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Deep learning has proven to be highly successful at semantic segmentation of remote sensing images (RSIs); however, it remains challenging due to the significant intraclass variation and interclass similarity, which limit the accuracy and continuity of feature recognition in land use and land cover (LULC) applications. Here, we develop a stage-adaptive selective network that can significantly improve the accuracy and continuity of multiscale ground objects. Our proposed framework can learn to implement multiscale details based on a specific attention method (SaSPE) and transformer that work collectively. In addition, we enhance the feature extraction capability of the backbone network at both local and global scales by improving the window attention mechanism of the Swin Transfer. We experimentally demonstrate the success of this framework through quantitative and qualitative results. This study demonstrates the strong potential of the prior knowledge of deep learning-based models for semantic segmentation of RSIs. [ABSTRACT FROM AUTHOR]