Abstract: In order to quickly extract spatial information of citrus orchards in districts and counties by using high-resolution remote sensing images and convolutional neural network models, we selected Pujiang County, a key citrus production area in Sichuan Province, as the research area, downloaded high-resolution images on Google earth as the data source and constructed 3 types of citrus orchard sample data sets of different tree age stages. On this basis, we trained U-net and DeepLabv3+ semantic segmentation models to extract citrus orchard spatial information and verified its classification accuracy. The results showed that the U-net and DeepLabv3+ models with different neural network structures performed well, with the close citrus information extraction accuracy. The overall accuracy was 88.30% and 86.79% and the Kappa coefficient was 0.75 and 0.72, respectively. In addition, the identification accuracy of citrus orchards in small plots were analyzed, their minimum identification plot area was about 120 m~2, and the average accuracy was above 85% when the plots area exceeds this number. This research could provide reference for farmer or local agricultural departments to use high-resolution remote sensing and open source deep learning classification methods to quickly and automatically extract orchard spatial information.