gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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5.3. Experiments on GeoImageNet
After fMoW, we adopt our methods for unsupervised
learning on fMoW for improving representation learning on
the GeoImageNet. Unfortunately, since ImageNet does not
contain images from the same area over time we are not able
to integrate the temporal positive pairs into the MoCo-v2
objective. However, in our GeoImageNet experiments we
show that we can improve MoCo-v2 by introducing geo-
location classiﬁcation pre-text task.
Qualitative Analysis Table 6 shows the top-1 and top-5
classiﬁcation accuracy scores on the test set of GeoIma-
geNet. Surprisingly, with only geo-location classiﬁcation
task we can achieve 22.26% top-1 accuracy. With MoCo-v2
baseline, we get 38.51accuracy, about 3.47% more than su-
pervised learning method. With the addition of geo-location
classiﬁcation, we can further improve the top-1 accuracy by
1.45%. These results are interesting in a way that MoCo-v2
8
(200 epochs) on ImageNet-1k performs 8%worse than su-
pervised learning whereas it outperforms supervised learn-
ing on our highly imbalanced GeoImageNet with 5150 class
categories which is about 5×more than ImageNet-1k.
BackboneTop-1
(Accuracy)↑Top-5
(Accuracy)↑
Sup. Learning (Scratch) ResNet50 35.04 54.11
Geoloc. Learning ResNet50 22.26 39.33
MoCo-V2 ResNet50 38.51 57.67
MoCo-V2+Geo ResNet50 39.96 58.71
Table 6: Experiments on GeoImageNet. We divide the
dataset into 443,435 training and 100,000 test images across
5150 classes. We train MoCo-V2 and MoCo-V2+Geo for
200 epochs whereas Sup. and Geoloc. Learning are
trained until they converge .
6. Conclusion
In this work, we provide a self-supervised learning
framework for remote sensing data, where unlabeled data is
often plentiful but labeled data is scarce. By leveraging spa-
tially aligned images over time to construct temporal posi-
tive pairs in contrastive learning and geo-location in the de-
sign of pre-text tasks, we are able to close the gap between
self-supervised and supervised learning on image classiﬁca-
tion, object detection and semantic segmentation on remote
sensing and other geo-tagged image datasets.
Acknowledgement
This research is based upon work supported in part by the
Ofﬁce of the Director of National Intelligence (ODNI), In-
telligence Advanced Research Projects Activity (IARPA),
via 2021-2011000004. The views and conclusions con-
tained herein are those of the authors and should not be
interpreted as necessarily representing the ofﬁcial policies,
either expressed or implied, of ODNI, IARPA, or the U.S.
Government. The U.S. Government is authorized to repro-
duce and distribute reprints for governmental purposes not-
withstanding any copyright annotation therein.
This research was also supported by Stanford Data
for Development Initiative, HAI, IARPA SMART, ONR
(N00014-19-1-2145), and NSF grants #1651565 and
#1733686.
References
[1] Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell,
and Stefano Ermon. Generating interpretable poverty maps
using object detection in satellite images. arXiv preprint
arXiv:2002.01612 , 2020. 2
[2] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Ge-
offrey Hinton. A simple framework for contrastive learningof visual representations. arXiv preprint arXiv:2002.05709 ,
2020. 1, 2, 4
[3] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He.
Improved baselines with momentum contrastive learning.
arXiv preprint arXiv:2003.04297 , 2020. 1, 2, 4, 6, 7, 8
[4] Anil M Cheriyadat. Unsupervised feature learning for aerial
scene classiﬁcation. IEEE Transactions on Geoscience and
Remote Sensing , 52(1):439–451, 2013. 2
[5] Gordon Christie, Neil Fendley, James Wilson, and Ryan
Mukherjee. Functional map of the world. In Proceedings
of the IEEE Conference on Computer Vision and Pattern
Recognition , pages 6172–6180, 2018. 1, 2, 4
[6] Grace Chu, Brian Potetz, Weijun Wang, Andrew Howard,
Yang Song, Fernando Brucher, Thomas Leung, and Hartwig
Adam. Geo-aware networks for ﬁne-grained recognition. In
Proceedings of the IEEE International Conference on Com-
puter Vision Workshops , pages 0–0, 2019. 3
[7] Terrance de Vries, Ishan Misra, Changhan Wang, and Lau-
rens van der Maaten. Does object recognition work for ev-
eryone? In Proceedings of the IEEE Conference on Com-
puter Vision and Pattern Recognition Workshops , pages 52–
59, 2019. 2, 4
[8] 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. 4
[9] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Un-
supervised representation learning by predicting image rota-
tions. arXiv preprint arXiv:1803.07728 , 2018. 2
[10] Jean-Bastien Grill, Florian Strub, Florent Altch ´e, 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, 2020. 1

5.3. Experiments on GeoImageNet

After fMoW, we adopt our methods for unsupervised

learning on fMoW for improving representation learning on

the GeoImageNet. Unfortunately, since ImageNet does not

contain images from the same area over time we are not able

to integrate the temporal positive pairs into the MoCo-v2

objective. However, in our GeoImageNet experiments we

show that we can improve MoCo-v2 by introducing geo-

location classiﬁcation pre-text task.

