CORRECTION article

Front. Environ. Sci., 27 May 2025

Sec. Environmental Informatics and Remote Sensing

Volume 13 - 2025 | https://doi.org/10.3389/fenvs.2025.1621083

Corrigendum: Label semantics and image features aware remote sensing sample retrieval from multi-source datasets for AI-enabled remote sensing monitoring

This article is a correction to:

  1. Label Semantics and Image Features aware Remote Sensing Sample Retrieval from Multi-Source Datasets for AI-enabled Remote Sensing Monitoring

    1. Read original article
XingTao Ren,XingTao Ren1,2Yan Ma Yan Ma1*YiXin Zhou,YiXin Zhou1,2
  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • 2School of Electronic, Electrical and CommunicationEngineering, University of Chinese Academy of Sciences, Beijing, China

A Corrigendum on:
Label semantics and image features aware remote sensing sample retrieval from multi-source datasets for AI-enabled remote sensing monitoring

by Ren X, Ma Y and Zhou Y (2025). Front. Environ. Sci. 13:1580797. doi: 10.3389/fenvs.2025.1580797

In the published article, there was an error in the Funding statement. The authors inadvertently omitted a funding source: The Science and Disruptive Technology Project of Aerospace Information Research Institute, Chinese Academy of Sciences (Grant No. E2Z206010F). The correct Funding statement appears below.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by the National Natural Science Foundation of China: 42071413, and the Science and disruptive technology project of Aerospace Information Research Institute of Chinese Academy of Sciences, grant number E2Z206010F.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: remote sensing sample datasets, remote sensing sample retrieval, remote sensing sample database, AI-enabled remote sensing application, remote sensing imagery, deep learning, label category system, remote sensing big data

Citation: Ren X, Ma Y and Zhou Y (2025) Corrigendum: Label semantics and image features aware remote sensing sample retrieval from multi-source datasets for AI-enabled remote sensing monitoring. Front. Environ. Sci. 13:1621083. doi: 10.3389/fenvs.2025.1621083

Received: 30 April 2025; Accepted: 01 May 2025;
Published: 27 May 2025.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2025 Ren, Ma and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yan Ma, bWF5YW5AYWlyY2FzLmFjLmNu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.