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
TL;DR
The LUAI 2021 challenge evaluated object detection and semantic segmentation methods on aerial images using DOTA-v2.0 and GID-15 datasets, encouraging advancements in understanding aerial imagery.
Contribution
This report presents the LUAI 2021 challenge with new tasks and datasets to benchmark aerial image understanding methods.
Findings
146 registrations across tasks
Focus on oriented and horizontal object detection
Semantic segmentation of common categories
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 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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Domain Adaptation and Few-Shot Learning