Building　change　detection　is　essential　to　many　applications,　such　as　monitoring　of　urban　areas,　land　use　management,　and　illegal　building　detection.　It　has　been　seen　as　an　effective　means　to　detect　building　changes　from　remote-sensing　images.　This　paper　proposes　an　object-based　Siamese　neural　network,　labeled　as　Obj-SiamNet,　to　detect　building　changes　from　high-resolution　remote-sensing　images.　We　combine　the　advantages　of　object-based　image　analysis　methods　and　Siamese　neural　networks　to　improve　the　geometric　accuracies　of　detected　boundaries.　Moreover,　we　implement　the　Obj-SiamNet　at　multiple　segmentation　levels　and　automatically　construct　a　set　of　fuzzy　measures　to　fuse　the　obtained　results　at　multi-levels.　Furthermore,　we　use　generative　adversarial　methods　to　generate　target-like　training　samples　from　publicly　available　datasets　and　construct　a　relatively　sufficient　training　dataset　for　the　Obj-SiamNet　model.　Finally,　we　apply　the　proposed　method　into　three　high-resolution　remote-sensing　datasets,　i.e.,　a　GF-2　image-pair　in　Fuzhou　City,　and　a　GF2　image　pair　in　Pucheng　County,　and　a　GF-2—GF-7　image　pair　in　Quanzhou　City.　We　also　compare　the　proposed　method　with　three　other　existing　ones,　namely,　STANet,　ChangeNet,　and　Siam-NestedUNet.　Experimental　results　show　that　the　proposed　method　performs　better　than　the　other　three　in　terms　of　detection　accuracy.　(1)　Compared　with　the　detection　results　from　single-scale　segmentation,　the　detection　results　from　multi-scale　increases　the　recall　rate　by　up　to　32%,　the　F1-Score　increases　by　up　to　25%,　and　the　Global　Total　Classification　error　(GTC)　decreases　by　up　to　7%.　(2)　When　the　number　of　available　samples　is　limited,　the　adopted　Generative　Adversarial　Network　(GAN)　is　able　to　generate　effective　target-like　samples　for　diverting　samples.　Compared　with　the　detection　without　using　GAN-generated　samples,　the　proposed　detection　increases　the　recall　rate　by　up　to　16%,　increases　the　F1-Score　by　up　to　14%,　and　decreases　GTC　by　9%.　(3)　Compared　with　other　change-detection　methods,　the　proposed　method　improves　the　detection　accuracies　significantly,　i.e.,　the　F1-Score　increases　by　up　to　23%,　and　GTC　decreases　by　up　to　9%.　Moreover,　the　boundaries　of　the　detected　changes　by　the　proposed　method　have　a　high　consistency　with　that　of　ground　truth.　We　conclude　that　the　proposed　Obj-SiamNet　method　has　a　high　potential　for　building　change　detection　from　high-resolution　remote-sensing　images.　©　2024　Science　Press.　All　rights　reserved.