Zobrazeno 1 - 10
of 1 659
pro vyhledávání: '"Koshimura A"'
In recent years, unmanned aerial vehicles (UAVs) have played an increasingly crucial role in supporting disaster emergency response efforts by analyzing aerial images. While current deep-learning models focus on improving accuracy, they often overloo
Externí odkaz:
http://arxiv.org/abs/2409.00510
Autor:
Takahashi, Keichi, Abe, Takashi, Musa, Akihiro, Sato, Yoshihiko, Shimomura, Yoichi, Takizawa, Hiroyuki, Koshimura, Shunichi
To issue early warnings and rapidly initiate disaster responses after tsunami damage, various tsunami inundation forecast systems have been deployed worldwide. Japan's Cabinet Office operates a forecast system that utilizes supercomputers to perform
Externí odkaz:
http://arxiv.org/abs/2408.07609
With the rapid development of earth observation technology, we have entered an era of massively available satellite remote-sensing data. However, a large amount of satellite remote sensing data lacks a label or the label cost is too high to hinder th
Externí odkaz:
http://arxiv.org/abs/2405.17734
Autor:
Bai, Yanbing, Yang, Zihao, Yu, Jinze, Ju, Rui-Yang, Yang, Bin, Mas, Erick, Koshimura, Shunichi
With the escalating frequency of floods posing persistent threats to human life and property, satellite remote sensing has emerged as an indispensable tool for monitoring flood hazards. SpaceNet8 offers a unique opportunity to leverage cutting-edge a
Externí odkaz:
http://arxiv.org/abs/2404.18235
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-4-2024, Pp 433-440 (2024)
With the rapid development of sensing platforms, unmanned aerial vehicle (UAV)-based mapping has become increasingly popular because of its economic efficiency and flexibility, especially for providing 3D information to support urban growth monitorin
Externí odkaz:
https://doaj.org/article/213fd6732c8449c1ad2c4e065abc47e6
Autor:
Masatoshi Yuhi, Shinya Umeda, Mamoru Arita, Junichi Ninomiya, Hideomi Gokon, Taro Arikawa, Toshitaka Baba, Fumihiko Imamura, Kenzou Kumagai, Shuichi Kure, Takuya Miyashita, Anawat Suppasri, Akio Kawai, Hisamichi Nobuoka, Tomoya Shibayama, Shunichi Koshimura, Nobuhito Mori
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-8 (2024)
Abstract An earthquake with a moment magnitude of 7.5 (Mw) struck the northern Noto Peninsula, Ishikawa Prefecture, Japan, at 16:10 local time on January 1, 2024. This earthquake triggered a tsunami that propagated along the coastline of Ishikawa, To
Externí odkaz:
https://doaj.org/article/38d97bf9c3d7433abaf060f7c722095e
Autor:
Mas, Erick ⁎, Koshimura, Shunichi
Publikováno v:
In International Journal of Disaster Risk Reduction August 2024 110
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 1046-1059 (2024)
Synthetic aperture radar (SAR) imagery is indispensable for acquiring a comprehensive, large-scale topographical perspective of the Earth's surface, facilitating the evaluation of diverse scenarios spanning various events. However, integrating SAR im
Externí odkaz:
https://doaj.org/article/2586ed6f2abd4a18b4365c0f6563f412
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5651-5667 (2024)
Integrated with remote sensing technology, deep learning has been increasingly used for rapid damage assessment. Despite reportedly having high accuracy, the approach requires numerous samples to maintain its performance. However, in the emergency re
Externí odkaz:
https://doaj.org/article/ba41ad06aa6d461680b7bf582672bfe3
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract Tsunami fragility functions (TFF) are statistical models that relate a tsunami intensity measure to a given building damage state, expressed as cumulative probability. Advances in computational and data retrieval speeds, coupled with novel d
Externí odkaz:
https://doaj.org/article/0ca4c88c40df44d79a0b9be8b2ec1c51