LUAI Challenge 2021 on Learning to Understand Aerial Images

Gui Song Xia; Jian Ding; Ming Qian; Nan Xue; Jiaming Han; Xiang Bai; Michael Ying Yang; Shengyang Li; Serge Belongie; Jiebo Luo; Mihai Datcu; Marcello Pelillo; Liangpei Zhang; Qiang Zhou; Chao Hui Yu; Kaixuan Hu; Yingjia Bu; Wenming Tan; Zhe Yang; Wei Li; Shang Liu; Jiaxuan Zhao; Tianzhi Ma; Zi Han Gao; Lingqi Wang; Yi Zuo; Licheng Jiao; Chang Meng; Hao Wang; Jiahao Wang; Yiming Hui; Zhuojun Dong; Jie Zhang; Qianyue Bao; Zixiao Zhang; Fang Liu

doi:10.1109/ICCVW54120.2021.00090

LUAI Challenge 2021 on Learning to Understand Aerial Images

Gui Song Xia, Jian Ding, Ming Qian, Nan Xue, Jiaming Han, Xiang Bai, Michael Ying Yang, Shengyang Li, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, Liangpei Zhang, Qiang Zhou, Chao Hui Yu, Kaixuan Hu, Yingjia Bu, Wenming Tan, Zhe Yang, Wei LiShang Liu, Jiaxuan Zhao, Tianzhi Ma, Zi Han Gao, Lingqi Wang, Yi Zuo, Licheng Jiao, Chang Meng, Hao Wang, Jiahao Wang, Yiming Hui, Zhuojun Dong, Jie Zhang, Qianyue Bao, Zixiao Zhang, Fang Liu

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

2 Citations (SciVal)

Abstract

This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV'2021, which focuses on object detection and semantic segmentation in aerial images. Using DOTA-v2.0 [7] and GID-15 [35] datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images. This challenge received a total of 146 registrations on the three tasks. Through the challenge, we hope to draw attention from a wide range of communities and call for more efforts on the problems of learning to understand aerial images.

Original language	English
Title of host publication	Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Place of Publication	U. S. A.
Publisher	IEEE
Pages	762-768
Number of pages	7
ISBN (Electronic)	9781665401913
DOIs	https://doi.org/10.1109/ICCVW54120.2021.00090
Publication status	Published - 11 Oct 2021
Event	18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada Duration: 11 Oct 2021 → 17 Oct 2021

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	2021-October
ISSN (Print)	1550-5499

Conference

Conference	18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Country/Territory	Canada
City	Virtual, Online
Period	11/10/21 → 17/10/21

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCVW54120.2021.00090

https://openaccess.thecvf.com/content/ICCV2021W/LUAI/html/Xia_LUAI_Challenge_2021_on_Learning_To_Understand_Aerial_Images_ICCVW_2021_paper.html

Cite this

Xia, G. S., Ding, J., Qian, M., Xue, N., Han, J., Bai, X., Yang, M. Y., Li, S., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L., Zhou, Q., Yu, C. H., Hu, K., Bu, Y., Tan, W., Yang, Z., ... Liu, F. (2021). LUAI Challenge 2021 on Learning to Understand Aerial Images. In Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 (pp. 762-768). (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2021-October). IEEE. https://doi.org/10.1109/ICCVW54120.2021.00090

LUAI Challenge 2021 on Learning to Understand Aerial Images. / Xia, Gui Song; Ding, Jian; Qian, Ming et al.
Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021. U. S. A.: IEEE, 2021. p. 762-768 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2021-October).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Xia, GS, Ding, J, Qian, M, Xue, N, Han, J, Bai, X, Yang, MY, Li, S, Belongie, S, Luo, J, Datcu, M, Pelillo, M, Zhang, L, Zhou, Q, Yu, CH, Hu, K, Bu, Y, Tan, W, Yang, Z, Li, W, Liu, S, Zhao, J, Ma, T, Gao, ZH, Wang, L, Zuo, Y, Jiao, L, Meng, C, Wang, H, Wang, J, Hui, Y, Dong, Z, Zhang, J, Bao, Q, Zhang, Z & Liu, F 2021, LUAI Challenge 2021 on Learning to Understand Aerial Images. in Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021. Proceedings of the IEEE International Conference on Computer Vision, vol. 2021-October, IEEE, U. S. A., pp. 762-768, 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Virtual, Online, Canada, 11/10/21. https://doi.org/10.1109/ICCVW54120.2021.00090

