gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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[11] James Hays and Alexei A Efros. Im2gps: estimating geo-
graphic information from a single image. In 2008 ieee con-
ference on computer vision and pattern recognition , pages
1–8. IEEE, 2008. 1, 3
[12] James Hays and Alexei A Efros. Large-scale image geolo-
calization. In Multimodal location estimation of videos and
images , pages 41–62. Springer, 2015. 1, 3
[13] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross
Girshick. Momentum contrast for unsupervised visual rep-
resentation learning. In Proceedings of the IEEE/CVF Con-
ference on Computer Vision and Pattern Recognition , pages
9729–9738, 2020. 1, 2, 4, 5, 7, 8
[14] Hayate Iso, Shoko Wakamiya, and Eiji Aramaki. Density
estimation for geolocation via convolutional mixture density
network. arXiv preprint arXiv:1705.02750 , 2017. 3
[15] Neal Jean, Sherrie Wang, Anshul Samar, George Azzari,
David Lobell, and Stefano Ermon. Tile2vec: Unsupervised
representation learning for spatially distributed data. In Pro-
ceedings of the AAAI Conference on Artiﬁcial Intelligence ,
volume 33, pages 3967–3974, 2019. 2
[16] Darius Lam, Richard Kuzma, Kevin McGee, Samuel Doo-
ley, Michael Laielli, Matthew Klaric, Yaroslav Bulatov, and
9
Brendan McCord. xview: Objects in context in overhead
imagery. arXiv preprint arXiv:1802.07856 , 2018. 1, 7
[17] Yansheng Li, Chao Tao, Yihua Tan, Ke Shang, and Jinwen
Tian. Unsupervised multilayer feature learning for satellite
image scene classiﬁcation. IEEE Geoscience and Remote
Sensing Letters , 13(2):157–161, 2016. 2
[18] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and
Piotr Doll ´ar. Focal loss for dense object detection. In Pro-
ceedings of the IEEE international conference on computer
vision , pages 2980–2988, 2017. 8
[19] Xiaoqiang Lu, Xiangtao Zheng, and Yuan Yuan. Remote
sensing scene classiﬁcation by unsupervised representation
learning. IEEE Transactions on Geoscience and Remote
Sensing , 55(9):5148–5157, 2017. 2
[20] Oisin Mac Aodha, Elijah Cole, and Pietro Perona. Presence-
only geographical priors for ﬁne-grained image classiﬁca-
tion. In Proceedings of the IEEE International Conference
on Computer Vision , pages 9596–9606, 2019. 3
[21] Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan,
Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe,
and Laurens van der Maaten. Exploring the limits of weakly
supervised pretraining. In Proceedings of the European Con-
ference on Computer Vision (ECCV) , pages 181–196, 2018.
1
[22] Ishan Misra and Laurens van der Maaten. Self-supervised
learning of pretext-invariant representations. In Proceedings
of the IEEE/CVF Conference on Computer Vision and Pat-
tern Recognition , pages 6707–6717, 2020. 2
[23] Lichao Mou, Pedram Ghamisi, and Xiao Xiang Zhu. Un-
supervised spectral–spatial feature learning via deep resid-
ual conv–deconv network for hyperspectral image classiﬁca-
tion. IEEE Transactions on Geoscience and Remote Sensing ,
56(1):391–406, 2017. 2
[24] Eric Muller-Budack, Kader Pustu-Iren, and Ralph Ewerth.
Geolocation estimation of photos using a hierarchical model
and scene classiﬁcation. In Proceedings of the European
Conference on Computer Vision (ECCV) , pages 563–579,
2018. 3
[25] T Nathan Mundhenk, Goran Konjevod, Wesam A Sakla,
and Koﬁ Boakye. A large contextual dataset for classiﬁca-
tion, detection and counting of cars with deep learning. In
European Conference on Computer Vision , pages 785–800.
Springer, 2016. 2
[26] Mehdi Noroozi and Paolo Favaro. Unsupervised learning
of visual representations by solving jigsaw puzzles. In
European Conference on Computer Vision , pages 69–84.
Springer, 2016. 2
[27] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Repre-
sentation learning with contrastive predictive coding. arXiv
preprint arXiv:1807.03748 , 2018. 4
[28] Deepak Pathak, Ross Girshick, Piotr Doll ´ar, Trevor Dar-
rell, and Bharath Hariharan. Learning features by watch-
ing objects move. In Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition , pages 2701–
2710, 2017. 2
[29] Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor
Darrell, and Alexei A Efros. Context encoders: Featurelearning by inpainting. In Proceedings of the IEEE con-
ference on computer vision and pattern recognition , pages
2536–2544, 2016. 2
[30] Adriana Romero, Carlo Gatta, and Gustau Camps-Valls. Un-
supervised deep feature extraction for remote sensing image
classiﬁcation. IEEE Transactions on Geoscience and Remote
Sensing , 54(3):1349–1362, 2015. 2
[31] Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent,
Neal Jean, Marshall Burke, David Lobell, and Stefano Er-
mon. Predicting economic development using geolocated
wikipedia articles. In Proceedings of the 25th ACM SIGKDD
International Conference on Knowledge Discovery & Data
Mining , pages 2698–2706, 2019. 1
[32] Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao
Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han
Zhang, and Colin Raffel. Fixmatch: Simplifying semi-
supervised learning with consistency and conﬁdence. arXiv
preprint arXiv:2001.07685 , 2020. 2
[33] Kevin Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, and
Lubomir Bourdev. Improving image classiﬁcation with lo-
cation context. In Proceedings of the IEEE international

