Semantic　change　detection　(SCD)　from　very　high-resolution　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　multi-task　learning　framework　to　simultaneously　identify　different　types　of　semantic　changes　and　regularize　change　boundaries.　Firstly,　we　construct　a　hybrid　encoder　combining　transformer　and　convolutional　neural　network　(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.　Secondly,　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%　Sek　and　20.18%　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.　IEEE