Semantic　change　detection　(SCD)　from　very　high-resolution　(VHR)　images　involves　two　key　challenges:　1)　the　global　features　of　bitemporal　images　tend　to　be　extracted　insufficiently,　leading　to　imprecise　land　cover　semantic　classification　results;　and　2)　the　detected　changed　objects　exhibit　ambiguous　boundaries,　resulting　in　low　geometric　accuracy.　To　address　these　two　issues,　we　propose　an　SCD　method　called　TBSCD-Net　based　on　a　multitask　learning　framework　to　simultaneously　identify　different　types　of　semantic　changes　and　regularize　change　boundaries.　First,　we　construct　a　hybrid　encoder　combining　transformer　and　convolutional　neural　network　(CNN)　(TCEncoder)　to　enhance　the　extraction　of　global　context　information.　A　bitemporal　semantic　linkage　module　(Bi-SLM)　is　embedded　into　the　TCEncoder　to　enhance　the　semantic　correlations　between　bitemporal　images.　Second,　we　introduce　a　boundary-region　joint　extractor　based　on　Laplacian　operators　(LOBRE)　to　regularize　the　changed　objects.　We　evaluated　the　effectiveness　of　the　proposed　method　using　the　SECOND　dataset　and　a　Fuzhou　GF-2　SCD　dataset　(FZ-SCD)　and　compared　it　with　seven　existing　methods.　The　proposed　method　performed　better　than　the　other　evaluated　methods　as　it　achieved　24.42%　separation　kappa　(Sek)　and　20.18%　global　total　classification　error　(GTC)　on　the　SECOND　dataset　and　23.10%　Sek　and　23.15%　GTC　on　the　FZ-SCD　dataset.　The　results　of　ablation　studies　on　the　FZ-SCD　dataset　also　verified　the　effectiveness　of　the　developed　modules　for　SCD.