LCS-Net: a lightweight architecture for efficient coastal water segmentation

In high-resolution remote sensing imagery, near-shore water bodies typically exhibit tortuous shorelines, fragmented lakeshore coves, and superimposed disturbances such as building reflections, vegetation shadows, and mixed substrates, posing significant challenges to the fine extraction of water boundaries. Although deep learning-based semantic segmentation has substantially improved water body recognition accuracy, most existing methods focus on global context modeling while paying insufficient attention to computational efficiency on edge devices; existing lightweight models also struggle to balance high precision with low-power deployment in complex near-shore scenarios. To address this gap, this paper proposes LCS-Net, an edge-deployment-oriented architecture that establishes a system-level collaborative paradigm of “redundant feature generation—dynamic feature filtering—bypass compensation” through the recombination of existing efficient components. Following a UNet-type encoder-decoder framework, the architecture constructs a lightweight backbone by cascading standard Ghost modules with depthwise separable convolutions, embeds channel-spatial attention mechanisms to suppress spectral aliasing, and introduces bilinear-interpolation-convolution upsampling with skip connections to restore high-frequency details. Experimental results demonstrate that on a self-built unmanned aerial vehicle near-shore water dataset, LCS-Net achieves 95.92% Intersection over Union (IoU) and 97.86% F1-score with merely 2.24 million parameters and 8.24 GFLOPs, simultaneously attaining high accuracy and efficiency. It should be noted that the reported performance was obtained under specific seasonal and sensor configurations; the model’s zero-shot generalization capability under broader environmental conditions awaits subsequent validation.