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The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites.
The tilt of the earth’s axis and the consequences thereof
John D. Boon · 1945
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The tasselled cap–a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat
Richard J. Kauth and G. S. Thomas · 1976
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Landsat 3 Return Beam Vidicon response artifacts: A report on RBV photographic product characteristics and quality coding system
Bill P. Clark · 1981
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R-trees: A dynamic index structure for spatial searching
Antonin Guttman · 1984
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Snow mapping and classification from Landsat Thematic Mapper data
Jeff Dozier and Danny Marks · 1987
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Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988
David Skole and Compton Tucker · 1993
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Completion of the 1990s National Land Cover Data Set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources
James E. Vogelmann, Stephen M. Howard, Limin Yang, Charles R. Larson, Bruce K. Wylie, and Nick Van Driel · 2001
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Studies of Antarctic sea ice concentrations from satellite data and their applications
Josefino C. Comiso and Konrad Steffen · 2001
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Improved baselines with Momentum Contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Landsat coverage of the earth at high latitudes
Robert Bindschadler · 2003
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Review ArticleDigital change detection methods in ecosystem monitoring: A review
Pol Coppin, Inge Jonckheere, Kristiaan Nackaerts, Bart Muys, and Eric Lambin · 2004
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Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm
Richard R. Irish, John L. Barker, Samuel N. Goward, and Terry Arvidson · 2006
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Monitoring and estimating tropical forest carbon stocks: Making REDD a reality
Holly K. Gibbs, Sandra Brown, John O. Niles, and Jonathan A. Foley · 2007
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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 2009 Cropland Data Layer
David M. Johnson, Richard Mueller, et al · 2010
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Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s
Holly K. Gibbs, Aaron S. Ruesch, Frédéric Achard, Murray K. Clayton, Peter Holmgren, Navin Ramankutty, and Jonathan A. Foley · 2010
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Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms
Robert E. Kennedy, Zhiqiang Yang, and Warren B. Cohen · 2010
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Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer program
Claire Boryan, Zhengwei Yang, Rick Mueller, and Mike Craig · 2011
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Development of the Landsat data continuity mission cloud-cover assessment algorithms
Pasquale L. Scaramuzza, Michelle A. Bouchard, and John L. Dwyer · 2011
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High-resolution global maps of 21st-century forest cover change
Matthew C. Hansen, Peter V. Potapov, Rebecca Moore, Matt Hancher, Svetlana A. Turubanova, Alexandra Tyukavina, David Thau, Stephen V. Stehman, Scott J. Goetz, Thomas R. Loveland, et al · 2013
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Bringing an ecological view of change to Landsat-based remote sensing
Robert E. Kennedy, Serge Andréfouët, Warren B. Cohen, Cristina Gómez, Patrick Griffiths, Martin Hais, Sean P. Healey, Eileen H. Helmer, Patrick Hostert, Mitchell B. Lyons, et al · 2014
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Continuous change detection and classification of land cover using all available Landsat data
Zhe Zhu and Curtis E. Woodcock · 2014
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Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing
M. Joseph Hughes and Daniel J. Hayes · 2014
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L7 Irish cloud validation masks
U.S. Geological Survey · 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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Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) Level 1 (L1) Data Format Control Book (DFCB)
Jim Lacasse · 2016
Cited alongside, same era.
Recent changes in glacier velocities and thinning at Novaya Zemlya
Andrew K. Melkonian, Michael J. Willis, Matthew E. Pritchard, and Adam J. Stewart · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
A survival guide to Landsat preprocessing
Nicholas E. Young, Ryan S. Anderson, Stephen M. Chignell, Anthony G. Vorster, Rick Lawrence, and Paul H. Evangelista · 2017
Cited alongside, same era.
Transitioning from change detection to monitoring with remote sensing: A paradigm shift
Curtis E. Woodcock, Thomas R. Loveland, Martin Herold, and Marvin E. Bauer · 2020
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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
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Conterminous United States land cover change patterns 2001–2016 from the 2016 national land cover database
Collin Homer, Jon Dewitz, Suming Jin, George Xian, Catherine Costello, Patrick Danielson, Leila Gass, Michelle Funk, James Wickham, Stephen Stehman, et al · 2020
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Geography-aware self-supervised learning
Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, and Stefano Ermon · 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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Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications
Zhe Zhu · 2017
Cited alongside, same era.
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Cloud detection algorithm comparison and validation for operational Landsat data products
Steve Foga, Pat L. Scaramuzza, Song Guo, Zhe Zhu, Ronald D. Dilley Jr., Tim Beckmann, Gail L. Schmidt, John L. Dwyer, M. Joseph Hughes, and Brady Laue · 2017
Cited alongside, same era.
Landsat Thematic Mapper (TM) Level 1 (L1) Data Format Control Book (DFCB)
Christopher Engebretson · 2018
Cited alongside, same era.
Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years
Alex S. Gardner, Geir Moholdt, Ted Scambos, Mark Fahnstock, Stefan Ligtenberg, Michiel Van Den Broeke, and Johan Nilsson · 2018
Cited alongside, same era.
Later among the works it cites.
Exploring simple Siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Barlow Twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Self-supervised SAR-optical data fusion of Sentinel-1/-2 images
Yuxing Chen and Lorenzo Bruzzone · 2021
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Self-supervised pre-training enhances change detection in Sentinel-2 imagery
Marrit Leenstra, Diego Marcos, Francesca Bovolo, and Devis Tuia · 2021
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Landsat 8 Collection 2 cloud truth mask validation set
Pasquale L. Scaramuzza · 2021
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National Land Cover Database (NLCD) 2019 products (ver. 2.0)
Jon Dewitz and U.S. Geological Survey · 2021
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Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States
James Wickham, Stephen V. Stehman, Daniel G. Sorenson, Leila Gass, and Jon A. Dewitz · 2021
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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 Lobell, and Stefano Ermon · 2022
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Scale-MAE: A scale-aware masked autoencoder for multiscale geospatial representation learning
Colorado J. Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell · 2022
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Self-supervised learning for scene classification in remote sensing: Current state of the art and perspectives
Paul Berg, Minh-Tan Pham, and Nicolas Courty · 2022
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Self-supervised pretraining on satellite imagery: A case study on label-efficient vehicle detection
Jules Bourcier, Thomas Floquet, Gohar Dashyan, Tugdual Ceillier, Karteek Alahari, and Jocelyn Chanussot · 2022
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Fabien H. Wagner, Ricardo Dalagnol, Alber H. Sánchez, Mayumi Hirye, Samuel Favrichon, Jake H. Lee, Steffen Mauceri, Yan Yang, and Sassan Saatchi · 2022
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Fifty years of Landsat science and impacts
Michael A. Wulder, David P. Roy, Volker C. Radeloff, Thomas R. Loveland, Martha C. Anderson, David M. Johnson, Sean Healey, Zhe Zhu, Theodore A. Scambos, Nima Pahlevan, et al · 2022
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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 · 2022
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Landsat 7 Collection 2 cloud truth mask validation set
Pasquale L. Scaramuzza · 2022
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Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon, Huy Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, et al · 2023
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World cities database, March 2023
Pareto Software, LLC · 2023
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Cropland Data Layer (CDL)
USDA National Agricultural Statistics Service (USDA-NASS) · 2023
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