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Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks.
Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development
G. M. Foody · 2003
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Visualizing high-dimensional data using t-SNE
L.J.P. van der Maaten and Geoffrey Hinton · 2008
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Manual of Geographic Information Systems
M. Madden, American Society for Photogrammetry, and Remote Sensing · 2009
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Terrapattern: open-ended, visual query-by-example for satellite imagery using deep learning, 2010
G Levin, D Newbury, K McDonald, I Alvarado, A Tiwari, and M Zaheer · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps
David J. Mulla · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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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, T A Scambos, C B Schaaf, J R Schott, Y Sheng, E F Vermote, A S Belward, R Bindschadler, W B Cohen, F Gao, J D Hipple, P Hostert, J Huntington, C O Justice, A Kilic, V Kovalskyy, Z P Lee, L Lymburner, J G Masek, J McCorkel, Y Shuai, R Trezza, J Vogelmann, R H Wynne, and Z Zhu · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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The world factbook; 2010
CIA Factbook · 2015
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Deep metric learning using triplet network
Elad Hoffer and Nir Ailon · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon · 2016
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Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images
W Lück and A van Niekerk · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
Ishan Misra, C Lawrence Zitnick, and Martial Hebert · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Learning temporal embeddings for complex video analysis
Vignesh Ramanathan, Kevin Tang, Greg Mori, and Li Fei-Fei · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, et al · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations
Alyssa K Whitcraft, Eric F Vermote, Inbal Becker-Reshef, and Christopher O Justice · 2015
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USDA National Agricultural Statistics Service Cropland Data Layer. published crop-specific data layer [online]., 2016
2016
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Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Patch2vec: Globally consistent image patch representation
O Fried, S Avidan, and D Cohen-Or · 2017
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Google Earth Engine: Planetary-scale geospatial analysis for everyone
Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2017
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