gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
conference on computer vision , pages 1008–1016, 2015. 3
[34] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Con-
trastive multiview coding. arXiv preprint arXiv:1906.05849 ,
2019. 4
[35] Burak Uzkent, Aneesh Rangnekar, and Matthew J Hoffman.
Tracking in aerial hyperspectral videos using deep kernelized
correlation ﬁlters. IEEE Transactions on Geoscience and
Remote Sensing , 57(1):449–461, 2018. 2
[36] Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang,
Marshall Burke, David Lobell, and Stefano Ermon. Learning
to interpret satellite images in global scale using wikipedia.
arXiv preprint arXiv:1905.02506 , 2019. 1, 2
[37] Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang,
Marshall Burke, David Lobell, and Stefano Ermon. Learning
to interpret satellite images using wikipedia. In Proceedings
of the Twenty-Eighth International Joint Conference on Arti-
ﬁcial Intelligence , 2019. 1
[38] Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang,
Marshall Burke, David B Lobell, and Stefano Ermon. Learn-
ing to interpret satellite images using wikipedia. In IJCAI ,
pages 3620–3626, 2019. 1
[39] Burak Uzkent, Christopher Yeh, and Stefano Ermon. Efﬁ-
cient object detection in large images using deep reinforce-
ment learning. In Proceedings of the IEEE/CVF Winter Con-
ference on Applications of Computer Vision , pages 1824–
1833, 2020. 2
[40] Adam Van Etten, Dave Lindenbaum, and Todd M Bacastow.
Spacenet: A remote sensing dataset and challenge series.
arXiv preprint arXiv:1807.01232 , 2018. 8
[41] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and
Pierre-Antoine Manzagol. Extracting and composing robust
features with denoising autoencoders. In Proceedings of the
25th international conference on Machine learning , pages
1096–1103, 2008. 2
[42] Nam V o, Nathan Jacobs, and James Hays. Revisiting im2gps
in the deep learning era. In Proceedings of the IEEE Inter-
10
national Conference on Computer Vision , pages 2621–2630,
2017. 3
[43] Xiaolong Wang and Abhinav Gupta. Unsupervised learn-
ing of visual representations using videos. In Proceedings of
the IEEE international conference on computer vision , pages
2794–2802, 2015. 2
[44] Tobias Weyand, Ilya Kostrikov, and James Philbin. Planet-
photo geolocation with convolutional neural networks. In
European Conference on Computer Vision , pages 37–55.
Springer, 2016. 3
[45] Yi Yang and Shawn Newsam. Bag-of-visual-words and spa-
tial extensions for land-use classiﬁcation. In Proceedings of
the 18th SIGSPATIAL international conference on advances
in geographic information systems , pages 270–279, 2010. 2
[46] Xiwen Yao, Junwei Han, Gong Cheng, Xueming Qian, and
Lei Guo. Semantic annotation of high-resolution satellite
images via weakly supervised learning. IEEE Transactions
on Geoscience and Remote Sensing , 54(6):3660–3671, 2016.
2
[47] Lefei Zhang, Liangpei Zhang, Bo Du, Jane You, and
Dacheng Tao. Hyperspectral image unsupervised classiﬁ-
cation by robust manifold matrix factorization. Information
Sciences , 485:154–169, 2019. 2
[48] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful
image colorization. In European conference on computer
vision , pages 649–666. Springer, 2016. 2
[49] Richard Zhang, Phillip Isola, and Alexei A Efros. Split-brain
autoencoders: Unsupervised learning by cross-channel pre-
diction. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition , pages 1058–1067, 2017. 2
[50] Hengshuang Zhao, Yi Zhang, Shu Liu, Jianping Shi, Chen
Change Loy, Dahua Lin, and Jiaya Jia. Psanet: Point-wise
spatial attention network for scene parsing. In Proceedings
of the European Conference on Computer Vision (ECCV) ,
pages 267–283, 2018. 8
11
SSL4EO-L:
Datasets and Foundation Models for Landsat Imagery
Adam J. Stewart1, Nils Lehmann2, Isaac A. Corley3, Yi Wang2, 4, Yi-Chia Chang1,
Nassim Ait Ali Braham2, 4, Shradha Sehgal1, Caleb Robinson5, Arindam Banerjee1
1University of Illinois Urbana-Champaign,2Technical University of Munich,
3University of Texas at San Antonio,4German Aerospace Center,
5Microsoft AI for Good Research Lab
Abstract
The Landsat program is the longest-running Earth observation program in history,
with 50+ years of data acquisition by 8 satellites. The multispectral imagery cap-
tured by sensors onboard these satellites is critical for a wide range of scientific
fields. Despite the increasing popularity of deep learning and remote sensing,
the majority of researchers still use decision trees and random forests for Land-
sat image analysis due to the prevalence of small labeled datasets and lack of
foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset
designed for Self-Supervised Learning for EarthObservation for the Landsat fam-
ily of satellites (including 3 sensors and 2 product levels) and the largest Landsat
dataset in history (5M image patches). Additionally, we modernize and re-release
the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML
benchmark datasets for Landsats 4–5 TM and Landsat 7 ETM+ SR. Finally, we
pre-train the first foundation models for Landsat imagery using SSL4EO-L and
evaluate their performance on multiple semantic segmentation tasks. All datasets
and model weights are available via the TorchGeo1library, making reproducibility
and experimentation easy, and enabling scientific advancements in the burgeoning
field of remote sensing for a multitude of downstream applications.
1 Introduction
On July 23rd, 1972, the National Aeronautics and Space Administration (NASA) launched Land-

conference on computer vision , pages 1008–1016, 2015. 3

[34] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Con-

trastive multiview coding. arXiv preprint arXiv:1906.05849 ,

2019. 4

[35] Burak Uzkent, Aneesh Rangnekar, and Matthew J Hoffman.

