Semantic　segmentation　of　ultrahigh-resolution　(UHR)　remote　sensing　images　is　a　fundamental　task　for　many　downstream　applications.　Achieving　precise　pixel-level　classification　is　paramount　for　obtaining　exceptional　segmentation　results.　This　challenge　becomes　even　more　complex　due　to　the　need　to　address　intricate　segmentation　boundaries　and　accurately　delineate　small　objects　within　the　remote　sensing　imagery.　To　meet　these　demands　effectively,　it　is　critical　to　integrate　two　crucial　components:　global　contextual　information　and　spatial　detail　feature　information.　In　response　to　this　imperative,　the　multilevel　context-aware　segmentation　network　(MCSNet)　emerges　as　a　promising　solution.　MCSNet　is　engineered　to　not　only　model　the　overarching　global　context　but　also　extract　intricate　spatial　detail　features,　thereby　optimizing　segmentation　outcomes.　The　strength　of　MCSNet　lies　in　its　two　pivotal　modules,　the　spatial　detail　feature　extraction　(SDFE)　module　and　the　refined　multiscale　feature　fusion　(RMFF)　module.　Moreover,　to　further　harness　the　potential　of　MCSNet,　a　multitask　learning　approach　is　employed.　This　approach　integrates　boundary　detection　and　semantic　segmentation,　ensuring　that　the　network　is　well-rounded　in　its　segmentation　capabilities.　The　efficacy　of　MCSNet　is　rigorously　demonstrated　through　comprehensive　experiments　conducted　on　two　established　international　society　for　photogrammetry　and　remote　sensing　(ISPRS)　2-D　semantic　labeling　datasets:　Potsdam　and　Vaihingen.　These　experiments　unequivocally　establish　MCSNet　stands　as　a　pioneering　solution,　that　delivers　state-of-the-art　performance,　as　evidenced　by　its　outstanding　mean　intersection　over　union　(mIoU)　and　mean　$F1$　-score　(mF1)　metrics.　The　code　is　available　at:　https://github.com/WUTCM-Lab/MCSNet.