# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 4 Conclusions  
In this paper, a semantic segmentation R50A3-LWBENet model based on a convolutional neural network is proposed to address the main challenges of multi-scale features, noise interference and boundary blurring in lake remote sensing image segmentation. The model is able to combine global and local information to refine the edge profile of the lake water body with better feature extraction capability and segmentation performance. The adopted structure includes an encoder, an attention mechanism module, and a decoder. A ResNet50 network with deeper convolutional layers is used for encoding in the feature extraction layer to avoid feature loss and help to solve the multi-scale feature fusion problem in semantic segmentation. After that, the SE block based on the channel attention mechanism is added to the residual module inside the network, which enhances the correlation of each channel of the remote sensing image, strengthens the local perception domain of the water body part, and fully extracts the water body features. After the feature extraction is completed, CBAM is added to utilize the spatial dimension and channel dimension features so that the model focuses more on the water body pixel regions that play a decisive role in image segmentation and ignores the irrelevant regions. The attention mechanism enables the network to dynamically adjust the weights of the feature map, which helps to improve the model's attention to key features, while suppressing the over-reliance on distracting information such as noise, thus improving the accuracy and robustness of semantic segmentation. Finally, the feature map is up-sampled using bilinear interpolation and the different levels of features are fused, i.e., decoded, to finalize the extraction of the lake water body. Compared with the U-Net, AU-Net, RU-Net, ARU-Net, SER34 AUNet and MU-Net models, the R50A3-LWBENet model has the highest values of _PA_, _MPA_, and _MIoU_ and the smallest values of loss, as well as segmenting the lake water body with significantly better integrity and accuracy than the other models. While this model achieved better results on this dataset, there is still room for further improvements that can be made to the network performance. In the future, we should continue to expand remote sensing images of other lakes to increase the coverage of the dataset and further improve the generalization performance of the model for the extraction of lake water bodies in cold and dry areas. With the continuous expansion of the dataset, the extraction of water bodies becomes more and more accurate, and a complete water monitoring and evaluation network is gradually formed to provide strong support for water ecological and environmental management. Conceptualization, M.L. and J.L. ; methodology, J.L. ; software, M.L. and H.H. ; validation, M.L., H.H. and J.L. ; formal analysis, M.L. and J.L. ; investigation, M.L. ; resources, J.L. and H.H. ; data curation, M.L. and H.H. ; writing--original draft preparation, M.L. ; writing--review and editing, M.L., J.L. and H.H. ; visualization, M.L. ; supervision, M.L. and J.L. ; project administration, J.L. and H.H. ; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Natural Science Foundation of Inner Mongolia Autonomous Region of China, grant number 2022 MS06026; Key Technology Research Project of Inner Mongolia Autonomous Region, grant number 2020 GG0169; Program for Improving the Research Ability of Young Teachers in Colleges and Universities in Inner Mongolia Autonomous Region, grant number BR220116; National Natural Science Foundation of China, grant number 62373203. Not applicable.