## 2 Materials and Methods
### The Encoder
In this article, we present the GCASPP module, which comprises five concurrent branches, as illustrated in Figure 2.  
GCASPP comprises four concurrent attention branches, each consisting of a Global Context Attention Module (GCAM) and convolutions with varying atrous rates and scales, along with an additional branch dedicated to global average pooling. The GCASPP's input feature map is imbued with abundant semantic details. The branch employing global average pooling performs down-sampling on the feature maps to alleviate over-training within the network. The four convolutions with different scales and atrous rates effectively capture contextual information from different receptive fields. Following the multiscale extraction layer, GCAM focuses on the pixel dependency relationships, thereby enhancing the network's flexibility in extracting contextual information. Consequently, this design enhances the capability of multi-scale feature extraction, thereby improving the precision of water body detection. Attention mechanisms are widely used in deep learning tasks such as semantic segmentation [28] and object detection [29]. [PERSON] et al. [30] integrated channel attention mechanisms with spatial attention to facilitate the detection of airport runways. However,  
Figure 3: The structure of the attention feature fusion module. the incorporation of dual attention introduces extra parameters, significantly prolonging the training duration of the neural network. [PERSON] et al. [31] employed an efficient Squeeze and Excitation attention mechanism for extracting airport runways, achieving a reduction in both training and evaluation timeframes. The non-local network proposed by [PERSON] et al. [32] can capture long-range dependencies and is a technique that fully utilizes the global information of images. However, this network has the problem of low computational efficiency. GCAM is a lightweight improvement based on the Non-local Network. The input feature map is denoted as \(X\in R^{\mathbb{C}\times H\times W}\), where C signifies the channel count and H and W correspond to the height and width of the features, respectively. GCAM maps \(X\) to \(A_{GC}(X)\in R^{\mathbb{C}\times 1\times 1}\). The detailed architecture of GCAM is illustrated in Figure 4.  
In order to achieve lightweight characteristics and reduce parameter computation, the squeeze transform module uses a \(1\times 1\) convolution operation to obtain a \(C/r\times 1\times 1\) eigenvector, where \(r\) is the reduction ratio and is set to \(r=4\). Then, BatchNorm is used to facilitate optimization and act as a regularizer that facilitates generalization. After passing through Relu layers, \(1\times 1\) convolution is performed. The following provides the definition:  
\[V_{i}=conv^{1\times 1}Relu(\text{BN}(conv^{1\times 1}X_{i})) \tag{1}\]  
where \(X_{i}\) and \(V_{i}\) are the input feature map and the output feature of the squeeze transform module, respectively, and \(i\) is the index of query positions. \(conv^{1\times 1}\) signifies a \(1\times 1\) convolution operation. Additionally, BN and \(Relu\) represent Batch Normalization and the Rectified Linear Unit activation function, respectively. In the context modeling module, an attention feature map is generated through the application of a SoftMax activation function. The specific calculation is defined as follows:  
\[A_{GCA}(X_{j})=\sum_{j=1}^{N_{p}}\frac{e^{conv^{1\times 1}X_{j}}}{\sum_{m=1}^{N_{p} }e^{conv^{1\times 1}X_{m}}} \tag{2}\]  
where \(A_{GCA}(X_{j})\) denotes the attention feature map and \(j\) enumerates all possible positions of the feature map \(X_{j}\). \(conv^{1\times 1}\) denotes a \(1\times 1\) convolution operation. Subsequently, the feature map \(V_{i}\) is multiplied with the attention feature map \(A_{GCA}(X_{j})\) to perform attention pooling. Finally, global contextual features are aggregated into the feature map \(X\) at each location through the use of addition.