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Abstract
Recent years, typhoon damages has become social problem owing to climate change. In 9 September 2019, Typhoon Faxai passed on the Chiba in Japan, whose damages included with electric provision stop because of strong wind recorded on the maximum 45 m/s. A large amount of tree fell down, and the neighbour electric poles also fell down at the same time. These disaster features have caused that it took 18 days for recovery longer than past ones. Immediate responses are important for faster recovery. As long as we can, aerial survey for global screening of devastated region would be required for decision support to respond where to recover ahead. This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography. This method can classify eight classes which contains land covers without damages and areas with disaster. Using target feature class probabilities, we can visualize disaster feature map to scale a colour range. Furthermore, we can realize explainable map on each unit grid images to compute the convolutional activation map using Grad-CAM. We demonstrate case studies applied to aerial photographs recorded at the Chiba region after typhoon.
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Acknowledgements
We gratefully acknowledge the constructive comments of the anonymous referees. Support was given by the Aero Asahi Co. of Jun Miura, who provided us the aerial photographs recorded at the Chiba after the Typhoon Faxai. We thank Takuji Fukumoto and Shinichi Kuramoto for supporting us MATLAB resources.
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Authors and Affiliations
Research Institute for Infrastructure Paradigm Shift, Yachiyo Engineering Co., Ltd., Asakusabashi 5-20-8, Taito-ku, Tokyo, Japan
Takato Yasuno, Masazumi Amakata & Masahiro Okano
- Takato Yasuno
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- Masazumi Amakata
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- Masahiro Okano
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Correspondence toTakato Yasuno.
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Editors and Affiliations
Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy
Alberto Del Bimbo
Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy
Rita Cucchiara
Department of Computer Science, Boston University, Boston, MA, USA
Stan Sclaroff
Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy
Giovanni Maria Farinella
Cloud & AI, JD.COM, Beijing, China
Tao Mei
Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy
Marco Bertini
Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico
Hugo Jair Escalante
Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy
Roberto Vezzani
Appendix
Appendix
(Left) House roof break feature map, (Right) Visual explanation of roof break (red-blue range) using Grad-CAM, each pair of original clip and activation map, roof break covered with vinyl seat. The red is positive roof break affected by strong wind (Color figure online)
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Yasuno, T., Amakata, M., Okano, M. (2021). Natural Disaster Classification Using Aerial Photography Explainable for Typhoon Damaged Feature. In: Del Bimbo, A.,et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_2
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