gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
with his help during our time of need. Satellite imagery and
derived images used in this paper in are from datasets which
redistribute imagery from Google Earth, DigitalGlobe, and
Copernicus Sentinel 2022 data. Trevor Darrell’s group was
supported in part by funding from the Department of Defense
as well as BAIR’s industrial alliance programs. Ritwik Gupta
is supported by the National Science Foundation under Grant
No. DGE-2125913.
References
[1]Manuela Andreoni, Blacki Migliozzi, Pablo Robles, and
Denise Lu. The Illegal Airstrips Bringing Toxic Mining
to Brazil’s Indigenous Land. The New York Times , 2022.
[2]Saeed Anwar, Salman Khan, and Nick Barnes. A deep
journey into super-resolution: A survey. ACM Computing
Surveys (CSUR) , 53(3):1–34, 2020.
[3]Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tan-
may, Marshall Burke, David Lobell, and Stefano Ermon.
Geography-Aware Self-Supervised Learning. In 2021
IEEE/CVF International Conference on Computer Vision
(ICCV) , pages 10161–10170, Montreal, QC, Canada, Oct.
2021. IEEE.
[4]Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tan-
may, Marshall Burke, David Lobell, and Stefano Ermon.
Geography-aware self-supervised learning. In Proceedings of
the IEEE/CVF International Conference on Computer Vision ,
pages 10181–10190, 2021.
[5]Yoshua Bengio, Aaron Courville, and Pascal Vincent. Rep-
resentation learning: A review and new perspectives. IEEE
transactions on pattern analysis and machine intelligence ,
35(8):1798–1828, 2013.
[6]P. Burt and E. Adelson. The Laplacian Pyramid as a Com-
pact Image Code. IEEE Transactions on Communications ,
31(4):532–540, Apr. 1983.
[7]Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jegou,
Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerg-
ing Properties in Self-Supervised Vision Transformers. In
2021 IEEE/CVF International Conference on Computer Vi-
sion (ICCV) , pages 9630–9640, Montreal, QC, Canada, Oct.
2021. IEEE.
[8]Fen Chen, Ruilong Ren, Tim Van de V oorde, Wenbo Xu,
Guiyun Zhou, and Yan Zhou. Fast Automatic Airport Detec-
tion in Remote Sensing Images Using Convolutional Neural
Networks. Remote Sensing , 10(3):443, Mar. 2018.
[9]Xinlei Chen and Kaiming He. Exploring Simple Siamese
Representation Learning. In Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition ,
pages 15750–15758, 2021.
[10] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning contin-
uous image representation with local implicit image function.
InProceedings of the IEEE/CVF conference on computer
vision and pattern recognition , pages 8628–8638, 2021.
[11] Gong Cheng, Junwei Han, and Xiaoqiang Lu. Remote Sens-
ing Image Scene Classification: Benchmark and State of the
Art. Proceedings of the IEEE , 105(10):1865–1883, Oct.
2017.
[12] Gordon Christie, Neil Fendley, James Wilson, and Ryan
Mukherjee. Functional Map of the World. In 2018 IEEE/CVF
Conference on Computer Vision and Pattern Recognition ,
pages 6172–6180, Salt Lake City, UT, June 2018. IEEE.
[13] Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu,
Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, and
Stefano Ermon. SatMAE: Pre-training Transformers for
Temporal and Multi-Spectral Satellite Imagery, Oct. 2022.
[14] Dengxin Dai and Wen Yang. Satellite Image Classification via
Two-Layer Sparse Coding With Biased Image Representation.
IEEE Geoscience and Remote Sensing Letters , 8(1):173–176,
Jan. 2011.
[15] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina
Toutanova. Bert: Pre-training of deep bidirectional
transformers for language understanding. arXiv preprint
arXiv:1810.04805 , 2018.
[16] Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman,
Ning Zhang, Eric Tzeng, and Trevor Darrell. Decaf: A deep
convolutional activation feature for generic visual recognition.
InInternational conference on machine learning , pages 647–
655. PMLR, 2014.
[17] Dumitru Erhan, Aaron Courville, Yoshua Bengio, and Pascal
Vincent. Why does unsupervised pre-training help deep
learning? In Proceedings of the thirteenth international
conference on artificial intelligence and statistics , pages 201–
208. JMLR Workshop and Conference Proceedings, 2010.
[18] Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li,
Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer.
Multiscale vision transformers. In Proceedings of the
IEEE/CVF International Conference on Computer Vision ,
pages 6824–6835, 2021.
[19] Pedro Felzenszwalb, David McAllester, and Deva Ramanan.
A discriminatively trained, multiscale, deformable part model.
In2008 IEEE Conference on Computer Vision and Pattern
Recognition , pages 1–8, June 2008.
[20] Anthony Fuller, Koreen Millard, and James R. Green. SatViT:
Pretraining Transformers for Earth Observation. IEEE Geo-
science and Remote Sensing Letters , 19:1–5, 2022.[21] Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, and
Yu Qiao. ConvMAE: Masked Convolution Meets Masked
Autoencoders, May 2022.
[22] Yuan Gao, Xiaojuan Sun, and Chao Liu. A general self-
supervised framework for remote sensing image classification.
Remote Sensing , 14(19):4824, 2022.
[23] Golnaz Ghiasi and Charless C. Fowlkes. Laplacian Pyramid
Reconstruction and Refinement for Semantic Segmentation.

with his help during our time of need. Satellite imagery and

derived images used in this paper in are from datasets which

redistribute imagery from Google Earth, DigitalGlobe, and

Copernicus Sentinel 2022 data. Trevor Darrell’s group was

supported in part by funding from the Department of Defense

as well as BAIR’s industrial alliance programs. Ritwik Gupta

is supported by the National Science Foundation under Grant

No. DGE-2125913.

