## 3 Materials and Methods
### ShadeNet: A Novel Approach to High-Precision Shade House Extraction  
#### 3.2.1 Overall Architecture of ShadeNet  
This paper proposes a semantic segmentation network (ShadeNet) for the precise extraction of shade houses, and its overall architecture is shown in Figure 6. The model primarily consists of three key modules: the GCLSA-Swin Transformer feature extraction module, the pixel decoder module, and the Transformer decoder module. Additionally, to enhance the network's global context understanding, we innovatively introduce a Global-Channel and Local-Spatial Attention (GCLSA) module into the feature extraction part. The functions of each module and their adaptability to the shade house extraction are detailed below from an overall structural perspective, and the collaborative mechanism of the network is explained in conjunction with the overall workflow. GCLSA-Swin Transformer Feature Extraction Module: As the backbone of the network, the GCLSA-Swin Transformer leverages the advantages of the Swin Transformer [17] to extract multi-scale and high-level semantic features from remote sensing images. Its window-based attention mechanism, combined with a hierarchical structure design, enables the efficient capture of both global and local features of shade houses. The shade houses in remote sensing images have the following characteristics: varying density within each image patch, which may be concentrated in certain areas or dispersed; varying sizes, encompassing large areas covered by shade houses, as well as small targets; and a high presence of similar objects in the surrounding environment, such as dark-colored water bodies, roads, and dark-colored vegetation, which can lead to model misclassification. The hierarchical feature design of Swin Transformer [17], by progressively extracting and integrating features of different scales, is capable of capturing the global distribution of shade houses while also accurately expressing small targets and detailed features, thus providing robust feature support for the subsequent segmentation tasks. The specific design will be detailed in Section 3.2.2. Pixel Decoder Module: The Pixel Decoder is responsible for fusing multi-scale features from the output of the GCLSA-Swin Transformer and generating high-resolution feature maps. In response to the diversity of shade houses sizes and the varying density of their distribution, the Pixel Decoder enhances the model's ability to resolve densely packed areas by progressively upsampling and integrating multi-scale features. At the same  
Figure 6: Overall network architecture. (**a**) Overall network framework; (**b**) structure of the Transformer Decoder; (**c**) structure of the GCLSA-Swin Transformer. time, it improves the model's recognition capability for small target regions. Particularly when dealing with areas that are dense or have complex backgrounds, which are prone to confusion, the pixel decoder effectively integrates contextual information to reduce mis-segmentation, thus laying the foundation for generating accurate segmentation masks. Transformer Decoder Module: The Transformer Decoder directly predicts the target class and segmentation mask through a query-based mechanism. Each query vector interacts with the feature map to generate candidate target regions, which are then further refined into precise target classifications and segmentation boundaries.