Simultaneous extracting area and quantity of agricultural greenhouses in large scale with deep learning method and high-resolution remote sensing images

Greenhouses are an important part of modern facility-based agriculture. While creating well-being for human society, greenhouses also bring negative impacts such as air pollution, soil pollution, and water pollution. Therefore, it is of great signiflcance to obtain information such as the area and quantity of greenhouses. It is still a challenging task to flnd a low-cost, high-efflciency, and easy-to-use method for the dual extraction of greenhouse area and quantity on a large scale. In this study, relatively easy-to-obtain high-resolution Google Earth remote sensing images are used as the experimental data source, and an area and quantity simultaneous extraction framework (AQSEF) is constructed to extract both the area and quantity of greenhouses. The AQSEF uses UNet and YOLO v5 series networks as core op-erators to complete model training and prediction, and main components such as SWP, OSW&NMS and GCA complete data postprocessing. To evaluate the feasibility of our method, we take Beijing, China, as the research area and select multiple accuracy evaluation indicators in the two branches for accuracy veriflcation. The results show that the mIoU, OA, Kappa, Recall and Precision with the best performance model in the area extraction branch can reach 0.931, 0.987, 0.867, 0.91 and 0.914, respectively. Additionally, the Recall, Precision, AP@0.5 and mAP@0.5: 0.95 values of the best performance model are 0.781, 0.891, 0.812 and 0.509, respectively, in the extraction of the quantity of greenhouses. Finally, in Beijing, the area covered by greenhouses is approximately 85.443 km2, and the quantity of greenhouses is approximately 155,464. With the proposed method, the time consumed for area extraction and quan-tity extraction is 6.73 h and 12.97 h, respectively. The experimental results show that AQSEF helps to overcome the spatiotemporal diversity of greenhouses and quickly and accurately map a high-spatial-resolution greenhouse distribu-tion product within the research area.