gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances
in Neural Information Processing Systems , 33:9912–9924, 2020.
[19] Xinlei Chen and Kaiming He. Exploring simple Siamese representation learning. In Pro-
ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages
15750–15758, 2021.
[20] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow Twins: Self-
supervised learning via redundancy reduction. In International Conference on Machine Learn-
ing, pages 12310–12320. PMLR, 2021.
[21] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework
for contrastive learning of visual representations. In International Conference on Machine
Learning , pages 1597–1607. PMLR, 2020.
[22] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena
Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Ghesh-
laghi Azar, et al. Bootstrap Your Own Latent-a new approach to self-supervised learning.
Advances in Neural Information Processing Systems , 33:21271–21284, 2020.
[23] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-
scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern
Recognition , pages 248–255. Ieee, 2009.
[24] Yuxing Chen and Lorenzo Bruzzone. Self-supervised SAR-optical data fusion of Sentinel-1/-2
images. IEEE Transactions on Geoscience and Remote Sensing , 60:1–11, 2021.
[25] Marrit Leenstra, Diego Marcos, Francesca Bovolo, and Devis Tuia. Self-supervised pre-
training enhances change detection in Sentinel-2 imagery. In Pattern Recognition. ICPR Inter-
national Workshops and Challenges: Virtual Event, January 10-15, 2021, Proceedings, Part
VII, pages 578–590. Springer, 2021.
[26] Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon, Huy V o, John Brandt, Jus-
tine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, et al. Sub-meter resolution
canopy height maps using self-supervised learning and a vision transformer trained on Aerial
and GEDI Lidar. arXiv preprint arXiv:2304.07213 , 2023.
[27] Jules Bourcier, Thomas Floquet, Gohar Dashyan, Tugdual Ceillier, Karteek Alahari, and Joce-
lyn Chanussot. Self-supervised pretraining on satellite imagery: A case study on label-efficient
vehicle detection. arXiv preprint arXiv:2210.11815 , 2022.
[28] Bo Peng, Qunying Huang, Jamp V ongkusolkit, Song Gao, Daniel B. Wright, Zheng N.
Fang, and Yi Qiang. Urban flood mapping with bitemporal multispectral imagery via a self-
supervised learning framework. IEEE Journal of Selected Topics in Applied Earth Observa-
tions and Remote Sensing , 14:2001–2016, 2020.
12
[29] Fabien H. Wagner, Ricardo Dalagnol, Alber H. Sánchez, Mayumi Hirye, Samuel Favrichon,
Jake H. Lee, Steffen Mauceri, Yan Yang, and Sassan Saatchi. K-textures, a self supervised
hard clustering deep learning algorithm for satellite images segmentation. arXiv preprint
arXiv:2205.08671 , 2022.
[30] Richard J. Kauth and G. S. Thomas. The tasselled cap–a graphic description of the spectral-
temporal development of agricultural crops as seen by Landsat. In LARS Symposia , page 159,
1976.
[31] James E. V ogelmann, Stephen M. Howard, Limin Yang, Charles R. Larson, Bruce K. Wylie,
and Nick Van Driel. Completion of the 1990s National Land Cover Data Set for the conter-
minous United States from Landsat Thematic Mapper data and ancillary data sources. Pho-
togrammetric Engineering and Remote Sensing , 67(6), 2001.
[32] Claire Boryan, Zhengwei Yang, Rick Mueller, and Mike Craig. Monitoring US agriculture: the
US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer
program. Geocarto International , 26(5):341–358, 2011.
[33] David M. Johnson, Richard Mueller, et al. The 2009 Cropland Data Layer. Photogrammetric
Engineering and Remote Sensing , 76(11):1201–1205, 2010.
[34] David M. Johnson. Using the Landsat archive to map crop cover history across the United
States. Remote Sensing of Environment , 232:111286, 2019.
[35] Josefino C. Comiso and Konrad Steffen. Studies of Antarctic sea ice concentrations from
satellite data and their applications. Journal of Geophysical Research: Oceans , 106(C12):
31361–31385, 2001.
[36] Jeff Dozier and Danny Marks. Snow mapping and classification from Landsat Thematic Map-
per data. Annals of Glaciology , 9:97–103, 1987.
[37] Alex S. Gardner, Geir Moholdt, Ted Scambos, Mark Fahnstock, Stefan Ligtenberg, Michiel
Van Den Broeke, and Johan Nilsson. Increased West Antarctic and unchanged East Antarctic
ice discharge over the last 7 years. The Cryosphere , 12(2):521–547, 2018.
[38] Andrew K. Melkonian, Michael J. Willis, Matthew E. Pritchard, and Adam J. Stewart. Recent
changes in glacier velocities and thinning at Novaya Zemlya. Remote Sensing of Environment ,
174:244–257, 2016.
[39] Thomas J. Ballinger, Robert V . Rohli, Michael J. Allen, David A. Robinson, and Thomas W.
Estilow. Half-century perspectives on North American spring snowline and snow cover as-
sociations with the Pacific-North American teleconnection pattern. Climate Research , 74(3):
201–216, 2018.
[40] 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. High-resolution global maps of 21st-century forest cover change. Science , 342(6160):
850–853, 2013.
[41] Holly K. Gibbs, Sandra Brown, John O. Niles, and Jonathan A. Foley. Monitoring and estimat-
ing tropical forest carbon stocks: Making REDD a reality. Environmental Research Letters , 2
(4):045023, 2007.
[42] Holly K. Gibbs, Aaron S. Ruesch, Frédéric Achard, Murray K. Clayton, Peter Holmgren,
Navin Ramankutty, and Jonathan A. Foley. Tropical forests were the primary sources of new
agricultural land in the 1980s and 1990s. Proceedings of the National Academy of Sciences ,
107(38):16732–16737, 2010.
[43] Robert E. Kennedy, Zhiqiang Yang, and Warren B. Cohen. Detecting trends in forest distur-
bance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation
algorithms. Remote Sensing of Environment , 114(12):2897–2910, 2010.
[44] David Skole and Compton Tucker. Tropical deforestation and habitat fragmentation in the
Amazon: Satellite data from 1978 to 1988. Science , 260(5116):1905–1910, 1993.
13
[45] 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.
Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology
and the Environment , 12(6):339–346, 2014.
[46] Pol Coppin, Inge Jonckheere, Kristiaan Nackaerts, Bart Muys, and Eric Lambin. Review
ArticleDigital change detection methods in ecosystem monitoring: A review. International
Journal of Remote Sensing , 25(9):1565–1596, 2004.
[47] Zhe Zhu. Change detection using Landsat time series: A review of frequencies, preprocessing,
algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing , 130:
370–384, 2017.
[48] Zhe Zhu and Curtis E. Woodcock. Continuous change detection and classification of land
cover using all available Landsat data. Remote Sensing of Environment , 144:152–171, 2014.
[49] Curtis E. Woodcock, Thomas R. Loveland, Martin Herold, and Marvin E. Bauer. Transitioning
from change detection to monitoring with remote sensing: A paradigm shift. Remote Sensing

Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances

in Neural Information Processing Systems , 33:9912–9924, 2020.

[19] Xinlei Chen and Kaiming He. Exploring simple Siamese representation learning. In Pro-

ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages

15750–15758, 2021.

[20] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow Twins: Self-

supervised learning via redundancy reduction. In International Conference on Machine Learn-

ing, pages 12310–12320. PMLR, 2021.

[21] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework

for contrastive learning of visual representations. In International Conference on Machine

Learning , pages 1597–1607. PMLR, 2020.

[22] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena

Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Ghesh-

laghi Azar, et al. Bootstrap Your Own Latent-a new approach to self-supervised learning.

Advances in Neural Information Processing Systems , 33:21271–21284, 2020.

[23] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-

scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern

Recognition , pages 248–255. Ieee, 2009.

[24] Yuxing Chen and Lorenzo Bruzzone. Self-supervised SAR-optical data fusion of Sentinel-1/-2

images. IEEE Transactions on Geoscience and Remote Sensing , 60:1–11, 2021.

[25] Marrit Leenstra, Diego Marcos, Francesca Bovolo, and Devis Tuia. Self-supervised pre-

training enhances change detection in Sentinel-2 imagery. In Pattern Recognition. ICPR Inter-

national Workshops and Challenges: Virtual Event, January 10-15, 2021, Proceedings, Part

VII, pages 578–590. Springer, 2021.

[26] Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon, Huy V o, John Brandt, Jus-

tine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, et al. Sub-meter resolution

canopy height maps using self-supervised learning and a vision transformer trained on Aerial

and GEDI Lidar. arXiv preprint arXiv:2304.07213 , 2023.

[27] Jules Bourcier, Thomas Floquet, Gohar Dashyan, Tugdual Ceillier, Karteek Alahari, and Joce-

lyn Chanussot. Self-supervised pretraining on satellite imagery: A case study on label-efficient

vehicle detection. arXiv preprint arXiv:2210.11815 , 2022.

[28] Bo Peng, Qunying Huang, Jamp V ongkusolkit, Song Gao, Daniel B. Wright, Zheng N.

Fang, and Yi Qiang. Urban flood mapping with bitemporal multispectral imagery via a self-

supervised learning framework. IEEE Journal of Selected Topics in Applied Earth Observa-

tions and Remote Sensing , 14:2001–2016, 2020.

