Accurate extraction of plastic greenhouses from high-resolution remote sensing imagery is essential for agricultural resource management and facility-based crop monitoring. However, the dense spatial distribution, irregular morphology, and complex background interference of greenhouses often limit the effectiveness of conventional segmentation methods. This study proposes a deep learning framework that integrates a multi-scale Transformer-based decoder with a Swin-UNet architecture to improve feature representation and extraction accuracy. To enhance geometric consistency, a post-processing strategy is introduced, combining connected component analysis and morphological operations to suppress noise and refine boundary shapes. Using GF-2 satellite imagery over Weifang City, China, the model achieved a recall of 92.44%, precision of 91.47%, intersection-over-union of 85.13%, and F1-score of 91.95%. In addition to instance-level extraction, spatial distribution and statistical analysis were performed across administrative divisions, revealing regional disparities in protected agriculture development. The proposed approach offers a practical solution for greenhouse mapping and supports broader applications in land use monitoring, agricultural policy enforcement, and resource inventory.

Addressing the demands of agricultural resource digitization and facility crop monitoring, precise extraction of plastic greenhouses using high-resolution remote sensing imagery demonstrates pivotal significance for implementing refined farmland management. However, the complex spatial topological relationships among densely arranged greenhouses and the spectral confusion of ground objects within agricultural backgrounds limit the effectiveness of conventional methods in the large-scale, precise extraction of plastic greenhouses. This study constructs an Individual Plastic Greenhouse Extraction Network (IPGENet) by integrating a multi-scale feature fusion decoder with the Swin-UNet architecture to improve the accuracy of large-scale individual plastic greenhouse extraction. To ensure sample accuracy while reducing manual labor costs, an iterative sampling approach is proposed to rapidly expand a small sample set into a large-scale dataset. Using GF-2 satellite imagery data in Shandong Province, China, the model realized large-scale mapping of individual plastic greenhouse extraction results. In addition to large-scale sub-meter extraction and mapping, the study conducted quantitative and spatial statistical analyses of extraction results across cities in Shandong Province, revealing regional disparities in plastic greenhouse development and providing a novel technical approach for large-scale plastic greenhouse mapping.