Qualitative Analysis Table 6 shows the top-1 and top-5

classiﬁcation accuracy scores on the test set of GeoIma-

geNet. Surprisingly, with only geo-location classiﬁcation

task we can achieve 22.26% top-1 accuracy. With MoCo-v2

baseline, we get 38.51accuracy, about 3.47% more than su-

pervised learning method. With the addition of geo-location

classiﬁcation, we can further improve the top-1 accuracy by

1.45%. These results are interesting in a way that MoCo-v2

8

(200 epochs) on ImageNet-1k performs 8%worse than su-

pervised learning whereas it outperforms supervised learn-

ing on our highly imbalanced GeoImageNet with 5150 class

categories which is about 5×more than ImageNet-1k.

BackboneTop-1

(Accuracy)↑Top-5

(Accuracy)↑

Sup. Learning (Scratch) ResNet50 35.04 54.11

Geoloc. Learning ResNet50 22.26 39.33

MoCo-V2 ResNet50 38.51 57.67

MoCo-V2+Geo ResNet50 39.96 58.71

Table 6: Experiments on GeoImageNet. We divide the

dataset into 443,435 training and 100,000 test images across

5150 classes. We train MoCo-V2 and MoCo-V2+Geo for

200 epochs whereas Sup. and Geoloc. Learning are

trained until they converge .

6. Conclusion

In this work, we provide a self-supervised learning

framework for remote sensing data, where unlabeled data is

often plentiful but labeled data is scarce. By leveraging spa-

tially aligned images over time to construct temporal posi-

tive pairs in contrastive learning and geo-location in the de-

sign of pre-text tasks, we are able to close the gap between

self-supervised and supervised learning on image classiﬁca-

tion, object detection and semantic segmentation on remote

sensing and other geo-tagged image datasets.

Acknowledgement

This research is based upon work supported in part by the

Ofﬁce of the Director of National Intelligence (ODNI), In-

telligence Advanced Research Projects Activity (IARPA),

via 2021-2011000004. The views and conclusions con-

tained herein are those of the authors and should not be

interpreted as necessarily representing the ofﬁcial policies,

either expressed or implied, of ODNI, IARPA, or the U.S.

Government. The U.S. Government is authorized to repro-

duce and distribute reprints for governmental purposes not-

withstanding any copyright annotation therein.

This research was also supported by Stanford Data

for Development Initiative, HAI, IARPA SMART, ONR

(N00014-19-1-2145), and NSF grants #1651565 and

#1733686.

References

[1] Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell,

and Stefano Ermon. Generating interpretable poverty maps

using object detection in satellite images. arXiv preprint

arXiv:2002.01612 , 2020. 2

[2] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Ge-

offrey Hinton. A simple framework for contrastive learningof visual representations. arXiv preprint arXiv:2002.05709 ,

2020. 1, 2, 4

[3] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He.

Improved baselines with momentum contrastive learning.

arXiv preprint arXiv:2003.04297 , 2020. 1, 2, 4, 6, 7, 8

[4] Anil M Cheriyadat. Unsupervised feature learning for aerial

scene classiﬁcation. IEEE Transactions on Geoscience and

Remote Sensing , 52(1):439–451, 2013. 2

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

Mukherjee. Functional map of the world. In Proceedings

of the IEEE Conference on Computer Vision and Pattern

Recognition , pages 6172–6180, 2018. 1, 2, 4

[6] Grace Chu, Brian Potetz, Weijun Wang, Andrew Howard,

Yang Song, Fernando Brucher, Thomas Leung, and Hartwig

Adam. Geo-aware networks for ﬁne-grained recognition. In

Proceedings of the IEEE International Conference on Com-

puter Vision Workshops , pages 0–0, 2019. 3

[7] Terrance de Vries, Ishan Misra, Changhan Wang, and Lau-

rens van der Maaten. Does object recognition work for ev-

eryone? In Proceedings of the IEEE Conference on Com-

puter Vision and Pattern Recognition Workshops , pages 52–

59, 2019. 2, 4

[8] 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. 4

[9] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Un-

supervised representation learning by predicting image rota-

tions. arXiv preprint arXiv:1803.07728 , 2018. 2

[10] Jean-Bastien Grill, Florian Strub, Florent Altch ´e, 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, 2020. 1