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LUAI Challenge 2021 on Learning to Understand Aerial Images

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

in Aerial patrol / Horizontal orientation / Image segmentation / Learning / Object recognition / Semantic segmentation / Semantics

2021

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LUAI Challenge 2021 on Learning to Understand Aerial Images

in Aerial patrol / Horizontal orientation / Image segmentation / Learning / Object recognition / Semantic segmentation / Semantics

2021

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LUAI Challenge 2021 on Learning to Understand Aerial Images

in Aerial patrol / Horizontal orientation / Image segmentation / Learning / Object recognition / Semantic segmentation / Semantics

2021

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Paper

LUAI Challenge 2021 on Learning to Understand Aerial Images

Li, Shengyang,

Wang, Lingqi,

Tan, Wenming,

Qian, Ming,

Belongie, Serge,

Li, Wei,

Gui-Song, Xia,

Datcu, Mihai,

Zuo, Yi,

Chao-hui, Yu,

Bu, Yingjia,

Ma, Tianzhi,

Hu, Kaixuan,

Zhou, Qiang,

Hui, Yiming,

Wang, Jiahao,

Liu, Fang,

Dong, Zhuojun,

Liu, Shang,

Zhang, Liangpei,

Bai, Xiang,

Chang, Meng,

Bao, Qianyue,

Zhang, Zixiao,

Xue, Nan,

Yang, Zhe,

Ding, Jian,

Yang, Michael Ying,

Luo, Jiebo,

Zhao, Jiaxuan,

Jiao, Licheng,

Han, Jiaming,

Pelillo, Marcello,

Zhang, Jie,

Zi-han, Gao,

Wang, Hao

2021

Overview

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.

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Publisher

Cornell University Library, arXiv.org

Subject

Aerial patrol

/ Horizontal orientation

/ Image segmentation

/ Learning

/ Object recognition

/ Semantic segmentation

/ Semantics