Detecting　land　use　changes　in　urban　areas　from　very-high-resolution　(VHR)　satellite　images　presents　two　primary　challenges:　1)　traditional　methods　focus　mainly　on　comparing　changes　in　land　cover-related　features,　which　are　insufficient　for　detecting　changes　in　land　use　and　are　prone　to　pseudo-changes　caused　by　illumination　differences,　seasonal　variations,　and　subtle　structural　changes　and　2)　spatial　structural　information,　which　is　characterized　by　topological　relationships　among　land　cover　objects,　is　crucial　for　urban　land　use　classification　but　remains　underexplored　in　change　detection.　To　address　these　challenges,　this　study　developed　a　local-global　structural　interaction　network　(LGSI-Net)　based　on　a　Siamese　graph　neural　network　(SGNN)　that　integrates　high-level　structural　and　semantic　information　to　detect　urban　land　use　changes　from　bitemporal　VHR　images.　We　developed　both　local　structural　feature　interaction　module　(LSIM)　and　global　structural　feature　interaction　module　(GSIM)　to　enhance　the　representation　of　bitemporal　structural　features　at　the　global　scene　graph　and　local　object　node　levels.　Experiments　on　the　publicly　available　MtS-WH　dataset　and　two　generated　datasets,　LUCD-FZ　and　LUCD-HF,　show　that　the　proposed　method　outperforms　the　existing　bag　of　visual　word　(BoVW)-based　method　and　CorrFusionNet.　Furthermore,　we　evaluated　the　detection　performance　for　different　semantic　feature　extraction　strategies　and　structural　feature　extraction　backbones.　The　results　demonstrate　that　the　proposed　method,　which　integrates　high-level　semantic　and　graph　isomorphism　network　(GIN)-derived　structural　features　achieves　the　best　performance.　The　method　trained　on　the　LUCD-FZ　dataset　was　successfully　transferred　to　the　LUCD-HF　dataset　with　different　urban　landscapes,　indicating　its　effectiveness　in　detecting　land　use　changes　from　VHR　satellite　images,　even　in　areas　with　relatively　large　imbalances　between　changed　and　unchanged　samples.