gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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Puya Vahabi, Alberto Todeschini, and Colorado J. Reed.
Snowpack Estimation in Key Mountainous Water Basins from
Openly-Available, Multimodal Data Sources, Aug. 2022.
[46] Maxim Neumann, Andr ´e Susano Pinto, Xiaohua Zhai, and
Neil Houlsby. In-domain representation learning for remote
sensing. ArXiv , abs/1911.06721, 2019.[47] Fernando Paolo, Tsu-ting Tim Lin, Ritwik Gupta, Bryce
Goodman, Nirav Patel, Daniel Kuster, David Kroodsma, and
Jared Dunnmon. xView3-SAR: Detecting Dark Fishing
Activity Using Synthetic Aperture Radar Imagery. In Thirty-
Sixth Conference on Neural Information Processing Systems
Datasets and Benchmarks Track , Oct. 2022.
[48] Xiaoman Qi, Panpan Zhu, Wang Yuebin, Liqiang Zhang, Jun-
huan Peng, Mengfan Wu, Jialong Chen, Xudong Zhao, Ning
Zang, and P. Takis Mathiopoulos. MLRSNet: A multi-label
high spatial resolution remote sensing dataset for semantic
scene understanding. ISPRS Journal of Photogrammetry and
Remote Sensing , 169:337–350, Nov. 2020.
[49] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya
Sutskever, et al. Improving language understanding by gener-
ative pre-training. Preprint , 2018.
[50] A. Rosenfeld and M. Thurston. Edge and Curve Detection
for Visual Scene Analysis. IEEE Transactions on Computers ,
C-20(5):562–569, May 1971.
[51] Jacob Shermeyer and Adam Van Etten. The effects of super-
resolution on object detection performance in satellite im-
agery. In Proceedings of the IEEE/CVF Conference on Com-
puter Vision and Pattern Recognition Workshops , pages 0–0,
2019.
[52] Beth Tellman, Jonathan A. Sullivan, and Colin S. Doyle.
Global Flood Observation with Multiple Satellites. In Global
Drought and Flood , chapter 5, pages 99–121. American
Geophysical Union (AGU), 2021.
[53] Adam Van Etten, Dave Lindenbaum, and Todd M. Bacastow.
SpaceNet: A Remote Sensing Dataset and Challenge Series,
July 2019.
[54] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko-
reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia
Polosukhin. Attention is All you Need. In Advances in
Neural Information Processing Systems , volume 30. Curran
Associates, Inc., 2017.
[55] Qi Wang, Shaoteng Liu, Jocelyn Chanussot, and Xuelong
Li. Scene Classification With Recurrent Attention of VHR
Remote Sensing Images. IEEE Transactions on Geoscience
and Remote Sensing , 57(2):1155–1167, Feb. 2019.
[56] Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander
Sorkine-Hornung, Olga Sorkine-Hornung, and Christopher
Schroers. A Fully Progressive Approach to Single-Image
Super-Resolution. In 2018 IEEE/CVF Conference on Com-
puter Vision and Pattern Recognition Workshops (CVPRW) ,
pages 977–97709, Salt Lake City, UT, USA, June 2018.
IEEE.
[57] Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin.
Unsupervised Feature Learning via Non-Parametric Instance
Discrimination. In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition , pages 3733–3742,
2018.
[58] Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian
Sun. Unified Perceptual Parsing for Scene Understanding. In
Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and
Yair Weiss, editors, Computer Vision – ECCV 2018 , volume
11209, pages 432–448. Springer International Publishing,
Cham, 2018.
[59] Xin-Yi Tong, Gui-Song Xia, Qikai Lu, Huangfeng Shen,
Shengyang Li, Shucheng You, Liangpei Zhang. Land-cover
classification with high-resolution remote sensing images us-
ing transferable deep models. Remote Sensing of Environment,
doi: 10.1016/j.rse.2019.111322 , 2020.
[60] Zhitong Xiong, Fahong Zhang, Yi Wang, Yilei Shi, and
Xiao Xiang Zhu. EarthNets: Empowering AI in Earth Obser-
vation, Oct. 2022.
[61] Xingqian Xu, Zhangyang Wang, and Humphrey Shi. Ul-
trasr: Spatial encoding is a missing key for implicit im-
age function-based arbitrary-scale super-resolution. arXiv
preprint arXiv:2103.12716 , 2021.
[62] Shengye Yan, Xinxing Xu, Dong Xu, Stephen Lin, and Xue-
long Li. Beyond Spatial Pyramids: A New Feature Extraction
Framework with Dense Spatial Sampling for Image Classifi-
cation. In European Conference on Computer Vision 2012 ,
pages 473–487, Aug. 2012.
[63] Shengye Yan, Xinxing Xu, Dong Xu, Stephen Lin, and Xue-
long Li. Image Classification With Densely Sampled Image
Windows and Generalized Adaptive Multiple Kernel Learn-
ing. IEEE Transactions on Cybernetics , 45(3):381–390, Mar.
2015.
[64] Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang,
Jing-Hao Xue, and Qingmin Liao. Deep learning for single
image super-resolution: A brief review. IEEE Transactions
on Multimedia , 21(12):3106–3121, 2019.
[65] 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 , GIS ’10, pages 270–279,
New York, NY , USA, Nov. 2010. Association for Computing
Machinery.
[66] Matthew D Zeiler and Rob Fergus. Visualizing and under-
standing convolutional networks. In European conference on
computer vision , pages 818–833. Springer, 2014.
[67] Renrui Zhang, Ziyu Guo, Rongyao Fang, Bin Zhao, Dong
Wang, Yu Qiao, Hongsheng Li, and Peng Gao. Point-
M2AE: Multi-scale Masked Autoencoders for Hierarchical

Puya Vahabi, Alberto Todeschini, and Colorado J. Reed.

