Semantic　segmentation　of　remote　sensing　images　is　a　challenging　and　critical　task.　The　complexity　of　the　remote　sensing　environment　often　poses　difficulties　in　accurately　capturing　object　boundaries.　To　address　this　challenge,　we　propose　a　Contextual　U-Net　(CU-Net)　architecture　for　semantic　segmentation　of　remote　sensing　images,　which　incorporates　three　collaborative　improvements.　Firstly,　a　Boundary　Feature　Extraction　(BFE)　module　is　introduced　to　fuse　semantic　feature　information　from　the　backbone　network　with　boundary　feature　information,　thereby　enhancing　the　accuracy　of　edge　segmentation　in　remote　sensing　images.　Secondly,　we　propose　an　Adaptive　Feature　Selection　(AFS)　module　that　highlights　representative　semantic　channels　for　irregular　objects,　enabling　long-distance　dependence　capture　between　pixels　in　the　irregular　region　of　the　boundary　and　pixels　inside　the　objects.　Thirdly,　a　Recursive　Feature　Fusion　(RFF)　module　is　introduced　to　effectively　aggregate　hierarchical　features　through　adaptive　inter-layer　feature　guidance,　facilitating　accurate　capture　of　image　edges　and　textures.　We　collected　high-quality　remote　sensing　data　through　UAVs,　comprising　4509　images　across　6　different　categories.　Extensive　experiments　demonstrate　that　the　proposed　CU-Net　architecture　outperforms　state-of-the-art　methods.　©　2023　SPIE.