@inproceedings{9316f711bb844778b521c6ada3ca2e3b,
title = "Challenge of Roof Damage Housings Detection from Satellite Images by Applying Deep Learning Methodology: -A Case Study of Ibaraki City at 2018 Osaka Earthquake-",
abstract = "In Japan, we were suffered by many kinds of disasters. Once disaster occurs, we have to develop the common operational picture including damage situation in order to realize effective disaster response. However, it should take much time-cost to gather the damage situation. Against this issue, we decided to detect blue sheets object put on the damaged roof in recovery phase of disaster response. In this research, we tried to detect damage situation from satellite images by utilizing deep learning methodology. Especially, we adopt VGG-16 model developed by Oxford university, which gained fourth prize of ILSVRC in 2014. We prepared training data and applied it to actual affected area by 2018 Osaka earthquake as a case study. Finally, we confirmed that our trained AI detected blue sheet object with about 95% accuracy ratio.",
keywords = "common operational picture, deep learning, disaster response, roof damage, satellite image",
author = "Munenari Inoguchi and Seiichi Kara and Kazuya Shirai and Atsushi Imai",
note = "Publisher Copyright: {\textcopyright} 2020 IEICE.; 35th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2020 ; Conference date: 03-07-2020 Through 06-07-2020",
year = "2020",
month = jul,
language = "英語",
series = "ITC-CSCC 2020 - 35th International Technical Conference on Circuits/Systems, Computers and Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "172--176",
booktitle = "ITC-CSCC 2020 - 35th International Technical Conference on Circuits/Systems, Computers and Communications",
}