## 6 Conclusions  
In this paper, we propose a novel semantic segmentation algorithm model called GEA-MSNet for the water body semantic segmentation of remote sensing images. The proposed model improves in three aspects: firstly, we introduce an innovative high-efficiency attention module called GEA to enhance the learning capability of shallow features and aggregate spatial and channel information features while maintaining a reasonable parameter count. Secondly, a residual network architecture is incorporated into the encoder component to extract the semantic features of water bodies during image downsampling, thereby augmenting both the accuracy and generalization capabilities of the model for water body extraction. Thirdly, in the decoder section, a multi-scale feature fusion module is devised to effectively leverage \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Module Choose**} & \multicolumn{4}{c}{**Indictors**} \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Ablation Study \\ Module \\ \end{tabular} } & U-net & Res-50 & GEA & Multi-Scale & mIoU & Recall & PA \\ \cline{2-8}  & \(\surd\) & & & & 63.05 & 75.07 & 83.55 \\  & \(\surd\) & \(\surd\) & & & 65.01 & 76.41 & 84.73 \\  & \(\surd\) & \(\surd\) & \(\surd\) & 71.62 & 81.24 & 88.29 \\  & \(\surd\) & \(\surd\) & & \(\surd\) & 73.08 & 81.98 & 89.10 \\ \hline GEA-MSNet & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 75.49 & 83.79 & 90.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of our ablation experiment, where \(\surd\) indicates the selected modules in our study. the semantic information of feature maps and enhance the precision of small water body extraction as well as lake edge segmentation during upsampling. The GEA-MSNet model outperforms other models in the task of the semantic segmentation of water bodies in remote sensing images. In terms of visualization results, the GEA-MSNet model demonstrates superior performance in accurately segmenting lake edges and small water bodies, while maintaining a minimal increase in parameter count. Furthermore, we conducted ablation experiments to demonstrate the individual contributions of each component in our model. However, despite achieving the highest accuracy in the lake semantic segmentation task, GEA-MSNet still exhibits issues related to target segmentation errors and a loss of water body information. Addressing these challenges will be the focus of our future research endeavors. Additionally, there exist numerous potential combinations of enhanced modules that warrant further investigation into their specific effects and performance. Our subsequent strategy entails exploring diverse combinations to enhance the model's performance in water body segmentation. The research focus of this paper solely pertains to the segmentation of lake areas in remote sensing images. The algorithm's performance in other image segmentation tasks remains unknown, and its suitability for multi-target segmentation has not been explored. The generalization of our study was limited to the use of only two public datasets for experimentation; however, future research could expand this scope by incorporating additional datasets. Conceptualization, Q.L. and Z.W. ; methodology, Z.W. ; software, Z.Z. ; validation, Q.L. and Z.W. ; formal analysis, Z.Z. ; investigation, Z.W. ; resources, Z.W. and L.W. ; writing--original draft preparation, Q.L. ; writing--review and editing, Q.L. and Z.Z. ; visualization, Z.W. ; supervision, L.W. ; project administration, L.W. All authors have read and agreed to the published version of the manuscript. This work is funded by National Natural Science Foundation of China (No. 32160279), Science and Technology Major Project of Inner Mongolia (No. 2021 ZD0011), Science and Technology Plan Project of Inner Mongolia, China (No. 2022 YFHH0017). Not applicable. The present study incorporates several publicly available datasets. The datasets were provided by National Cryosphere Desert Data Center.