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To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected.
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Norman Kerle · 2010
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Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2011
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F. Dell’Acqua and P. Gamba · 2012
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Alex Krizhevsky · 2012
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
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Austin Cooner, Yang Shao, and James Campbell · 2016
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore · 2017
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xbd: A dataset for assessing building damage from satellite imagery, 2019
Ritwik Gupta, Richard Hosfelt, Sandra Sajeev, Nirav Patel, Bryce Goodman, Jigar Doshi, Eric Heim, Howie Choset, and Matthew Gaston · 2019
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https://data.humdata.org
Humanitarian data exchange · 2019
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Structural building damage detection with deep learning: Assessment of a state-of-the-art cnn in operational conditions
Francesco Nex, Diogo Duarte, Fabio Giulio Tonolo, and Norman Kerle · 2019
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Building damage detection in satellite imagery using convolutional neural networks
Joseph Z. Xu, Wenhan Lu, Zebo Li, Pranav Khaitan, and Valeriya Zaytseva · 2019
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Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2020
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Satellite Image Classification Of Building Damages Using Airborne And Satellite Image Samples In A Deep Learning Approach
D. Duarte, F.C. Nex, N. Kerle, and G. Vosselman · 2018
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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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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2019
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Creating xbd: A dataset for assessing building damage from satellite imagery
Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston · 2019
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xview2 dataset, 2019
Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston · 2019
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xview2 challenge 1st place, 2020
Victor Durnov · 2020
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Rescuenet: Joint building segmentation and damage assessment from satellite imagery, 2020
Rohit Gupta and Mubarak Shah · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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Multi-hazard and spatial transferability of a cnn for automated building damage assessment
Tinka Valentijn, Jacopo Margutti, Marc van den Homberg, and Jorma Laaksonen · 2020
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Building disaster damage assessment in satellite imagery with multi-temporal fusion, 2020
Ethan Weber and Hassan Kané · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le · 2020
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