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Erratum | Open Access

Multi-level representation learning via ConvNeXt-based network for unaligned cross-view matching

Fangli Guana , Nan Zhaoa , Zhixiang Fangb, Ling Jiangc,d,e, Jianhui Zhanga , Yue Yuf ( ), Haosheng Huangg 
School of Computer Science, Hangzhou Dianzi University, Hangzhou, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou University, Chuzhou, China
Anhui Engineering Laboratory of Geo-information Smart Sensing and Services, Chuzhou, China
Anhui Center for Collaborative Innovation in Geographical Information Integration and Application, Chuzhou, China
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
Department of Geography, Ghent University, Ghent, Belgium
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The online version of the original article can be found at:
https://doi.org/10.1080/10095020.2024.2439385

Keywords

Cross-view matchingConvNeXtsatellite viewdrone viewmultilevel feature

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Geo-Spatial Information Science
Volume 28 Issue 5,
October 2025
Pages i-ii
DOI: 10.1080/10095020.2025.2577043

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Cite this article:
Guan F, Zhao N, Fang Z, et al. Multi-level representation learning via ConvNeXt-based network for unaligned cross-view matching. Geo-Spatial Information Science, 2025, 28(5): i-ii. https://doi.org/10.1080/10095020.2025.2577043

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Published: 01 October 2025
© 2025 Wuhan University.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.