gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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(a) Image
(b) Mask
(c) Prediction
Figure 5: Landsat 7 ETM+ TOA image, ground truth mask, and prediction made by a U-Net with a
ResNet-18 backbone pre-trained using MoCo and SSL4EO-L and fine-tuned on L7 Irish.
Figure 5 shows an example prediction made by a U-Net pre-trained on SSL4EO-L and fine-tuned on
L7 Irish. The model is able to correctly detect the majority of clouds in the image, but fails to detect
cloud shadow due to its infrequent appearance in the training dataset. However, the model actually
does a better job than the human annotator in the lower left corner, where the “ground truth” mask
misses substantial cloud and thin cloud.
5 Limitations
There are a few limitations of the sampling method we chose to create our pre-training dataset. Due
to low light levels near the poles, Landsat satellites do not capture images above 81.8° latitudes [77],
and do not produce SR products above 76° latitudes.4The additional 23.5° tilt of the Earth’s axis
during the winter [78] means that it is not possible to collect imagery for all 4 seasons above 52.5°
latitude. It may be possible to relax this constraint and allow for sampling from locations where 3
out of 4 seasons have imagery. Due to cloud cover and lower populations, there is very little imagery
of tropical rainforests or polar regions, both of which are common applications of Landsat data.
The benchmark datasets we create are limited to the United States and may not adequately re-
flect performance in other regions where agricultural practices and crops differ greatly. Ideally, we
would create additional global datasets. There exist large global Landsat-based datasets including
the Global Forest Cover Change dataset [40]. However, these datasets do not exist during all times
when these satellites are active. We would also like to have classification datasets in addition to
semantic segmentation datasets. It may be possible to classify images by biome, although this task
may be too easy. In future work, we would like to add pre-trained models for MSS data, although
this will require a different sampling technique due to limited coverage over most of the world.
6 Conclusion
In this paper we introduce the SSL4EO-L pre-training dataset, the first ever SSL dataset for Landsat
imagery and the largest Landsat dataset in history. We pre-train the first foundation models for
the Landsat family of satellites, enabling progress in a multitude of scientific fields that can benefit
from remote sensing and deep learning. Additionally, we revitalize the L7 Irish and L8 Biome
datasets. We create the first benchmark datasets for the TM and ETM+ SR sensors, allowing direct
comparison across all modern Landsat sensors and products. All datasets, model weights, training
code, and scripts used to produce our results are distributed via the TorchGeo library, allowing for
ease of experimentation and reproduction of our results.
4https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance
10
Acknowledgments and Disclosure of Funding
The authors gratefully acknowledge the computational and data resources provided through the
joint high-performance data analytics (HPDA) project “terrabyte” of the German Aerospace Center
(DLR) and the Leibniz Supercomputing Center (LRZ). This work was supported by the Helmholtz
Association’s Initiative and Networking Fund on the HAICORE@FZJ partition. This work made
use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus
Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications
(NCSA) and which is supported by funds from the University of Illinois at Urbana-Champaign. The
work was supported in part by the National Science Foundation (NSF) through awards IIS 21-31335,
OAC 21-30835, DBI 20-21898, as well as a C3.ai research award and the Taiwan-UIUC Fellowship.
References
[1] Laura E. P. Rocchio. Virginia T. Norwood: The mother of Landsat. Landsat Science , August
2020.
[2] Bill P. Clark. Landsat 3 Return Beam Vidicon response artifacts: A report on RBV pho-
tographic product characteristics and quality coding system. Technical report, EROS Data
Center, U.S. Geological Survey, August 1981.
[3] Christopher Engebretson. Landsat Multispectral Scanner (MSS) Collection 2 (C2) Level 1
(L1) Data Format Control Book (DFCB). Technical report, Department of the Interior, U.S.
Geological Survey, September 2020. LSDS-1416.
[4] Christopher Engebretson. Landsat Thematic Mapper (TM) Level 1 (L1) Data Format Control
Book (DFCB). Technical report, Department of the Interior, U.S. Geological Survey, February
2018. LSDS-284.
[5] Jim Lacasse. Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) Level 1 (L1) Data
Format Control Book (DFCB). Technical report, Department of the Interior, U.S. Geological
Survey, August 2016. LSDS-272.
[6] Christopher Engebretson. Landsat 8–9 Operational Land Imager (OLI) - Thermal Infrarer
Sensor (TIRS) Collection 2 Level 1 (L1) Data Format Control Book (DFCB). Technical report,
Department of the Interior, U.S. Geological Survey, September 2020. LSDS-1822.
[7] Nicholas E. Young, Ryan S. Anderson, Stephen M. Chignell, Anthony G. V orster, Rick
Lawrence, and Paul H. Evangelista. A survival guide to Landsat preprocessing. Ecology ,
98(4):920–932, 2017.
[8] Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, and Stefano Ermon.
Tile2Vec: Unsupervised representation learning for spatially distributed data. In Proceedings
of the AAAI Conference on Artificial Intelligence , volume 33, pages 3967–3974, 2019.
[9] Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lo-
bell, and Stefano Ermon. Geography-aware self-supervised learning. In Proceedings of the
IEEE/CVF International Conference on Computer Vision , pages 10181–10190, 2021.
[10] Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall
Burke, David Lobell, and Stefano Ermon. SatMAE: Pre-training transformers for temporal
and multi-spectral satellite imagery. Advances in Neural Information Processing Systems , 35:
197–211, 2022.
[11] Colorado J. Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian
Clipp, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell. Scale-MAE: A scale-
aware masked autoencoder for multiscale geospatial representation learning. arXiv preprint
arXiv:2212.14532 , 2022.
[12] Oscar Manas, Alexandre Lacoste, Xavier Giró-i-Nieto, David Vazquez, and Pau Rodriguez.
Seasonal Contrast: Unsupervised pre-training from uncurated remote sensing data. In Pro-
ceedings of the IEEE/CVF International Conference on Computer Vision , pages 9414–9423,
2021.
11
[13] Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M. Albrecht, and
Xiao Xiang Zhu. SSL4EO-S12: A large-scale multi-modal, multi-temporal dataset for self-
supervised learning in Earth observation. arXiv preprint arXiv:2211.07044 , 2022.
[14] Di Wang, Jing Zhang, Bo Du, Gui-Song Xia, and Dacheng Tao. An empirical study of remote
sensing pretraining. IEEE Transactions on Geoscience and Remote Sensing , 2022.
[15] Yi Wang, Conrad M. Albrecht, Nassim Ait Ali Braham, Lichao Mou, and Xiao Xiang Zhu.
Self-supervised learning in remote sensing: A review. arXiv preprint arXiv:2206.13188 , 2022.
[16] Paul Berg, Minh-Tan Pham, and Nicolas Courty. Self-supervised learning for scene classifica-
tion in remote sensing: Current state of the art and perspectives. Remote Sensing , 14(16):3995,
2022.
[17] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with Momentum
Contrastive learning. arXiv preprint arXiv:2003.04297 , 2020.
[18] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand

(a) Image

(b) Mask

(c) Prediction

Figure 5: Landsat 7 ETM+ TOA image, ground truth mask, and prediction made by a U-Net with a

ResNet-18 backbone pre-trained using MoCo and SSL4EO-L and fine-tuned on L7 Irish.

Figure 5 shows an example prediction made by a U-Net pre-trained on SSL4EO-L and fine-tuned on

L7 Irish. The model is able to correctly detect the majority of clouds in the image, but fails to detect

cloud shadow due to its infrequent appearance in the training dataset. However, the model actually

does a better job than the human annotator in the lower left corner, where the “ground truth” mask

misses substantial cloud and thin cloud.

5 Limitations

There are a few limitations of the sampling method we chose to create our pre-training dataset. Due

to low light levels near the poles, Landsat satellites do not capture images above 81.8° latitudes [77],

and do not produce SR products above 76° latitudes.4The additional 23.5° tilt of the Earth’s axis

during the winter [78] means that it is not possible to collect imagery for all 4 seasons above 52.5°

latitude. It may be possible to relax this constraint and allow for sampling from locations where 3

out of 4 seasons have imagery. Due to cloud cover and lower populations, there is very little imagery

of tropical rainforests or polar regions, both of which are common applications of Landsat data.

The benchmark datasets we create are limited to the United States and may not adequately re-

flect performance in other regions where agricultural practices and crops differ greatly. Ideally, we

would create additional global datasets. There exist large global Landsat-based datasets including

the Global Forest Cover Change dataset [40]. However, these datasets do not exist during all times

when these satellites are active. We would also like to have classification datasets in addition to

semantic segmentation datasets. It may be possible to classify images by biome, although this task

may be too easy. In future work, we would like to add pre-trained models for MSS data, although

this will require a different sampling technique due to limited coverage over most of the world.

6 Conclusion

In this paper we introduce the SSL4EO-L pre-training dataset, the first ever SSL dataset for Landsat

imagery and the largest Landsat dataset in history. We pre-train the first foundation models for

the Landsat family of satellites, enabling progress in a multitude of scientific fields that can benefit

from remote sensing and deep learning. Additionally, we revitalize the L7 Irish and L8 Biome

datasets. We create the first benchmark datasets for the TM and ETM+ SR sensors, allowing direct

comparison across all modern Landsat sensors and products. All datasets, model weights, training

code, and scripts used to produce our results are distributed via the TorchGeo library, allowing for

ease of experimentation and reproduction of our results.

