Japan Society of Civil Engineers 2024 Annual Meeting
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Session Details

[共通セッション] 土木分野におけるAIの活用

土木分野におけるAIの活用（その6）

Fri. Sep 6, 2024 9:00 AM - 10:20 AM JST
Fri. Sep 6, 2024 12:00 AM - 1:20 AM UTC
第2講義室(経済学部)(川内南キャンパス文科系総合講義棟)
Chair: Koji Mano
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9:00 AM - 9:10 AM JST(12:00 AM - 12:10 AM UTC)

[CS14-36]Deep learning of satellite imagery for detecting landslide locations during earthquakes.

*Kyoya Hara1, Yoshihisa Maruyama1, Wen Liu1 (1. Graduated School of Chiba University)
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9:10 AM - 9:20 AM JST(12:10 AM - 12:20 AM UTC)

[CS14-37]Flood Mapping from Optical Satellite Imagery Using Multi-modal Deep Learning

*Takuto Sato1, Takashi Miyamoto1 (1. University of Yamanashi)
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9:20 AM - 9:30 AM JST(12:20 AM - 12:30 AM UTC)

[CS14-38]Automatic Post-Earthquakes RC Building Damage Assessment using machine learning

*Lama Hajmousa1 (1. Student)
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9:30 AM - 9:40 AM JST(12:30 AM - 12:40 AM UTC)

[CS14-39]Detecting building damage caused by typhoons using remote sensing technology and deep learning

*Haruto Konishi1, Takashi Miyamoto1, Mitsuyoshi Kunugihara2, Daisuke Nakai2 (1. Yamanashi University, 2. Mitsui Sumitomo Insurance Company, Limited)
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9:40 AM - 9:50 AM JST(12:40 AM - 12:50 AM UTC)

[CS14-40]Research on Flood Inundation Area Prediction by Assimilation of Deep Learning and Flood Simulation

*Jun Ogishima1, Takashi Miyamoto1 (1. University of Yamanashi)
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9:50 AM - 10:00 AM JST(12:50 AM - 1:00 AM UTC)

[CS14-41]Basic Study for Detecting Earthquake Damaged Buildings Using Aerial Laser Survey Data and Aerial Photographs

*Ayana Egashira1, Wen Liu1, Yoshihisa Maruyama1 (1. Chiba University)
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10:00 AM - 10:10 AM JST(1:00 AM - 1:10 AM UTC)

[CS14-42]Building the Object Detection Model for Building Damage Assessment Using Photos Showing the disaster Victims in the Kumamoto Earthquake

*Ryota Akiba1, Wen LIU2, Yoshihisa MARUYAMA2 (1. Graduated School of Science and Engineering , Chiba University, 2. Graduated School of Engineering , Chiba University)
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10:10 AM - 10:20 AM JST(1:10 AM - 1:20 AM UTC)

[CS14-43]Detecting Flooded Areas Using Aerial Imagery with Deep Learning

*Kodai Yamada1, Shiori Kubo1, Hidenori Yoshida1 (1. Kagawa University)
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