## 6 Conclusions  
Shade houses are an essential component of modern facility agriculture, and accurately acquiring their distribution and area information is of great significance for environmental protection and sustainable development. As a key type of APCs, the extraction of shade houses remains an area with limited research. To address this gap, this paper proposes a precise extraction method for shade houses based on high-resolution remote sensing imagery and semantic segmentation technology. The ShadeNet designed in this study integrates the GCLSA module into the Swin Transformer-based Mask2 Former framework. Through a hierarchical structure and sliding window attention, the model effectively captures both local and global features of the image. Meanwhile, by combining multilayer perceptrons and convolutional layers, the model strengthens its understanding of inter-channel and spatial relationships, significantly enhancing its ability to capture global features and interpret contextual information, thereby improving segmentation accuracy. The proposed ShadeNet was tested on a self-labeled shade house dataset. The results show that the ShadeNet network outperforms PSPNet [47], SegNet [45], U-Net [27], DeepLabV3+ [44], HRNet [28] and SegFormer [46] by 7.37%, 6.25%, 5.26%, 4.45%, 4.28%, 2.75%, and in terms of the mIOU metric. Moreover, it significantly reduces misjudgments and misclassifications of shade houses. This method enables accurate extraction of shade houses, providing reliable technical support for obtaining information such as their distribution and total coverage area. The contributions of this study are manifold. First, the combination of Swin Transformer [17] and Mask2 Former [18] significantly enhances the model's ability to capture both global and local features, effectively addressing the limited receptive field problem of traditional CNNs. Second, the innovative development of the GCLSA module strengthens the model's understanding of inter-channel relationships and spatial dependencies,  
Figure 14: Exemplary diagrams of shade house boundary and self-shadow confusion zones (cases (**a**,**b**)). improving its ability to extract global features and analyze contextual information. Finally, the proposed ShadeNet provides an effective solution for the accurate detection of shade houses, filling a gap in existing literature and offering valuable technical support for agricultural monitoring and environmental management. In summary, the proposed ShadeNet method provides a significant advancement in the field of shade houses extraction from high-resolution remote sensing images. The integration of advanced attention mechanisms and feature learning techniques enables the model to achieve high accuracy and robustness in various complex scenarios. Future work will focus on addressing the current limitations and exploring new directions to further enhance the performance and applicability of the model. Specifically, future research will focus on incorporating boundary extraction methods to optimize the delineation of shade house boundaries, thereby improving extraction accuracy and minimizing the impact of boundary errors on both model training and final outcomes. Conceptualization, Y.L. and Q.Z. ; methodology, Y.L. ; validation, Y.L., M.X. and W.D. ; formal analysis, Q.Z. ; investigation, Y.L. ; writing--original draft preparation, Y.L., M.X. and Q.Z. ; writing--review and editing, Y.L., M.X., W.D. and Q.Z. ; project administration, Q.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China, grant number 2022 YFE0209300. The funder is the corresponding author, [PERSON]. The funding period for this project is from 1 November 2022, to 31 October 2025. Not applicable.