LUAI Challenge 2021 on Learning to Understand Aerial Images

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 and GID-15 datasets, this challenge proposes three tasks for oriented object detection, ho...

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Veröffentlicht in:arXiv.org 2021-09
Hauptverfasser: Gui-Song, Xia, Ding, Jian, Qian, Ming, Xue, Nan, Han, Jiaming, Bai, Xiang, Yang, Michael Ying, Li, Shengyang, Belongie, Serge, Luo, Jiebo, Datcu, Mihai, Pelillo, Marcello, Zhang, Liangpei, Zhou, Qiang, Chao-hui, Yu, Hu, Kaixuan, Bu, Yingjia, Tan, Wenming, Yang, Zhe, Li, Wei, Liu, Shang, Zhao, Jiaxuan, Ma, Tianzhi, Zi-han, Gao, Wang, Lingqi, Zuo, Yi, Jiao, Licheng, Chang, Meng, Wang, Hao, Wang, Jiahao, Hui, Yiming, Dong, Zhuojun, Zhang, Jie, Bao, Qianyue, Zhang, Zixiao, Liu, Fang
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Sprache:eng
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Zusammenfassung: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 and GID-15 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.
ISSN:2331-8422