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Biomimetics
Volume 10
Issue 11
10.3390/biomimetics10110732
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Water Body Identification from Satellite Images Using a Hybrid Evolutionary Algorithm-Optimized U-Net Framework

by
Yue Yuan
1,2,
Peiyang Wei
1,3,4,*,
Zhixiang Qi
3,
Xun Deng
3,*,
Ji Zhang
5,
Jianhong Gan
3,4,
Tinghui Chen
3,4 and
Zhibin Li
3,4,6
1
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
3
School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
4
Dazhou Key Laboratory of Government Data Security, Sichuan University of Arts and Science, Dazhou 635000, China
5
Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
6
Xinjiang Technical Institute of Physics &Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
*
Authors to whom correspondence should be addressed.
Biomimetics 2025, 10(11), 732; https://doi.org/10.3390/biomimetics10110732 (registering DOI)
Submission received: 9 September 2025 / Revised: 22 October 2025 / Accepted: 29 October 2025 / Published: 1 November 2025
(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)
Review Reports Versions Notes

Abstract

Accurate and automated identification of water bodies from satellite imagery is critical for environmental monitoring, water resource management, and disaster response. Current deep learning approaches, however, suffer from a strong dependence on manual hyperparameter tuning, which limits their automation capability and robustness in complex, multi-scale scenarios. To overcome this limitation, this study proposes a fully automated segmentation framework that synergistically integrates an enhanced U-Net model with a novel hybrid evolutionary optimization strategy. Extensive experiments on public Kaggle and Sentinel-2 datasets demonstrate the superior performance of our method, which achieves a Pixel Accuracy of 96.79% and an F1-Score of 94.75, outperforming various mainstream baseline models by over 10% in key metrics. The framework effectively addresses the class imbalance problem and enhances feature representation without human intervention. This work provides a viable and efficient path toward fully automated remote sensing image analysis, with significant potential for application in large-scale water resource monitoring, dynamic environmental assessment, and emergency disaster management.
Keywords: water body identification; evolutionary algorithms; hyperparameter optimization; deep learning; remote sensing; semantic segmentation water body identification; evolutionary algorithms; hyperparameter optimization; deep learning; remote sensing; semantic segmentation

Share and Cite

MDPI and ACS Style

Yuan, Y.; Wei, P.; Qi, Z.; Deng, X.; Zhang, J.; Gan, J.; Chen, T.; Li, Z. Water Body Identification from Satellite Images Using a Hybrid Evolutionary Algorithm-Optimized U-Net Framework. Biomimetics 2025, 10, 732. https://doi.org/10.3390/biomimetics10110732

AMA Style

Yuan Y, Wei P, Qi Z, Deng X, Zhang J, Gan J, Chen T, Li Z. Water Body Identification from Satellite Images Using a Hybrid Evolutionary Algorithm-Optimized U-Net Framework. Biomimetics. 2025; 10(11):732. https://doi.org/10.3390/biomimetics10110732

Chicago/Turabian Style

Yuan, Yue, Peiyang Wei, Zhixiang Qi, Xun Deng, Ji Zhang, Jianhong Gan, Tinghui Chen, and Zhibin Li. 2025. "Water Body Identification from Satellite Images Using a Hybrid Evolutionary Algorithm-Optimized U-Net Framework" Biomimetics 10, no. 11: 732. https://doi.org/10.3390/biomimetics10110732

APA Style

Yuan, Y., Wei, P., Qi, Z., Deng, X., Zhang, J., Gan, J., Chen, T., & Li, Z. (2025). Water Body Identification from Satellite Images Using a Hybrid Evolutionary Algorithm-Optimized U-Net Framework. Biomimetics, 10(11), 732. https://doi.org/10.3390/biomimetics10110732

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