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Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
Bootstrap methods: another look at the jackknife
B Eforn · 1979
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An image is worth 16x16 words: Transformers for image recognition at scale. arxiv 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2010
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Object detection with discriminatively trained part-based models
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan · 2010
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Learning to count objects in images
Victor Lempitsky and Andrew Zisserman · 2010
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Sentinel-2: Esa’s optical high-resolution mission for gmes operational services
Matthias Drusch, Umberto Del Bello, Sébastien Carlier, Olivier Colin, Veronica Fernandez, Ferran Gascon, Bianca Hoersch, Claudia Isola, Paolo Laberinti, Philippe Martimort, et al · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?
Otávio AB Penatti, Keiller Nogueira, and Jefersson A Dos Santos · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Greenhouse Gas Emissions: Understanding Global Warming Potentials
EPA · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Using artificial intelligence to improve real-time decision-making for high-impact weather
Amy McGovern, Kimberly L. Elmore, David John Gagne, Sue Ellen Haupt, Christopher D. Karstens, Ryan Lagerquist, Travis Smith, and John K. Williams · 2017
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Chesapeake bay program partnership high-resolution land cover classification accuracy assessment methodology, 2017
Cassandra Pallai and Kathryn Wesson · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep learning in remote sensing: A comprehensive review and list of resources
Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, and Friedrich Fraundorfer · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Never-ending learning
T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, B. Yang, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, and J. Welling · 2018
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PyTorch Lightning, 3 2019
William Falcon and The PyTorch Lightning team · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Tile2vec: Unsupervised representation learning for spatially distributed data
Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, and Stefano Ermon · 2019
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Quantifying the carbon emissions of machine learning
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres · 2019
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Machine learning-based estimation of forest carbon stocks to increase transparency of forest preservation efforts
Björn Lütjens, Lucas Liebenwein, and Katharina Kramer · 2019
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Deep learning in remote sensing applications: A meta-analysis and review
Lei Ma, Yu Liu, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, and Brian Alan Johnson · 2019
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Large scale high-resolution land cover mapping with multi-resolution data
Caleb Robinson, Le Hou, Kolya Malkin, Rachel Soobitsky, Jacob Czawlytko, Bistra Dilkina, and Nebojsa Jojic · 2019
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Tackling climate change with machine learning
David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, et al · 2019
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Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
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Learning to interpret satellite images using wikipedia
Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, and Stefano Ermon · 2019
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Cumulo: A dataset for learning cloud classes
Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J Kusner, and Duncan Watson-Parris · 2019
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Using satellite imagery to understand and promote sustainable development
Marshall Burke, Anne Driscoll, David B Lobell, and Stefano Ermon · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Sentinel-2
ESA · 2021
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Airborne methane surveys pay for themselves: An economic case study of increased revenue from emissions control
Forrest Johnson, Andrew Wlazlo, Ryan Keys, Viren Desai, Erin Wetherley, Ryan Calvert, and Elena Berman · 2021
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So2sat lcz42: A benchmark dataset for global local climate zones classification
Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu, Yilei Shi, Jian Kang, Lichao Mou, Hossein Bagheri, Matthias Häberle, Yuansheng Hua, Rong Huang, et al · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Elijah Cole, Benjamin Deneu, Titouan Lorieul, Maximilien Servajean, Christophe Botella, Dan Morris, Nebojsa Jojic, Pierre Bonnet, and Alexis Joly · 2020
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High-resolution soybean yield mapping across the us midwest using subfield harvester data
Walter T Dado, Jillian M Deines, Rinkal Patel, Sang-Zi Liang, and David B Lobell · 2020
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Automated identification of oil field features using cnns
Sonu Dileep, Daniel Zimmerle, J Ross Beveridge, and Timothy Vaughn · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
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Methanet - an ai-driven approach to quantifying methane point-source emission from high-resolution 2-d plume imagery
Siraput Jongaramrungruang, Christian Frankenberg, Andrew K. Thorpe, and Georgios Matheou · 2021
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Scalable deep learning to identify brick kilns and aid regulatory capacity
Jihyeon Lee, Nina R. Brooks, Fahim Tajwar, Marshall Burke, Stefano Ermon, David B. Lobell, Debashish Biswas, and Stephen P. Luby · 2021
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Physically-consistent generative adversarial networks for coastal flood visualization
Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodríguez, Océane Boulais, Aruna Sankaranarayanan, Aaron Pina, Yarin Gal, Chedy Raissi, Alexander Lavin, and Dava Newman · 2021
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Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data
Oscar Manas, Alexandre Lacoste, Xavier Giró-i Nieto, David Vazquez, and Pau Rodriguez · 2021
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3d-pv-locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3d
Kevin Mayer, Benjamin Rausch, Marie-Louise Arlt, Gunther Gust, Zhecheng Wang, Dirk Neumann, and Ram Rajagopal · 2021
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Carbon emissions and large neural network training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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CodeCarbon: Estimate and Track Carbon Emissions from Machine Learning Computing
Victor Schmidt, Kamal Goyal, Aditya Joshi, Boris Feld, Liam Conell, Nikolas Laskaris, Doug Blank, Jonathan Wilson, Sorelle Friedler, and Sasha Luccioni · 2021
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Torchgeo: deep learning with geospatial data
Adam J Stewart, Caleb Robinson, Isaac A Corley, Anthony Ortiz, Juan M Lavista Ferres, and Arindam Banerjee · 2021
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Bigearthnet-mm: A large-scale, multimodal, multilabel benchmark archive for remote sensing image classification and retrieval [software and data sets]
Gencer Sumbul, Arne De Wall, Tristan Kreuziger, Filipe Marcelino, Hugo Costa, Pedro Benevides, Mario Caetano, Begüm Demir, and Volker Markl · 2021
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Landsat 8
USGS · 2021
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A benchmark dataset for canopy crown detection and delineation in co-registered airborne rgb, lidar and hyperspectral imagery from the national ecological observation network
Ben G Weinstein, Sarah J Graves, Sergio Marconi, Aditya Singh, Alina Zare, Dylan Stewart, Stephanie A Bohlman, and Ethan P White · 2021
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Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning
Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David B Lobell, and Stefano Ermon · 2021
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Smallholder cashew plantations in benin, 2021
Jin Z., Lin C., Weigl C., Obarowski J., and Hale D · 2021
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Remote sensing dataset for detecting cows from high resolution aerial images
Diab Abuaiadah and Alexander Switzer · 2022
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Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery
Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B Lobell, and Stefano Ermon · 2022
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An empirical study of remote sensing pretraining
Di Wang, Jing Zhang, Bo Du, Gui-Song Xia, and Dacheng Tao · 2022
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Earthnets: Empowering ai in earth observation
Zhitong Xiong, Fahong Zhang, Yi Wang, Yilei Shi, and Xiao Xiang Zhu · 2022
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Current trends in deep learning for earth observation: An open-source benchmark arena for image classification
Ivica Dimitrovski, Ivan Kitanovski, Dragi Kocev, and Nikola Simidjievski · 2023
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