the layer is to the output layer, the better the features can be captured, but the resolution of the visualization is lower. Conversely, the closer the layer is to the input layer, the higher the resolution of the visualization, but the less well the features are captured. By analyzing the visualization results of the Grad-CAM for different convolutional layers of different models, we show that there certainly exist convolutional layers suitable for visualization, and clarify some characteristics of the trained CNNs when making their decisions.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / map image / OpenStreetMap / Grad-CAM / traffic accident data / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 65, NLP2022-10, pp. 46-51, June 2022.
Paper # NLP2022-10 
Date of Issue 2022-06-02 (NLP, CCS) 
ISSN Online edition: ISSN 2432-6380
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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 CCS NLP  
Conference Date 2022-06-09 - 2022-06-10 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2022-06-CCS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Visualization of decisions from CNN models trained on OpenStreetMap images labeled based on traffic accident data 
Sub Title (in English)  
Keyword(1) deep learning  
Keyword(2) map image  
Keyword(3) OpenStreetMap  
Keyword(4) Grad-CAM  
Keyword(5) traffic accident data  
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1st Author's Name Kaito Arase  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Zhijian Wu  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Tsuyoshi Migita  
3rd Author's Affiliation Okayama University (Okayama Univ.)
4th Author's Name Norikazu Takahashi  
4th Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2022-06-09 17:15:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2022-10, CCS2022-10 
Volume (vol) vol.122 
Number (no) no.65(NLP), no.66(CCS) 
Page pp.46-51 
#Pages
Date of Issue 2022-06-02 (NLP, CCS) 


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