12

[29] Fabien H. Wagner, Ricardo Dalagnol, Alber H. Sánchez, Mayumi Hirye, Samuel Favrichon,

Jake H. Lee, Steffen Mauceri, Yan Yang, and Sassan Saatchi. K-textures, a self supervised

hard clustering deep learning algorithm for satellite images segmentation. arXiv preprint

arXiv:2205.08671 , 2022.

[30] Richard J. Kauth and G. S. Thomas. The tasselled cap–a graphic description of the spectral-

temporal development of agricultural crops as seen by Landsat. In LARS Symposia , page 159,

1976.

[31] James E. V ogelmann, Stephen M. Howard, Limin Yang, Charles R. Larson, Bruce K. Wylie,

and Nick Van Driel. Completion of the 1990s National Land Cover Data Set for the conter-

minous United States from Landsat Thematic Mapper data and ancillary data sources. Pho-

togrammetric Engineering and Remote Sensing , 67(6), 2001.

[32] Claire Boryan, Zhengwei Yang, Rick Mueller, and Mike Craig. Monitoring US agriculture: the

US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer

program. Geocarto International , 26(5):341–358, 2011.

[33] David M. Johnson, Richard Mueller, et al. The 2009 Cropland Data Layer. Photogrammetric

Engineering and Remote Sensing , 76(11):1201–1205, 2010.

[34] David M. Johnson. Using the Landsat archive to map crop cover history across the United

States. Remote Sensing of Environment , 232:111286, 2019.

[35] Josefino C. Comiso and Konrad Steffen. Studies of Antarctic sea ice concentrations from

satellite data and their applications. Journal of Geophysical Research: Oceans , 106(C12):

31361–31385, 2001.

[36] Jeff Dozier and Danny Marks. Snow mapping and classification from Landsat Thematic Map-

per data. Annals of Glaciology , 9:97–103, 1987.

[37] Alex S. Gardner, Geir Moholdt, Ted Scambos, Mark Fahnstock, Stefan Ligtenberg, Michiel

Van Den Broeke, and Johan Nilsson. Increased West Antarctic and unchanged East Antarctic

ice discharge over the last 7 years. The Cryosphere , 12(2):521–547, 2018.

[38] Andrew K. Melkonian, Michael J. Willis, Matthew E. Pritchard, and Adam J. Stewart. Recent

changes in glacier velocities and thinning at Novaya Zemlya. Remote Sensing of Environment ,

174:244–257, 2016.

[39] Thomas J. Ballinger, Robert V . Rohli, Michael J. Allen, David A. Robinson, and Thomas W.

Estilow. Half-century perspectives on North American spring snowline and snow cover as-

sociations with the Pacific-North American teleconnection pattern. Climate Research , 74(3):

201–216, 2018.

[40] 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. High-resolution global maps of 21st-century forest cover change. Science , 342(6160):

850–853, 2013.

[41] Holly K. Gibbs, Sandra Brown, John O. Niles, and Jonathan A. Foley. Monitoring and estimat-

ing tropical forest carbon stocks: Making REDD a reality. Environmental Research Letters , 2

(4):045023, 2007.

[42] Holly K. Gibbs, Aaron S. Ruesch, Frédéric Achard, Murray K. Clayton, Peter Holmgren,

Navin Ramankutty, and Jonathan A. Foley. Tropical forests were the primary sources of new

agricultural land in the 1980s and 1990s. Proceedings of the National Academy of Sciences ,

107(38):16732–16737, 2010.

[43] Robert E. Kennedy, Zhiqiang Yang, and Warren B. Cohen. Detecting trends in forest distur-

bance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation

algorithms. Remote Sensing of Environment , 114(12):2897–2910, 2010.

[44] David Skole and Compton Tucker. Tropical deforestation and habitat fragmentation in the

Amazon: Satellite data from 1978 to 1988. Science , 260(5116):1905–1910, 1993.

13

[45] 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.

Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology

and the Environment , 12(6):339–346, 2014.

[46] Pol Coppin, Inge Jonckheere, Kristiaan Nackaerts, Bart Muys, and Eric Lambin. Review

ArticleDigital change detection methods in ecosystem monitoring: A review. International

Journal of Remote Sensing , 25(9):1565–1596, 2004.

[47] Zhe Zhu. Change detection using Landsat time series: A review of frequencies, preprocessing,

algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing , 130:

370–384, 2017.

[48] Zhe Zhu and Curtis E. Woodcock. Continuous change detection and classification of land

cover using all available Landsat data. Remote Sensing of Environment , 144:152–171, 2014.

[49] Curtis E. Woodcock, Thomas R. Loveland, Martin Herold, and Marvin E. Bauer. Transitioning

from change detection to monitoring with remote sensing: A paradigm shift. Remote Sensing