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Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years.
“Unsupervised flood extent detection from sar imagery applying shadow filtering from sar simulated image,”
M. Vassileva, A. Nascetti, F. GiulioTonolo, and P. Boccardo, · 2015
Earlier work this paper cites.
“Google earth engine: Planetary-scale geospatial analysis for everyone,”
Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore, · 2017
Earlier work this paper cites.
“Unsupervised rapid flood mapping using sentinel-1 grd sar images,”
Donato Amitrano, Gerardo Di Martino, Antonio Iodice, Daniele Riccio, and Giuseppe Ruello, · 2018
Earlier work this paper cites.
“Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks,”
Abhijit Guha Roy, Nassir Navab, and Christian Wachinger, · 2018
Earlier work this paper cites.
“Inundation extent mapping by synthetic aperture radar: A review,”
Xinyi Shen, Dacheng Wang, Kebiao Mao, Emmanouil Anagnostou, and Yang Hong, · 2019
Cited alongside, same era.
“Sen1floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1,”
Derrick Bonafilia, Beth Tellman, Tyler Anderson, and Erica Issenberg, · 2020
Cited alongside, same era.
“Learning deep models from weak labels for water surface segmentation in sar images,”
Francesco Asaro, Gianluca Murdaca, and Claudio Maria Prati, · 2021
Cited alongside, same era.
“Water body detection using deep learning with sentinel-1 sar satellite data and land cover maps,”
Hyungyun Jeon, Duk-jin Kim, and Junwoo Kim, · 2021
Cited alongside, same era.
“H2o-net: Self-supervised flood segmentation via adversarial domain adaptation and label refinement,”
Peri Akiva, Matthew Purri, Kristin Dana, Beth Tellman, and Tyler Anderson, · 2021
Later among the works it cites.
“Exploring sentinel-1 and sentinel-2 diversity for flood inundation mapping using deep learning,”
Goutam Konapala, Sujay V Kumar, and Shahryar Khalique Ahmad, · 2021
Later among the works it cites.
“Enhancement of detecting permanent water and temporary water in flood disasters by fusing sentinel-1 and sentinel-2 imagery using deep learning algorithms: Demonstration of sen1floods11 benchmark datasets,”
Yanbing Bai, Wenqi Wu, Zhengxin Yang, Jinze Yu, Bo Zhao, Xing Liu, Hanfang Yang, Erick Mas, and Shunichi Koshimura, · 2021
Later among the works it cites.
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