## 1 Introduction
The GEA attention module, which we have investigated, is a novel and efficient attention mechanism that can be effectively incorporated into the algorithmic design of various image segmentation tasks to enhance the accuracy of segmentation. Moreover, the concept of efficient feature aggregation in the GEA attention module may also inspire other researchers in their pursuit of model improvement. On the other hand, the feature learning method of the water body segmentation model in up-sampling is limited, hindering the acquisition of global information and resulting in the omission of small water bodies. To address this issue, we propose a multi-scale feature fusion module to enhance the extraction of spatial semantic information for water bodies. Experimental results demonstrate that our GEA-MSNet model exhibits significant improvements in data quality and demonstrates certain generalization capabilities across different datasets. The main work of this paper is as follows:  
1. The paper presents a backbone network design based on Res-UNet coding and decoding architecture. A novel GEA efficient attention module is introduced for shallow feature fusion. This module employs deep separable convolutions operations to effectively reduce the number of feature parameters. Additionally, a global response normalization layer is utilized to integrate features and enhance the learning capability of local water body information while suppressing the interference information to expedite model convergence and mitigate overfitting phenomenon. Forming an efficient feature learning attention module. 2. We introduce a multi-scale feature fusion module into the decoder component of our model to optimize the up-sampling feature transfer process. This module employs a parallel double-branch structure and combines mixed expansion convolution with ordinary convolution to enhance the receptive field of the convolution kernel, thereby integrating more contextual features for improved accuracy in lake water extraction and segmentation. 3. The model's effectiveness and results were validated through a multitude of experiments conducted on diverse datasets. To enhance the network's robustness, a GAN network was employed to generate an additional 300 image expansion datasets in the lake scene classification datasets. Furthermore, the generalization performance of the network was assessed using an open Qinghai-Tibet Plateau lake dataset. The remaining sections of this paper are organized as follows: The Section 2 provides an overview of relevant research, including a description of the datasets and an introduction to the theoretical framework of the model. In the Section 3, we present the experimental methodology, outline the overall model structure, and provide detailed descriptions of each designed module's specific structure. The Section 4 encompasses various experiments conducted in conjunction with different datasets and models for comparative analysis. On the Section 5, we discuss our conclusions while also conducting ablation experiments to validate each module's contribution. Finally, in the Section 6, we summarize our work comprehensively and offer prospects for future research.