The　accurate　identification　of　land-surface　changed　classes　when　locating　changed　areas　with　regular　boundaries　from　satellite　images　represents　a　significant　challenge,　particularly　in　these　areas　with　considerable　spectra　and　seasonal　differences.　This　paper　develops　a　boundary-guided　Siamese　multitask　network,　namely　BGSNet,　for　the　purpose　of　semantic　change　detection　(SCD)　from　high-resolution　remote　sensing　images.　The　objective　of　BGSNet　is　to　utilize　robust　boundary　semantics　to　enhance　the　intra-class　consistency　of　change　features,　alleviating　the　pseudo-changes　caused　by　temporal　variances　while　retaining　well　boundary　details.　The　proposed　BGSNet　consists　of　three　tasks　including　bi-temporal　semantic　segmentation,　changed　areas　detection,　and　boundary　detection　tasks.　In　particular,　for　the　semantic　segmentation　task,　a　Siamese　multilevel　pyramid　network　based　on　transformer　as　feature　extractors　is　introduced　to　fully　capture　robust　semantic　features　of　bitemporal　remote　sensing　images.　For　the　boundary　detection　task,　a　multi-scale　feature　decoder　is　designed　to　enhance　boundary　semantic　representation.　For　the　change　detection　task,　a　boundary-contextual　guided　module　is　constructed　to　supply　fine-grained　semantic　constraints,　refining　the　boundaries　of　detected　areas.　Finally,　we　introduce　a　multitask　self-adaptive　weighting　loss　function　that　considers　task　uncertainty,　which　effectively　balances　the　learning　effects　of　different　tasks,　and　improves　the　model＇s　adaptability　to　varied　semantic　change　scenarios.　Extensive　experiments　were　conducted　on　the　JiLin-1　and　HRSCD　datasets,　demonstrating　that　BGSNet　outperformed　the　eight　state-of-the-art　methods　in　identifying　various　semantic　changes.　Our　methods　produced　the　highest　attribute　accuracy,　exceeding　reference　methods　by　5.41%-29.63%　and　2.94%-28.09%　in　SeK　measures.　Moreover,　the　detected　results　by　BGSNet　exhibited　excellent　boundary　consistency　with　ground　truth,　resulting　in　the　lowest　geometric　errors　GTC　of　0.187,　0.308,　and　0.321　on　the　JiLin-1,　HRSCD,　and　Fuzhou　datasets,　respectively.　The　proposed　method　also　showed　a　high　application　promise　in　large-scale　cropland　nonagriculturalization　scenarios　with　significant　temporal　and　spectral　variations.　The　code　and　data　will　be　made　available　at　https://github.com/long123524/BGSNet.