Extraction　of　land　use　information　from　very　high　resolution　(VHR)　images　plays　a　crucial　role　in　urban　planning　and　management.　The　study　aims　to　extract　urban　land　use　information　using　VHR　images　and　open　geographic　data　using　graph　neural　networks.　We　first　obtained　land　cover　objects　using　a　semantic　segmentation　model.　The　spatial　topological　relationships　between　land　cover　objects　were　then　modeled　using　graph　theory　and　represented　as　graph-structured　data,　in　which　the　attributes　of　graph　nodes　were　computed　based　upon　points　of　interest　(POI)　data　and　classified　land　cover　map.　Last,　we　used　graph　neural　network　to　learn　high-level　structural　features　for　urban　land　use　classification.　The　proposed　method　was　applied　to　the　core　urban　area　of　Fuzhou　city,　China.　Results　showed　that　graph　neural　networks　are　effective　for　urban　land　use　classification　from　VHR　images,　and　integrating　open　geographic　data　further　improves　the　accuracy　of　urban　land　use　classification　to　87%　compared　to　the　84%accuracy　obtained　by　using　only　VHR　images.　Our　method　exhibits　high　potential　for　extracting　fine-grained　urban　land　use　in　various　urban　areas.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.