4https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance

10

Acknowledgments and Disclosure of Funding

The authors gratefully acknowledge the computational and data resources provided through the

joint high-performance data analytics (HPDA) project “terrabyte” of the German Aerospace Center

(DLR) and the Leibniz Supercomputing Center (LRZ). This work was supported by the Helmholtz

Association’s Initiative and Networking Fund on the HAICORE@FZJ partition. This work made

use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus

Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications

(NCSA) and which is supported by funds from the University of Illinois at Urbana-Champaign. The

work was supported in part by the National Science Foundation (NSF) through awards IIS 21-31335,

OAC 21-30835, DBI 20-21898, as well as a C3.ai research award and the Taiwan-UIUC Fellowship.

References

[1] Laura E. P. Rocchio. Virginia T. Norwood: The mother of Landsat. Landsat Science , August

2020.

[2] Bill P. Clark. Landsat 3 Return Beam Vidicon response artifacts: A report on RBV pho-

tographic product characteristics and quality coding system. Technical report, EROS Data

Center, U.S. Geological Survey, August 1981.

[3] Christopher Engebretson. Landsat Multispectral Scanner (MSS) Collection 2 (C2) Level 1

(L1) Data Format Control Book (DFCB). Technical report, Department of the Interior, U.S.

Geological Survey, September 2020. LSDS-1416.

[4] Christopher Engebretson. Landsat Thematic Mapper (TM) Level 1 (L1) Data Format Control

Book (DFCB). Technical report, Department of the Interior, U.S. Geological Survey, February

2018. LSDS-284.

[5] Jim Lacasse. Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) Level 1 (L1) Data

Format Control Book (DFCB). Technical report, Department of the Interior, U.S. Geological

Survey, August 2016. LSDS-272.

[6] Christopher Engebretson. Landsat 8–9 Operational Land Imager (OLI) - Thermal Infrarer

Sensor (TIRS) Collection 2 Level 1 (L1) Data Format Control Book (DFCB). Technical report,

Department of the Interior, U.S. Geological Survey, September 2020. LSDS-1822.

[7] Nicholas E. Young, Ryan S. Anderson, Stephen M. Chignell, Anthony G. V orster, Rick

Lawrence, and Paul H. Evangelista. A survival guide to Landsat preprocessing. Ecology ,

98(4):920–932, 2017.

[8] Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, and Stefano Ermon.

Tile2Vec: Unsupervised representation learning for spatially distributed data. In Proceedings

of the AAAI Conference on Artificial Intelligence , volume 33, pages 3967–3974, 2019.

[9] Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lo-

bell, and Stefano Ermon. Geography-aware self-supervised learning. In Proceedings of the

IEEE/CVF International Conference on Computer Vision , pages 10181–10190, 2021.

[10] Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall

Burke, David Lobell, and Stefano Ermon. SatMAE: Pre-training transformers for temporal

and multi-spectral satellite imagery. Advances in Neural Information Processing Systems , 35:

197–211, 2022.

[11] Colorado J. Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian

Clipp, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell. Scale-MAE: A scale-

aware masked autoencoder for multiscale geospatial representation learning. arXiv preprint

arXiv:2212.14532 , 2022.

[12] Oscar Manas, Alexandre Lacoste, Xavier Giró-i-Nieto, David Vazquez, and Pau Rodriguez.

Seasonal Contrast: Unsupervised pre-training from uncurated remote sensing data. In Pro-

ceedings of the IEEE/CVF International Conference on Computer Vision , pages 9414–9423,

2021.

11

[13] Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M. Albrecht, and

Xiao Xiang Zhu. SSL4EO-S12: A large-scale multi-modal, multi-temporal dataset for self-

supervised learning in Earth observation. arXiv preprint arXiv:2211.07044 , 2022.

[14] Di Wang, Jing Zhang, Bo Du, Gui-Song Xia, and Dacheng Tao. An empirical study of remote

sensing pretraining. IEEE Transactions on Geoscience and Remote Sensing , 2022.

[15] Yi Wang, Conrad M. Albrecht, Nassim Ait Ali Braham, Lichao Mou, and Xiao Xiang Zhu.

Self-supervised learning in remote sensing: A review. arXiv preprint arXiv:2206.13188 , 2022.

[16] Paul Berg, Minh-Tan Pham, and Nicolas Courty. Self-supervised learning for scene classifica-

tion in remote sensing: Current state of the art and perspectives. Remote Sensing , 14(16):3995,

2022.

[17] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with Momentum

Contrastive learning. arXiv preprint arXiv:2003.04297 , 2020.

[18] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand