Long-range　contextual　information　is　crucial　for　the　semantic　segmentation　of　high-resolution　(HR)　remote　sensing　images　(RSIs).　However,　image　cropping　operations,　commonly　used　for　training　neural　networks,　limit　the　perception　of　long-range　contexts　in　large　RSIs.　To　overcome　this　limitation,　we　propose　a　wide-context　network　(WiCoNet)　for　the　semantic　segmentation　of　HR　RSIs.　Apart　from　extracting　local　features　with　a　conventional　convolutional　neural　network　(CNN),　the　WiCoNet　has　an　extra　context　branch　to　aggregate　information　from　a　larger　image　area.　Moreover,　we　introduce　a　context　transformer　to　embed　contextual　information　from　the　context　branch　and　selectively　project　it　onto　the　local　features.　The　context　transformer　extends　the　vision　transformer,　an　emerging　kind　of　neural　networks,　to　model　the　dual-branch　semantic　correlations.　It　overcomes　the　locality　limitation　of　CNNs　and　enables　the　WiCoNet　to　see　the　bigger　picture　before　segmenting　the　land-cover/land-use　(LCLU)　classes.　Ablation　studies　and　comparative　experiments　conducted　on　several　benchmark　datasets　demonstrate　the　effectiveness　of　the　proposed　method.　In　addition,　we　present　a　new　Beijing　Land-Use　(BLU)　dataset.　This　is　a　large-scale　HR　satellite　dataset　with　high-quality　and　fine-grained　reference　labels,　which　can　facilitate　future　studies　in　this　field.