Tracking in aerial hyperspectral videos using deep kernelized

correlation ﬁlters. IEEE Transactions on Geoscience and

Remote Sensing , 57(1):449–461, 2018. 2

[36] Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang,

Marshall Burke, David Lobell, and Stefano Ermon. Learning

to interpret satellite images in global scale using wikipedia.

arXiv preprint arXiv:1905.02506 , 2019. 1, 2

[37] Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang,

Marshall Burke, David Lobell, and Stefano Ermon. Learning

to interpret satellite images using wikipedia. In Proceedings

of the Twenty-Eighth International Joint Conference on Arti-

ﬁcial Intelligence , 2019. 1

[38] Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang,

Marshall Burke, David B Lobell, and Stefano Ermon. Learn-

ing to interpret satellite images using wikipedia. In IJCAI ,

pages 3620–3626, 2019. 1

[39] Burak Uzkent, Christopher Yeh, and Stefano Ermon. Efﬁ-

cient object detection in large images using deep reinforce-

ment learning. In Proceedings of the IEEE/CVF Winter Con-

ference on Applications of Computer Vision , pages 1824–

1833, 2020. 2

[40] Adam Van Etten, Dave Lindenbaum, and Todd M Bacastow.

Spacenet: A remote sensing dataset and challenge series.

arXiv preprint arXiv:1807.01232 , 2018. 8

[41] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and

Pierre-Antoine Manzagol. Extracting and composing robust

features with denoising autoencoders. In Proceedings of the

25th international conference on Machine learning , pages

1096–1103, 2008. 2

[42] Nam V o, Nathan Jacobs, and James Hays. Revisiting im2gps

in the deep learning era. In Proceedings of the IEEE Inter-

10

national Conference on Computer Vision , pages 2621–2630,

2017. 3

[43] Xiaolong Wang and Abhinav Gupta. Unsupervised learn-

ing of visual representations using videos. In Proceedings of

the IEEE international conference on computer vision , pages

2794–2802, 2015. 2

[44] Tobias Weyand, Ilya Kostrikov, and James Philbin. Planet-

photo geolocation with convolutional neural networks. In

European Conference on Computer Vision , pages 37–55.

Springer, 2016. 3

[45] Yi Yang and Shawn Newsam. Bag-of-visual-words and spa-

tial extensions for land-use classiﬁcation. In Proceedings of

the 18th SIGSPATIAL international conference on advances

in geographic information systems , pages 270–279, 2010. 2

[46] Xiwen Yao, Junwei Han, Gong Cheng, Xueming Qian, and

Lei Guo. Semantic annotation of high-resolution satellite

images via weakly supervised learning. IEEE Transactions

on Geoscience and Remote Sensing , 54(6):3660–3671, 2016.

2

[47] Lefei Zhang, Liangpei Zhang, Bo Du, Jane You, and

Dacheng Tao. Hyperspectral image unsupervised classiﬁ-

cation by robust manifold matrix factorization. Information

Sciences , 485:154–169, 2019. 2

[48] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful

image colorization. In European conference on computer

vision , pages 649–666. Springer, 2016. 2

[49] Richard Zhang, Phillip Isola, and Alexei A Efros. Split-brain

autoencoders: Unsupervised learning by cross-channel pre-

diction. In Proceedings of the IEEE Conference on Computer

Vision and Pattern Recognition , pages 1058–1067, 2017. 2

[50] Hengshuang Zhao, Yi Zhang, Shu Liu, Jianping Shi, Chen

Change Loy, Dahua Lin, and Jiaya Jia. Psanet: Point-wise

spatial attention network for scene parsing. In Proceedings

of the European Conference on Computer Vision (ECCV) ,

pages 267–283, 2018. 8

11

SSL4EO-L:

Datasets and Foundation Models for Landsat Imagery

Adam J. Stewart1, Nils Lehmann2, Isaac A. Corley3, Yi Wang2, 4, Yi-Chia Chang1,

Nassim Ait Ali Braham2, 4, Shradha Sehgal1, Caleb Robinson5, Arindam Banerjee1

1University of Illinois Urbana-Champaign,2Technical University of Munich,

3University of Texas at San Antonio,4German Aerospace Center,

5Microsoft AI for Good Research Lab

Abstract

The Landsat program is the longest-running Earth observation program in history,

with 50+ years of data acquisition by 8 satellites. The multispectral imagery cap-

tured by sensors onboard these satellites is critical for a wide range of scientific

fields. Despite the increasing popularity of deep learning and remote sensing,

the majority of researchers still use decision trees and random forests for Land-

sat image analysis due to the prevalence of small labeled datasets and lack of

foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset

designed for Self-Supervised Learning for EarthObservation for the Landsat fam-

ily of satellites (including 3 sensors and 2 product levels) and the largest Landsat

dataset in history (5M image patches). Additionally, we modernize and re-release

the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML

benchmark datasets for Landsats 4–5 TM and Landsat 7 ETM+ SR. Finally, we

pre-train the first foundation models for Landsat imagery using SSL4EO-L and

evaluate their performance on multiple semantic segmentation tasks. All datasets

and model weights are available via the TorchGeo1library, making reproducibility

and experimentation easy, and enabling scientific advancements in the burgeoning

field of remote sensing for a multitude of downstream applications.

1 Introduction

On July 23rd, 1972, the National Aeronautics and Space Administration (NASA) launched Land-