References

[1]Manuela Andreoni, Blacki Migliozzi, Pablo Robles, and

Denise Lu. The Illegal Airstrips Bringing Toxic Mining

to Brazil’s Indigenous Land. The New York Times , 2022.

[2]Saeed Anwar, Salman Khan, and Nick Barnes. A deep

journey into super-resolution: A survey. ACM Computing

Surveys (CSUR) , 53(3):1–34, 2020.

[3]Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tan-

may, Marshall Burke, David Lobell, and Stefano Ermon.

Geography-Aware Self-Supervised Learning. In 2021

IEEE/CVF International Conference on Computer Vision

(ICCV) , pages 10161–10170, Montreal, QC, Canada, Oct.

2021. IEEE.

[4]Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tan-

may, Marshall Burke, David Lobell, and Stefano Ermon.

Geography-aware self-supervised learning. In Proceedings of

the IEEE/CVF International Conference on Computer Vision ,

pages 10181–10190, 2021.

[5]Yoshua Bengio, Aaron Courville, and Pascal Vincent. Rep-

resentation learning: A review and new perspectives. IEEE

transactions on pattern analysis and machine intelligence ,

35(8):1798–1828, 2013.

[6]P. Burt and E. Adelson. The Laplacian Pyramid as a Com-

pact Image Code. IEEE Transactions on Communications ,

31(4):532–540, Apr. 1983.

[7]Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jegou,

Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerg-

ing Properties in Self-Supervised Vision Transformers. In

2021 IEEE/CVF International Conference on Computer Vi-

sion (ICCV) , pages 9630–9640, Montreal, QC, Canada, Oct.

2021. IEEE.

[8]Fen Chen, Ruilong Ren, Tim Van de V oorde, Wenbo Xu,

Guiyun Zhou, and Yan Zhou. Fast Automatic Airport Detec-

tion in Remote Sensing Images Using Convolutional Neural

Networks. Remote Sensing , 10(3):443, Mar. 2018.

[9]Xinlei Chen and Kaiming He. Exploring Simple Siamese

Representation Learning. In Proceedings of the IEEE/CVF

Conference on Computer Vision and Pattern Recognition ,

pages 15750–15758, 2021.

[10] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning contin-

uous image representation with local implicit image function.

InProceedings of the IEEE/CVF conference on computer

vision and pattern recognition , pages 8628–8638, 2021.

[11] Gong Cheng, Junwei Han, and Xiaoqiang Lu. Remote Sens-

ing Image Scene Classification: Benchmark and State of the

Art. Proceedings of the IEEE , 105(10):1865–1883, Oct.

2017.

[12] Gordon Christie, Neil Fendley, James Wilson, and Ryan

Mukherjee. Functional Map of the World. In 2018 IEEE/CVF

Conference on Computer Vision and Pattern Recognition ,

pages 6172–6180, Salt Lake City, UT, June 2018. IEEE.

[13] Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu,

Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, and

Stefano Ermon. SatMAE: Pre-training Transformers for

Temporal and Multi-Spectral Satellite Imagery, Oct. 2022.

[14] Dengxin Dai and Wen Yang. Satellite Image Classification via

Two-Layer Sparse Coding With Biased Image Representation.

IEEE Geoscience and Remote Sensing Letters , 8(1):173–176,

Jan. 2011.

[15] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina

Toutanova. Bert: Pre-training of deep bidirectional

transformers for language understanding. arXiv preprint

arXiv:1810.04805 , 2018.

[16] Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman,

Ning Zhang, Eric Tzeng, and Trevor Darrell. Decaf: A deep

convolutional activation feature for generic visual recognition.

InInternational conference on machine learning , pages 647–

655. PMLR, 2014.

[17] Dumitru Erhan, Aaron Courville, Yoshua Bengio, and Pascal

Vincent. Why does unsupervised pre-training help deep

learning? In Proceedings of the thirteenth international

conference on artificial intelligence and statistics , pages 201–

208. JMLR Workshop and Conference Proceedings, 2010.

[18] Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li,

Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer.

Multiscale vision transformers. In Proceedings of the

IEEE/CVF International Conference on Computer Vision ,

pages 6824–6835, 2021.

[19] Pedro Felzenszwalb, David McAllester, and Deva Ramanan.

A discriminatively trained, multiscale, deformable part model.

In2008 IEEE Conference on Computer Vision and Pattern

Recognition , pages 1–8, June 2008.

[20] Anthony Fuller, Koreen Millard, and James R. Green. SatViT:

Pretraining Transformers for Earth Observation. IEEE Geo-

science and Remote Sensing Letters , 19:1–5, 2022.[21] Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, and

Yu Qiao. ConvMAE: Masked Convolution Meets Masked

Autoencoders, May 2022.

[22] Yuan Gao, Xiaojuan Sun, and Chao Liu. A general self-

supervised framework for remote sensing image classification.

Remote Sensing , 14(19):4824, 2022.

[23] Golnaz Ghiasi and Charless C. Fowlkes. Laplacian Pyramid

Reconstruction and Refinement for Semantic Segmentation.