###### Abstract  
Shade houses are critical for modern agriculture, providing optimal growing conditions for shade-sensitive crops. However, their rapid expansion poses ecological challenges, making the accurate extraction of their spatial distribution crucial for sustainable development. The unique dark appearance of shade houses leads to low accuracy and high misclassification rates in traditional spectral index-based extraction methods, while deep learning approaches face challenges such as insufficient datasets, limited receptive fields, and poor generalization capabilities. To address these challenges, we propose ShadeNet, a novel method for shade house detection using high-resolution remote sensing imagery and semantic segmentation. ShadeNet integrates the Swin Transformer and Mask2 Former frameworks, enhanced by a Global-Channel and Local-Spatial Attention (GCLSA) module. This architecture significantly improves multi-scale feature extraction and global feature capture, thereby enhancing extraction accuracy. Tested on a self-labeled dataset, ShadeNet achieved a mean Intersection over Union (mIOU) improvement of 2.75% to 7.37% compared to existing methods, significantly reducing misclassification. The integration of the GCLSA module within the Swin Transformer framework enhances the model's ability to capture both global and local features, addressing the limitations of traditional CNNs. This innovation provides a robust solution for shade houses detection, supporting sustainable agricultural development and environmental protection. shade houses; transformers; channel-spatial attention mechanism; high-resolution remote sensing imagery; semantic segmentation; agricultural plastic covers (APCs) Article  
15 February 2025 25  
an important role not only in technological and ecological aspects, but also in promoting the sustainable development of agriculture on the socio-economic level, thus becoming an integral part of facility agriculture [5]. However, the rapid expansion of shade houses, which predominantly use plastic shading nets as a covering material, has resulted in numerous ecological challenges, including high greenhouse gas emissions [6], land occupation [7], soil acidification and salinization [8], biodiversity loss [9], and microplastic pollution [10]. Therefore, the management and monitoring of shade houses should be prioritized equally alongside PCGs and PMFs in the regulation of facility agricultural land. Accurately capturing the spatial distribution and area of shade houses is essential for crop monitoring, yield estimation, environmental conservation, and informed agricultural planning. Despite this, existing studies have largely focused on PCGs and PMFs [1; 11], while the monitoring of shade houses remains underexplored. This study proposes a novel method for accurately extracting shade houses, which in turn facilitates the collection of distribution data and other relevant information, addressing this research gap. Traditional APC extraction methods rely on manual surveys, which are time-consuming, inaccurate, and highly subjective [12]. Although remote sensing technology enables large-scale and real-time monitoring, the significant spectral differences between shade houses and PCGs make it difficult to establish a unified spectral index for the simultaneous extraction of these two structures [13]. As a result, the extraction of shade houses is often overlooked. Furthermore, traditional spectral index methods commonly face the issues of \"same object, different spectra\" and \"different objects, same spectra\", making it challenging to accurately distinguish shade houses from other easily confused land covers [14]. While [PERSON] et al. made some progress in shade house extraction using convolutional neural network (CNN)-based deep learning methods, their approach still suffers from low accuracy [15]. The local receptive field characteristic of CNNs limits their ability to capture global contextual information, particularly in complex terrain backgrounds, resulting in poor generalization [16].