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Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use monitoring, or tackling climate change.
Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2003
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Remote sensing of tropical forest environments: towards the monitoring of environmental resources for sustainable development
G. M. Foody · 2003
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Sentinel-2: Esa’s optical high-resolution mission for gmes operational services
M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort, et al · 2012
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Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps
D. J. Mulla · 2013
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Landsat-8: Science and product vision for terrestrial global change research
D. P. Roy, M. A. Wulder, T. R. Loveland, C. E. Woodcock, R. G. Allen, M. C. Anderson, D. Helder, J. R. Irons, D. M. Johnson, R. Kennedy, et al · 2014
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Imagenet large scale visual recognition challenge (2014)
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2014
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Combining satellite imagery and machine learning to predict poverty
N. Jean, M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon · 2016
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Learning visual features from large weakly supervised data
A. Joulin, L. Van Der Maaten, A. Jabri, and N. Vasilache · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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Planet-photo geolocation with convolutional neural networks
T. Weyand, I. Kostrikov, and J. Philbin · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Google earth engine: Planetary-scale geospatial analysis for everyone
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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Deep clustering for unsupervised learning of visual features
M. Caron, P. Bojanowski, A. Joulin, and M. Douze · 2018
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Fully convolutional siamese networks for change detection
R. C. Daudt, B. Le Saux, and A. Boulch · 2018
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Urban change detection for multispectral earth observation using convolutional neural networks
R. C. Daudt, B. Le Saux, A. Boulch, and Y. Gousseau · 2018
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Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. Van Der Maaten · 2018
Cited alongside, same era.
Bigearthnet: A large-scale benchmark archive for remote sensing image understanding
G. Sumbul, M. Charfuelan, B. Demir, and V. Markl · 2019
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Y. Tian, D. Krishnan, and P. Isola · 2019
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Learning to interpret satellite images in global scale using wikipedia
B. Uzkent, E. Sheehan, C. Meng, Z. Tang, M. Burke, D. Lobell, and S. Ermon · 2019
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Geography-aware self-supervised learning
K. Ayush, B. Uzkent, C. Meng, M. Burke, D. Lobell, and S. Ermon · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
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A. v. d. Oord, Y. Li, and O. Vinyals · 2018
Cited alongside, same era.
Assisting flood disaster response with earth observation data and products: A critical assessment
G. J. Schumann, G. R. Brakenridge, A. J. Kettner, R. Kashif, and E. Niebuhr · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin · 2018
Cited alongside, same era.
Unsupervised pre-training of image features on non-curated data
M. Caron, P. Bojanowski, J. Mairal, and A. Joulin · 2019
Cited alongside, same era.
Exploitation of sentinel-2 time series to map burned areas at the national level: A case study on the 2017 italy wildfires
F. Filipponi · 2019
Cited alongside, same era.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
P. Helber, B. Bischke, A. Dengel, and D. Borth · 2019
Cited alongside, same era.
Defining ecological regions in italy based on a multivariate clustering approach: A first step towards a targeted vector borne disease surveillance
C. Ippoliti, L. Candeloro, M. Gilbert, M. Goffredo, G. Mancini, G. Curci, S. Falasca, S. Tora, A. Di Lorenzo, M. Quaglia, et al · 2019
Cited alongside, same era.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
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Watching the world go by: Representation learning from unlabeled videos
D. Gordon, K. Ehsani, D. Fox, and A. Farhadi · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Looc: Localize overlapping objects with count supervision
I. H. Laradji, R. Pardinas, P. Rodriguez, and D. Vazquez · 2020
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Self-supervised learning of pretext-invariant representations
I. Misra and L. v. d. Maaten · 2020
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Bigearthnet dataset with a new class-nomenclature for remote sensing image understanding
G. Sumbul, J. Kang, T. Kreuziger, F. Marcelino, H. Costa, P. Benevides, M. Caetano, and B. Demir · 2020
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The color out of space: learning self-supervised representations for earth observation imagery
S. Vincenzi, A. Porrello, P. Buzzega, M. Cipriano, P. Fronte, R. Cuccu, C. Ippoliti, A. Conte, and S. Calderara · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
T. Wang and P. Isola · 2020
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What should not be contrastive in contrastive learning
T. Xiao, X. Wang, A. A. Efros, and T. Darrell · 2020
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A weakly supervised consistency-based learning method for covid-19 segmentation in ct images
I. Laradji, P. Rodriguez, O. Manas, K. Lensink, M. Law, L. Kurzman, W. Parker, D. Vazquez, and D. Nowrouzezahrai · 2021
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