The demand for semantic segmentation of ultra-high-resolution remote sensing images is becoming increasingly stronger in various fields, posing a great challenge with concern to the accuracy requirement. Most of the existing methods process ultra-high-resolution images using downsampling or cropping, but using this approach could result in a decline in the accuracy of segmenting data, as it may cause the omission of local details or global contextual information. Some scholars have proposed the two-branch structure, but the noise introduced by the global image will interfere with the result of semantic segmentation and reduce the segmentation accuracy. Therefore, we propose a model that can achieve ultra-high-precision semantic segmentation. The model consists of a local branch, a surrounding branch, and a global branch. To achieve high precision, the model is designed with a two-level fusion mechanism. The high-resolution fine structures are captured through the local and surrounding branches in the low-level fusion process, and the global contextual information is captured from downsampled inputs in the high-level fusion process. We conducted extensive experiments and analyses using the Potsdam and Vaihingen datasets of the ISPRS. The results show that our model has extremely high precision.