The invention discloses a deep learning remote sensing image semantic segmentation method and system based on U-NET, and the method comprises the steps: carrying out the correction and reconstruction of initial remote sensing data, carrying out the classification preprocessing, constructing a remote sensing sample library, carrying out the prediction classification based on a segmentation network model, obtaining a basic training data set, and carrying out the enhancement processing, thereby obtaining a target training data set, and processing the remote sensing image by using the trained segmentation network model to obtain a target segmentation result. According to the method, atmospheric correction and radiation correction are adopted to eliminate radiation quantity errors and interference data, and then super-resolution reconstruction is performed on the remote sensing image, so that interference of external factors is effectively avoided, and the resolution requirement on the remote sensing image is reduced; and the segmentation network model can adaptively learn the characteristics of different targets and realize multi-target segmentation in the same remote sensing image, so that when the segmentation target is changed, only the corresponding data set needs to be adopted to retrain the segmentation network model, the manual reconstruction of the characteristics and the algorithm is not needed, and the workload is greatly reduced.