[11] James Hays and Alexei A Efros. Im2gps: estimating geo-

graphic information from a single image. In 2008 ieee con-

ference on computer vision and pattern recognition , pages

1–8. IEEE, 2008. 1, 3

[12] James Hays and Alexei A Efros. Large-scale image geolo-

calization. In Multimodal location estimation of videos and

images , pages 41–62. Springer, 2015. 1, 3

[13] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross

Girshick. Momentum contrast for unsupervised visual rep-

resentation learning. In Proceedings of the IEEE/CVF Con-

ference on Computer Vision and Pattern Recognition , pages

9729–9738, 2020. 1, 2, 4, 5, 7, 8

[14] Hayate Iso, Shoko Wakamiya, and Eiji Aramaki. Density

estimation for geolocation via convolutional mixture density

network. arXiv preprint arXiv:1705.02750 , 2017. 3

[15] Neal Jean, Sherrie Wang, Anshul Samar, George Azzari,

David Lobell, and Stefano Ermon. Tile2vec: Unsupervised

representation learning for spatially distributed data. In Pro-

ceedings of the AAAI Conference on Artiﬁcial Intelligence ,

volume 33, pages 3967–3974, 2019. 2

[16] Darius Lam, Richard Kuzma, Kevin McGee, Samuel Doo-

ley, Michael Laielli, Matthew Klaric, Yaroslav Bulatov, and

9

Brendan McCord. xview: Objects in context in overhead

imagery. arXiv preprint arXiv:1802.07856 , 2018. 1, 7

[17] Yansheng Li, Chao Tao, Yihua Tan, Ke Shang, and Jinwen

Tian. Unsupervised multilayer feature learning for satellite

image scene classiﬁcation. IEEE Geoscience and Remote

Sensing Letters , 13(2):157–161, 2016. 2

[18] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and

Piotr Doll ´ar. Focal loss for dense object detection. In Pro-

ceedings of the IEEE international conference on computer

vision , pages 2980–2988, 2017. 8

[19] Xiaoqiang Lu, Xiangtao Zheng, and Yuan Yuan. Remote

sensing scene classiﬁcation by unsupervised representation

learning. IEEE Transactions on Geoscience and Remote

Sensing , 55(9):5148–5157, 2017. 2

[20] Oisin Mac Aodha, Elijah Cole, and Pietro Perona. Presence-

only geographical priors for ﬁne-grained image classiﬁca-

tion. In Proceedings of the IEEE International Conference

on Computer Vision , pages 9596–9606, 2019. 3

[21] Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan,

Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe,

and Laurens van der Maaten. Exploring the limits of weakly

supervised pretraining. In Proceedings of the European Con-

ference on Computer Vision (ECCV) , pages 181–196, 2018.

1

[22] Ishan Misra and Laurens van der Maaten. Self-supervised

learning of pretext-invariant representations. In Proceedings

of the IEEE/CVF Conference on Computer Vision and Pat-

tern Recognition , pages 6707–6717, 2020. 2

[23] Lichao Mou, Pedram Ghamisi, and Xiao Xiang Zhu. Un-

supervised spectral–spatial feature learning via deep resid-

ual conv–deconv network for hyperspectral image classiﬁca-

tion. IEEE Transactions on Geoscience and Remote Sensing ,

56(1):391–406, 2017. 2

[24] Eric Muller-Budack, Kader Pustu-Iren, and Ralph Ewerth.

Geolocation estimation of photos using a hierarchical model

and scene classiﬁcation. In Proceedings of the European

Conference on Computer Vision (ECCV) , pages 563–579,

2018. 3

[25] T Nathan Mundhenk, Goran Konjevod, Wesam A Sakla,

and Koﬁ Boakye. A large contextual dataset for classiﬁca-

tion, detection and counting of cars with deep learning. In

European Conference on Computer Vision , pages 785–800.

Springer, 2016. 2

[26] Mehdi Noroozi and Paolo Favaro. Unsupervised learning

of visual representations by solving jigsaw puzzles. In

European Conference on Computer Vision , pages 69–84.

Springer, 2016. 2

[27] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Repre-

sentation learning with contrastive predictive coding. arXiv

preprint arXiv:1807.03748 , 2018. 4

[28] Deepak Pathak, Ross Girshick, Piotr Doll ´ar, Trevor Dar-

rell, and Bharath Hariharan. Learning features by watch-

ing objects move. In Proceedings of the IEEE Conference

on Computer Vision and Pattern Recognition , pages 2701–

2710, 2017. 2

[29] Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor

Darrell, and Alexei A Efros. Context encoders: Featurelearning by inpainting. In Proceedings of the IEEE con-

ference on computer vision and pattern recognition , pages

2536–2544, 2016. 2

[30] Adriana Romero, Carlo Gatta, and Gustau Camps-Valls. Un-

supervised deep feature extraction for remote sensing image

classiﬁcation. IEEE Transactions on Geoscience and Remote

Sensing , 54(3):1349–1362, 2015. 2

[31] Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent,

Neal Jean, Marshall Burke, David Lobell, and Stefano Er-

mon. Predicting economic development using geolocated

wikipedia articles. In Proceedings of the 25th ACM SIGKDD

International Conference on Knowledge Discovery & Data

Mining , pages 2698–2706, 2019. 1

[32] Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao

Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han

Zhang, and Colin Raffel. Fixmatch: Simplifying semi-

supervised learning with consistency and conﬁdence. arXiv

preprint arXiv:2001.07685 , 2020. 2

[33] Kevin Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, and

Lubomir Bourdev. Improving image classiﬁcation with lo-

cation context. In Proceedings of the IEEE international