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[3] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen
Sun, Mario Lu ˇci´c, and Cordelia Schmid. Vivit: A video vi-
sion transformer. In 2021 IEEE/CVF International Confer-
ence on Computer Vision (ICCV) , pages 6816–6826, 2021.
3
[4] Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer
normalization. ArXiv , abs/1607.06450, 2016. 3
[5] I. Bello, B. Zoph, Q. Le, A. Vaswani, and J. Shlens. Atten-
tion augmented convolutional networks. In 2019 IEEE/CVF
International Conference on Computer Vision (ICCV) , pages
3285–3294, 2019. 3
[6] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Sub-
biah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakan-
tan, Pranav Shyam, Girish Sastry, Amanda Askell, Sand-
hini Agarwal, Ariel Herbert-V oss, Gretchen Krueger, Tom
Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler,
Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric
Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack
Clark, Christopher Berner, Sam McCandlish, Alec Radford,
Ilya Sutskever, and Dario Amodei. Language models are
few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell,
M.F. Balcan, and H. Lin, editors, Advances in Neural Infor-
mation Processing Systems , volume 33, pages 1877–1901.
Curran Associates, Inc., 2020. 2
[7] Jorge Andres Chamorro Martinez, Laura Elena Cu ´e La
Rosa, Raul Queiroz Feitosa, Ieda Del’Arco Sanches, and
Patrick Nigri Happ. Fully convolutional recurrent networks
for multidate crop recognition from multitemporal image se-
quences. ISPRS Journal of Photogrammetry and Remote
Sensing , 171:188–201, 2021. 2, 8
[8] Christopher Conrad, Stefan Dech, Olena Dubovyk, Sebas-
tian Fritsch, Doris Klein, Fabian L ¨ow, Gunther Schorcht, and
Julian Zeidler. Derivation of temporal windows for accurate
crop discrimination in heterogeneous croplands of Uzbek-
istan using multitemporal rapideye images. Computers and
Electronics in Agriculture , 103:63–74, 2014. 2
[9] Christopher Conrad, Sebastian Fritsch, Julian Zeidler, Gerd
R¨ucker, and Stefan Dech. Per-field irrigated crop classifica-
tion in arid central asia using spot and aster data. Remote
Sensing , 2(4):1035–1056, 2010. 2
[10] Gordon Conway. One Billion Hungry: Can we Feed the
World? Cornell University Press, 2012. 1
[11] Xiyang Dai, Yinpeng Chen, Jianwei Yang, Pengchuan
Zhang, Lu Yuan, and Lei Zhang. Dynamic detr: End-to-end
object detection with dynamic attention. In 2021 IEEE/CVF
International Conference on Computer Vision (ICCV) , pages
2968–2977, 2021. 3
[12] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina
Toutanova. BERT: Pre-training of deep bidirectional trans-formers for language understanding. In Proceedings of the
2019 Conference of the North American Chapter of the As-
sociation for Computational Linguistics: Human Language
Technologies, Volume 1 (Long and Short Papers) , pages
4171–4186, Minneapolis, Minnesota, June 2019. Associa-
tion for Computational Linguistics. 2, 3
[13] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov,
Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner,
Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl-
vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is
worth 16x16 words: Transformers for image recognition at
scale. In International Conference on Learning Representa-
tions , 2021. 2, 3, 4
[14] Vivien Sainte Fare Garnot and Loic Landrieu. Panoptic
segmentation of satellite image time series with convolu-
tional temporal attention networks. In Proceedings of the
IEEE/CVF International Conference on Computer Vision
(ICCV) , pages 4872–4881, October 2021. 2, 5, 7, 8
[15] V . S. F. Garnot, L. Landrieu, S. Giordano, and N. Chehata.
Time-space tradeoff in deep learning models for crop clas-
sification on satellite multi-spectral image time series. In
IGARSS 2019 - 2019 IEEE International Geoscience and Re-
mote Sensing Symposium , pages 6247–6250, 2019. 2
[16] Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Gior-
dano, and Nesrine Chehata. Satellite image time series clas-
sification with pixel-set encoders and temporal self-attention.
InProceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) , June 2020. 2
[17] Rohit Girdhar and Deva Ramanan. Attentional pooling for
action recognition. In I. Guyon, U. V on Luxburg, S. Bengio,
H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, ed-
itors, Advances in Neural Information Processing Systems ,
volume 30. Curran Associates, Inc., 2017. 5
[18] Noel Gorelick, Matt Hancher, Mike Dixon, Simon
Ilyushchenko, David Thau, and Rebecca Moore. Google
earth engine: Planetary-scale geospatial analysis for every-
one. Remote Sensing of Environment , 202:18–27, 2017. Big
Remotely Sensed Data: tools, applications and experiences.
1
[19] Pengyu Hao, Yulin Zhan, Li Wang, Zheng Niu, and Muham-
mad Shakir. Feature selection of time series modis data for
early crop classification using random forest: A case study
in Kansas, USA. Remote Sensing , 7(5):5347–5369, 2015. 2
[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.
Deep residual learning for image recognition. In 2016 IEEE
Conference on Computer Vision and Pattern Recognition
(CVPR) , pages 770–778, 2016. 1, 2
[21] Dan Hendrycks and Kevin Gimpel. Gaussian error linear
units (gelus). arXiv: Learning , 2016. 3
[22] Dino Ienco, Raffaele Gaetano, Claire Dupaquier, and Pierre
Maurel. Land cover classification via multitemporal spatial