Two　main　issues　are　faced　when　using　very-high-spatial-resolution　(VHR)　satellite　images　for　building　change　detection:　1)　the　boundaries　of　detected　changes　are　hard　to　be　consistent　with　the　ground　truth　and　2)　detected　changes　are　easily　affected　by　different　viewing　angles　of　bitemporal　images,　leading　to　noticeable　false　changes.　To　deal　with　these　issues,　this　study　develops　a　new　Siamese　change　detection　network　[i.e.,　Siamese　multitask　change　detection　network　(SMCD-Net)]　based　on　a　multitask　learning　framework　to　improve　building　change　detection,　particularly　in　the　geometric　aspect.　Boundary　information　is　formulated　as　an　auxiliary　task　to　constrain　the　learning　of　high-level　semantic　features.　To　enhance　the　identification　of　real　changes　from　false　changes,　we　model　the　directional　relationships　between　buildings　and　their　shadows　by　fuzzy　sets,　and　incorporate　the　relationship　information　into　SMCD-Net,　leading　to　a　network　variant,　labeled　as　SMCD-Net-m.　Experiments　were　conducted　on　three　datasets:　a　publicly　available　dataset,　a　Chinese　GaoFen-2　dataset,　and　a　French　Pleiades　dataset.　We　compared　our　methods　with　seven　other　methods,　i.e.,　object-based　Siamese　network,　ChangeStar,　ChangeFormer,　BIT,　STANet,　FC-Siam-diff,　and　Siam-NestedUNet.　Results　showed　that　the　proposed　SMCD-Net　obtained　the　best　detection　results,　achieving　the　lowest　global　total　errors　on　all　datasets.　By　incorporating　directional　information,　SMCD-Net-m　evidently　improved　detection　accuracy,　particularly　when　using　bitemporal　images　with　a　large　viewing　angle　difference.　The　improvement　was　positively　correlated　with　the　accuracy　of　building　shadows　extracted　from　VHR　images.