ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1612446
This article is part of the Research TopicNew Artificial Intelligence Methods for Remote Sensing Monitoring of Coastal Cities and EnvironmentView all 4 articles
Research on intelligent classification of coastal land cover by integrating remote sensing images and deep learning
Provisionally accepted
- Zhongyuan University of Science and Technology, Zhengzhou, China
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The intelligent classification of coastal land cover is an essential task for effective coastal management and environmental monitoring. With the increasing availability of remote sensing images, leveraging advanced machine learning methods, such as deep learning, has become pivotal in improving classification accuracy. Traditional methods, like pixel-based and objectoriented classification, often struggle with high complexity and inaccurate results due to limitations in handling spatial relationships and spectral data. This research addresses these shortcomings by integrating deep learning models, particularly convolutional neural networks (CNNs) and spatially dependent learning techniques, to develop a robust classification model for coastal land cover using remote sensing data. Our approach incorporates multi-scale spatial analysis and graph-based models to capture spatial dependencies and contextual features across various coastal environments. The model also emphasizes spatial continuity, enabling a more realistic representation of complex land cover types such as wetlands, beaches, mangroves, and urbanized coastlines. Compared to traditional machine learning baselines, our method achieves improvements of +10-15% in overall accuracy and +12-14% in macro F1-score, highlighting the practical advantages of deep learning in capturing spatial structures and heterogeneity. The proposed method achieves classification accuracies of 95.83% on the Gaofen Image dataset and 94.34% on the LandCoverNet dataset, with F1 scores of 91.65% and 92.42% respectively. These results demonstrate significant improvements in both precision and robustness when applied to high-resolution coastal remote sensing images. This work highlights the potential of deep learning in enhancing remote sensing analysis for environmental and urban applications, paving the way for intelligent decision-making in dynamic coastal zones.
Keywords: coastal land cover, remote sensing, deep learning, spatial analysis, Classification
Received: 15 Apr 2025; Accepted: 30 Jun 2025.
Copyright: © 2025 Lin, Hei, Wang and Zhang. 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) or licensor 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: Xinhao Lin, Zhongyuan University of Science and Technology, Zhengzhou, China
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