@inproceedings{9205fceefed646849ac8079f1e39422a,

title = "LUAI Challenge 2021 on Learning to Understand Aerial Images",

abstract = "This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV'2021, which focuses on object detection and semantic segmentation in aerial images. Using DOTA-v2.0 [7] and GID-15 [35] datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images. This challenge received a total of 146 registrations on the three tasks. Through the challenge, we hope to draw attention from a wide range of communities and call for more efforts on the problems of learning to understand aerial images.",

author = "Xia, {Gui Song} and Jian Ding and Ming Qian and Nan Xue and Jiaming Han and Xiang Bai and Yang, {Michael Ying} and Shengyang Li and Serge Belongie and Jiebo Luo and Mihai Datcu and Marcello Pelillo and Liangpei Zhang and Qiang Zhou and Yu, {Chao Hui} and Kaixuan Hu and Yingjia Bu and Wenming Tan and Zhe Yang and Wei Li and Shang Liu and Jiaxuan Zhao and Tianzhi Ma and Gao, {Zi Han} and Lingqi Wang and Yi Zuo and Licheng Jiao and Chang Meng and Hao Wang and Jiahao Wang and Yiming Hui and Zhuojun Dong and Jie Zhang and Qianyue Bao and Zixiao Zhang and Fang Liu",

year = "2021",

month = oct,

day = "11",

doi = "10.1109/ICCVW54120.2021.00090",

language = "English",

series = "Proceedings of the IEEE International Conference on Computer Vision",

publisher = "IEEE",

pages = "762--768",

booktitle = "Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021",

address = "USA United States",

note = "18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 ; Conference date: 11-10-2021 Through 17-10-2021",

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T1 - LUAI Challenge 2021 on Learning to Understand Aerial Images

AU - Xia, Gui Song

AU - Ding, Jian

AU - Qian, Ming

AU - Xue, Nan

AU - Han, Jiaming

AU - Bai, Xiang

AU - Yang, Michael Ying

AU - Li, Shengyang

AU - Belongie, Serge

AU - Luo, Jiebo

AU - Datcu, Mihai

AU - Pelillo, Marcello

AU - Zhang, Liangpei

AU - Zhou, Qiang

AU - Yu, Chao Hui

AU - Hu, Kaixuan

AU - Bu, Yingjia

AU - Tan, Wenming

AU - Yang, Zhe

AU - Li, Wei

AU - Liu, Shang

AU - Zhao, Jiaxuan

AU - Ma, Tianzhi

AU - Gao, Zi Han

AU - Wang, Lingqi

AU - Zuo, Yi

AU - Jiao, Licheng

AU - Meng, Chang

AU - Wang, Hao

AU - Wang, Jiahao

AU - Hui, Yiming

AU - Dong, Zhuojun

AU - Zhang, Jie

AU - Bao, Qianyue

AU - Zhang, Zixiao

AU - Liu, Fang

PY - 2021/10/11

Y1 - 2021/10/11

N2 - This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV'2021, which focuses on object detection and semantic segmentation in aerial images. Using DOTA-v2.0 [7] and GID-15 [35] datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images. This challenge received a total of 146 registrations on the three tasks. Through the challenge, we hope to draw attention from a wide range of communities and call for more efforts on the problems of learning to understand aerial images.

AB - This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV'2021, which focuses on object detection and semantic segmentation in aerial images. Using DOTA-v2.0 [7] and GID-15 [35] datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images. This challenge received a total of 146 registrations on the three tasks. Through the challenge, we hope to draw attention from a wide range of communities and call for more efforts on the problems of learning to understand aerial images.

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ER -

LUAI Challenge 2021 on Learning to Understand Aerial Images

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