Datasets:
gremlin97
/
RemoteSensingCorpus

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
820
[24] Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt,
and Thomas Dandres. Quantifying the carbon emissions of
machine learning. arXiv preprint arXiv:1910.09700 , 2019.
4
[25] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei,
Zheng Zhang, Stephen Lin, and Baining Guo. Swin trans-
former: Hierarchical vision transformer using shifted win-
dows. CoRR , abs/2103.14030, 2021. 2, 3, 6, 7, 8
[26] Zihan Liu, Genta Indra Winata, and Pascale Fung. Contin-
ual mixed-language pre-training for extremely low-resource
neural machine translation. CoRR , abs/2105.03953, 2021. 2,
3
[27] Yang Long, Yiping Gong, Zhifeng Xiao, and Qing Liu. Ac-
curate object localization in remote sensing images based on
convolutional neural networks. IEEE Transactions on Geo-
science and Remote Sensing , 55(5):2486–2498, 2017. 4
[28] Oscar Ma ˜nas, Alexandre Lacoste, Xavier Gir ´o-i-Nieto,
David V ´azquez, and Pau Rodr ´ıguez. Seasonal contrast: Un-
supervised pre-training from uncurated remote sensing data.
CoRR , abs/2103.16607, 2021. 1, 2, 3, 4, 6, 7, 8, 9
[29] Maxim Neumann, Andr ´e Susano Pinto, Xiaohua Zhai, and
Neil Houlsby. In-domain representation learning for remote
sensing. CoRR , abs/1911.06721, 2019. 1, 2, 3, 7
[30] Keiller Nogueira, Ot ´avio Augusto Bizetto Penatti, and Je-
fersson Alex dos Santos. Towards better exploiting convo-
lutional neural networks for remote sensing scene classifica-
tion. CoRR , abs/1602.01517, 2016. 1
[31] U.S. Department of Agriculture. National agriculture im-
agery program (NAIP). 4
[32] Deepak Pathak, Philipp Kr ¨ahenb ¨uhl, Jeff Donahue, Trevor
Darrell, and Alexei A. Efros. Context encoders: Feature
learning by inpainting. CoRR , abs/1604.07379, 2016. 3
[33] Xiaoman Qi, Panpan Zhu, Yuebin Wang, Liqiang Zhang,
Junhuan Peng, Mengfan Wu, Jialong Chen, Xudong Zhao,
Ning Zang, and P. Takis Mathiopoulos. Mlrsnet: A multi-
label high spatial resolution remote sensing dataset for se-
mantic scene understanding. ISPRS Journal of Photogram-
metry and Remote Sensing , 169:337–350, 2020. 4
[34] Colorado J Reed, Xiangyu Yue, Ani Nrusimha, Sayna
Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shang-
hang Zhang, Devin Guillory, Sean Metzger, et al. Self-
supervised pretraining improves self-supervised pretraining.
InProceedings of the IEEE/CVF Winter Conference on Ap-
plications of Computer Vision , pages 2584–2594, 2022. 3
[35] Franz Rottensteiner, Gunho Sohn, Jaewook Jung, Markus
Gerke, Caroline Baillard, Sebastien Benitez, and Uwe Bre-
itkopf. The isprs benchmark on urban object classification
and 3d building reconstruction. ISPRS Annals of the Pho-
togrammetry, Remote Sensing and Spatial Information Sci-
ences I-3 (2012), Nr. 1 , 1(1):293–298, 2012. 7
[36] Gencer Sumbul, Marcela Charfuelan, Beg ¨um Demir, and
V olker Markl. Bigearthnet: A large-scale benchmark archive
for remote sensing image understanding. In IGARSS 2019-
2019 IEEE International Geoscience and Remote Sensing
Symposium , pages 5901–5904. IEEE, 2019. 7
[37] Stefano Vincenzi, Angelo Porrello, Pietro Buzzega, Marco
Cipriano, Pietro Fronte, Roberto Cuccu, Carla Ippoliti, An-
namaria Conte, and Simone Calderara. The color out of
space: learning self-supervised representations for earth ob-
servation imagery. CoRR , abs/2006.12119, 2020. 3
[38] 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. 2
[39] Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and
Jian Sun. Unified perceptual parsing for scene understand-
ing. In European Conference on Computer Vision . Springer,
2018. 7
[40] Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han
Hu, and Yue Cao. Revealing the dark secrets of masked im-
age modeling. arXiv preprint arXiv:2205.13543 , 2022. 3
[41] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jian-
min Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim:
A simple framework for masked image modeling. CoRR ,
abs/2111.09886, 2021. 3, 5, 6[42] 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. 7
[43] Chenxiao Zhang, Peng Yue, Deodato Tapete, Liangcun
Jiang, Boyi Shangguan, Li Huang, and Guangchao Liu.
A deeply supervised image fusion network for change de-
tection in high resolution bi-temporal remote sensing im-
ages. ISPRS Journal of Photogrammetry and Remote Sens-
ing, 166:183–200, 2020. 6, 7
[44] Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang
Xie, Alan L. Yuille, and Tao Kong. ibot: Image BERT pre-
training with online tokenizer. CoRR , abs/2111.07832, 2021.
3
[45] Weixun Zhou, Shawn Newsam, Congmin Li, and Zhenfeng
Shao. Patternnet: A benchmark dataset for performance
evaluation of remote sensing image retrieval. ISPRS Jour-
nal of Photogrammetry and Remote Sensing , 145:197–209,
2018. Deep Learning RS Data. 4
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1
RSVQA: Visual Question Answering for Remote
Sensing Data
Sylvain Lobry, Member, IEEE, Diego Marcos, Jesse Murray, Devis Tuia, Senior Member, IEEE
This is the pre-acceptance version, to read the final version pub-
lished in the journal IEEE Transactions on Geoscience and Remote
Sensing, please go to: https://doi.org/10.1109/TGRS.2020.2988782.
Abstract —This paper introduces the task of visual question