Remote Sensing Techniques with the Use of Deep Learning in the Determining Dynamics of the Illegal Occupation of Rivers and Lakes: A Case Study in the Jinshui River Basin, Wuhan, China

Laiyin Shen, Yuhong Huang, Chi Zhou and Lihui Wang ()
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Laiyin Shen: Hubei Engineering Research Center of Water Resources Digital and Intelligent Technology, Hubei Water Resources Research Institute, Wuhan 430070, China
Yuhong Huang: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
Chi Zhou: Hubei Engineering Research Center of Water Resources Digital and Intelligent Technology, Hubei Water Resources Research Institute, Wuhan 430070, China
Lihui Wang: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China

Sustainability, 2025, vol. 17, issue 3, 1-17

Abstract: The “Four Illegal Activities”, which involve occupation, extraction, and construction along shorelines, have become significant challenges in river and lake management in recent years. Due to the diverse and scattered nature of monitoring targets, coupled with the large volumes of data involved, traditional manual inspection methods are no longer sufficient to meet regulatory demands. Late fusion change detection methods in deep learning are particularly effective for monitoring river and lake occupation due to their straightforward principles and processes. However, research on this topic remains limited. To fill this gap, we selected eight popular deep learning networks—VGGNet, ResNet, MobileNet, EfficientNet, DenseNet, Inception-ResNet, SeNet, and DPN—and used the Jinshui River Basin in Wuhan as a case study to explore the application of Siamese networks to monitor river and lake occupation. Our results indicate that the Siamese network based on EfficientNet outperforms all other models. It can be reasonably concluded that the combination of the SE module and residual connections provides an effective approach for improving the performance of deep learning models in monitoring river and lake occupation. Our findings contribute to improving the efficiency of monitoring river and lake occupation, thereby enhancing the effectiveness of water resource and ecological environment protection. In addition, they aid in the development and implementation of efficient strategies for promoting sustainable development.

Keywords: river and lake systems; illegal occupation; change detection; deep learning; remote sensing; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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