Snowpack Estimation in Key Mountainous Water Basins from

Openly-Available, Multimodal Data Sources, Aug. 2022.

[46] Maxim Neumann, Andr ´e Susano Pinto, Xiaohua Zhai, and

Neil Houlsby. In-domain representation learning for remote

sensing. ArXiv , abs/1911.06721, 2019.[47] Fernando Paolo, Tsu-ting Tim Lin, Ritwik Gupta, Bryce

Goodman, Nirav Patel, Daniel Kuster, David Kroodsma, and

Jared Dunnmon. xView3-SAR: Detecting Dark Fishing

Activity Using Synthetic Aperture Radar Imagery. In Thirty-

Sixth Conference on Neural Information Processing Systems

Datasets and Benchmarks Track , Oct. 2022.

[48] Xiaoman Qi, Panpan Zhu, Wang Yuebin, Liqiang Zhang, Jun-

huan Peng, Mengfan Wu, Jialong Chen, Xudong Zhao, Ning

Zang, and P. Takis Mathiopoulos. MLRSNet: A multi-label

high spatial resolution remote sensing dataset for semantic

scene understanding. ISPRS Journal of Photogrammetry and

Remote Sensing , 169:337–350, Nov. 2020.

[49] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya

Sutskever, et al. Improving language understanding by gener-

ative pre-training. Preprint , 2018.

[50] A. Rosenfeld and M. Thurston. Edge and Curve Detection

for Visual Scene Analysis. IEEE Transactions on Computers ,

C-20(5):562–569, May 1971.

[51] Jacob Shermeyer and Adam Van Etten. The effects of super-

resolution on object detection performance in satellite im-

agery. In Proceedings of the IEEE/CVF Conference on Com-

puter Vision and Pattern Recognition Workshops , pages 0–0,

2019.

[52] Beth Tellman, Jonathan A. Sullivan, and Colin S. Doyle.

Global Flood Observation with Multiple Satellites. In Global

Drought and Flood , chapter 5, pages 99–121. American

Geophysical Union (AGU), 2021.

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

SpaceNet: A Remote Sensing Dataset and Challenge Series,

July 2019.

[54] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko-

reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia

Polosukhin. Attention is All you Need. In Advances in

Neural Information Processing Systems , volume 30. Curran

Associates, Inc., 2017.

[55] Qi Wang, Shaoteng Liu, Jocelyn Chanussot, and Xuelong

Li. Scene Classification With Recurrent Attention of VHR

Remote Sensing Images. IEEE Transactions on Geoscience

and Remote Sensing , 57(2):1155–1167, Feb. 2019.

[56] Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander

Sorkine-Hornung, Olga Sorkine-Hornung, and Christopher

Schroers. A Fully Progressive Approach to Single-Image

Super-Resolution. In 2018 IEEE/CVF Conference on Com-

puter Vision and Pattern Recognition Workshops (CVPRW) ,

pages 977–97709, Salt Lake City, UT, USA, June 2018.

IEEE.

[57] Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin.

Unsupervised Feature Learning via Non-Parametric Instance

Discrimination. In Proceedings of the IEEE Conference on

Computer Vision and Pattern Recognition , pages 3733–3742,

2018.

[58] Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian

Sun. Unified Perceptual Parsing for Scene Understanding. In

Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and

Yair Weiss, editors, Computer Vision – ECCV 2018 , volume

11209, pages 432–448. Springer International Publishing,

Cham, 2018.

[59] Xin-Yi Tong, Gui-Song Xia, Qikai Lu, Huangfeng Shen,

Shengyang Li, Shucheng You, Liangpei Zhang. Land-cover

classification with high-resolution remote sensing images us-

ing transferable deep models. Remote Sensing of Environment,

doi: 10.1016/j.rse.2019.111322 , 2020.

[60] Zhitong Xiong, Fahong Zhang, Yi Wang, Yilei Shi, and

Xiao Xiang Zhu. EarthNets: Empowering AI in Earth Obser-

vation, Oct. 2022.

[61] Xingqian Xu, Zhangyang Wang, and Humphrey Shi. Ul-

trasr: Spatial encoding is a missing key for implicit im-

age function-based arbitrary-scale super-resolution. arXiv

preprint arXiv:2103.12716 , 2021.

[62] Shengye Yan, Xinxing Xu, Dong Xu, Stephen Lin, and Xue-

long Li. Beyond Spatial Pyramids: A New Feature Extraction

Framework with Dense Spatial Sampling for Image Classifi-

cation. In European Conference on Computer Vision 2012 ,

pages 473–487, Aug. 2012.

[63] Shengye Yan, Xinxing Xu, Dong Xu, Stephen Lin, and Xue-

long Li. Image Classification With Densely Sampled Image

Windows and Generalized Adaptive Multiple Kernel Learn-

ing. IEEE Transactions on Cybernetics , 45(3):381–390, Mar.

2015.

[64] Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang,

Jing-Hao Xue, and Qingmin Liao. Deep learning for single

image super-resolution: A brief review. IEEE Transactions

on Multimedia , 21(12):3106–3121, 2019.

[65] 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 , GIS ’10, pages 270–279,

New York, NY , USA, Nov. 2010. Association for Computing

Machinery.

[66] Matthew D Zeiler and Rob Fergus. Visualizing and under-

standing convolutional networks. In European conference on

computer vision , pages 818–833. Springer, 2014.

[67] Renrui Zhang, Ziyu Guo, Rongyao Fang, Bin Zhao, Dong

Wang, Yu Qiao, Hongsheng Li, and Peng Gao. Point-

M2AE: Multi-scale Masked Autoencoders for Hierarchical