Abstract: Clear-zone environment analysis is a critical task for meteorological station sites, which is of great significance for the representativeness of the stations. Traditionally, the acquisition of clear-zone environment data mainly relies on total stations or theodolites to measure occlusion of terrain, buildings, and vegetation around the station site. However, the manual measurement process is easily affected by subjective factors, with large errors and low efficiency. To meet the demand for rapid, efficient, and accurate clear-zone assessment in meteorological observation networks, this study proposes a method for analyzing the clear-zone situation of meteorological station sites based on machine learning, which can quickly provide calculation results. This method uses the combined model of DeeplabV3+ and Resnet18 to process the panoramic photos of meteorological station sites, calculates the occlusion angle parameters of the clear-zone environment around the station sites, automatically identifies the occluding objects, and then analyzes the clear-zone situation of the meteorological station sites. The experimental results show that this method can effectively generate different training models. After loading the model, it can quickly extract the clear-zone environment data from station panoramic photos. Compared with traditional methods, it has significant advantages in efficiency and convenience. In addition, through image processing, this method can be compatible with all-sky imaging pictures of ground meteorological stations, and has important application value for site selection and construction of meteorological stations as well as the protection of meteorological observation environments.