Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a series of impacts on the environment. Therefore, the extraction of large-scale greenhouses is crucial for the sustainable development of agriculture and environmental governance. It is difficult for existing methods to acquire precise boundaries. Therefore, we propose a spatial convolutional long short-term memory structure, which can fully consider the spatial continuity of ground objects. We use multitask learning to improve the network’s ability to extract image boundaries and promote convergence through auxiliary loss. We propose a superpixel optimization module to optimize the main-branch results of network semantic segmentation using more precise boundaries obtained by advanced superpixel segmentation techniques. Compared with other mainstream methods, our proposed structure can better consider spatial information and obtain more accurate results. We chose Shandong Province, China, as the study area and used Gaofen-1 satellite remote sensing images to create a new greenhouse dataset. Our method achieved an F1 score of 77%, a significant improvement over mainstream semantic segmentation networks, and it could extract greenhouse results with more precise boundaries. We also completed large-scale greenhouse mapping for Shandong Province, and the results show that our proposed modules have great potential in greenhouse extraction.

Since the material yard is a crucial place for storing coal, ore, and other raw materials, accurate access to its location is of great significance to the construction of resource-based ports, environmental supervision, and investment and operating costs. Its extraction is difficult owing to its small size, variable shape, and dense distribution. In this paper, the SPPA-Net target detection network was proposed to extract the material yard. Firstly, a Dual-Channel-Spatial-Mix Block (DCSM-Block) was designed based on the Faster R-CNN framework to enhance the feature extraction ability of the location and spatial information of the material yard. Secondly, the Feature Pyramid Network (FPN) was introduced to improve the detection of material yards with different scales. Thirdly, a spatial pyramid pooling self-attention module (SPP-SA) was established to increase the global semantic information between material yards and curtail false detection and missed detection. Finally, the domestic GF-2 satellite data was adopted to conduct extraction experiments on the material yard of the port. The results demonstrated that the detection accuracy of the material yard reached 88.7% when the recall rate was 90.1%. Therefore, this study provided a new method for the supervision and environmental supervision of resource-based port material yards.