###### Abstract
More importantly, existing research often does not prioritize shade houses as the main extraction target, and specialized methods for shade house extraction are still lacking. To overcome the challenges outlined above, this paper aims to propose a high-precision, shade houses-specific extraction method. We utilized high-resolution remote sensing imagery as the data source, and defined shade house extraction as a semantic segmentation task. The key problems to be addressed include the following:  
1. The lack of datasets, as there is currently no publicly available semantic segmentation dataset for shade houses, and high-quality datasets are essential for training deep learning models. 2. The low extraction accuracy, which is partly due to the concentrated distribution of shade houses, as farmers maximize land use, resulting in a high number and density of shade houses in the study area. Additionally, the dark appearance of shade houses often leads to confusion with black PMFs, roads, building shadows, and dark water bodies, causing diverse features to be present in high-resolution remote sensing imagery. This poses significant challenges for the accurate extraction of shade houses. This paper introduces several key contributions:  
1. We have constructed the first dedicated semantic segmentation dataset for shade houses. We collected six optical remote sensing images from Google Earth, each with a resolution of 38,656 \(\times\) 34,048 pixels, covering the period from 2021 to 2023, and spanning all four seasons. The data were annotated using SAM-Tool ([[https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)]([https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)), accessed on 24 March 2025), resulting in a dataset containing 4101 image patches of size 512 \(\times\) 512 pixels. This dataset provides essential data support for subsequent research. We propose ShadeNet, a novel method for detecting shade houses using high-resolution remote sensing imagery and semantic segmentation. ShadeNet integrates the Swin Transformer [17] and Mask2 Former [18] frameworks, and is enhanced by the Global-Channel and Local-Spatial Attention (GCLSA) module. This architecture significantly improves multi-scale feature extraction and global feature capture, thereby enhancing extraction accuracy. Notably, this study represents the first application of combining Swin Transformer [17] with Mask2 Former [18] for the remote sensing extraction task of PCGs, demonstrating the potential of this combination in remote sensing object extraction tasks with complex backgrounds. The structure of this paper is arranged as follows: Section 2 reviews related works, summarizing existing methods for shade house extraction and their limitations. Section 3 provides a detailed introduction to the proposed high-precision shade house extraction method, including the construction of the dataset and the design of the ShadeNet. Section 4 describes the experimental design and results, and analyzes the effectiveness of the proposed method in various scenarios. Section 5 discusses the advantages and limitations of the proposed method, as well as potential improvements and applications. Finally, Section 6 summarizes the research findings and outlines directions for future research.