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Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts.
Multi-temporal remote sensing image registration using deep convolutional features
Zhuoqian Yang, Tingting Dan, and Yang Yang · 2004
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The use of remote sensing for post-earthquake damage assessment: lessons from recent events, and future prospects
R Foulser-Piggott, R Spence, K Saito, DM Brown, and R Eguchi · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Detection of urban damage using remote sensing and machine learning algorithms: Revisiting the 2010 haiti earthquake
Austin J Cooner, Yang Shao, and James B Campbell · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Damage detection from aerial images via convolutional neural networks
Aito Fujita, Ken Sakurada, Tomoyuki Imaizumi, Riho Ito, Shuhei Hikosaka, and Ryosuke Nakamura · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Cited alongside, same era.
Building damage assessment using deep learning and ground-level image data
Karoon Rashedi Nia and Greg Mori · 2017
Cited alongside, same era.
Identifying collapsed buildings using post-earthquake satellite imagery and convolutional neural networks: A case study of the 2010 haiti earthquake
Min Ji, Lanfa Liu, and Manfred Buchroithner · 2018
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xview: Objects in context in overhead imagery
Darius Lam, Richard Kuzma, Kevin McGee, Samuel Dooley, Michael Laielli, Matthew Klaric, Yaroslav Bulatov, and Brendan McCord · 2018
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xbd: A dataset for assessing building damage from satellite imagery
Ritwik Gupta, Richard Hosfelt, Sandra Sajeev, Nirav Patel, Bryce Goodman, Jigar Doshi, Eric Heim, Howie Choset, and Matthew Gaston · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Detectron2
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Sean Andrew Chen, Andrew Escay, Christopher Haberland, Tessa Schneider, Valentina Staneva, and Youngjun Choe · 2018
Cited alongside, same era.
Satellite image classification of building damages using airborne and satellite image samples in a deep learning approach
D Duarte, Francesco Nex, N Kerle, and George Vosselman · 2018
Cited alongside, same era.
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
Later among the works it cites.
Building damage detection in satellite imagery using convolutional neural networks
Joseph Z Xu, Wenhan Lu, Zebo Li, Pranav Khaitan, and Valeriya Zaytseva · 2019
Later among the works it cites.