This　paper　presents　a　cross-learning　network　(i.e.,　CLCFormer)　integrating　fine-grained　spatial　details　within　long-range　global　contexts　based　upon　convolutional　neural　network　(CNN)　and　transformer,　for　semantic　segmentation　of　very　high-resolution　(VHR)　remote　sensing　images.　More　specifically,　CLCFormer　comprises　two　parallel　encoders,　derived　from　CNN　and　transformer,　and　a　CNN　decoder.　The　encoders　are　backboned　on　SwinV2　and　EfficientNet-B3,　from　which　the　extracted　semantic　features　are　aggregated　at　multiple　levels　using　a　bilateral　feature　fusion　module.　Firstly,　we　used　attention　gate　modules　to　enhance　feature　representation,　improving　segmentation　results　for　objects　with　various　shapes　and　sizes.　Secondly,　we　used　an　attention　residual　module　to　refine　spatial　features’s　learning,　alleviating　boundary　blurring　of　occluded　objects.　Finally,　we　developed　a　new　strategy,　called　auxiliary　supervise　strategy,　for　model　optimization　to　further　improve　segmentation　performance.　Our　method　was　tested　on　the　WHU,　Inria,　and　Potsdam　datasets,　and　compared　with　CNN-based　and　transformer-based　methods.　Results　showed　that　our　method　achieved　state-of-the-art　performance　on　the　WHU　building　dataset　(92.31%IoU),　Inria　building　dataset　(83.71%IoU),　and　Potsdam　dataset　(80.27%MIoU).　We　concluded　that　CLCFormer　is　a　flexible,　robust,　and　effective　method　for　the　semantic　segmentation　of　VHR　images.　The　codes　of　the　proposed　model　are　avaliable　at　https://github.com/long123524/CLCFormer.　IEEE