Spatial　information　regarding　the　arrangement　of　land　cover　objects　plays　an　important　role　in　distinguishing　the　land　use　types　at　land　parcel　or　local　neighborhood　levels.　This　study　investigates　the　use　of　graph　convolutional　networks　(GCNs)　in　order　to　characterize　spatial　arrangement　features　for　land　use　classification　from　high　resolution　remote　sensing　images,　with　particular　interest　in　comparing　land　use　classifications　between　different　graph-based　methods　and　between　different　remote　sensing　images.　We　examine　three　kinds　of　graph-based　methods,　i.e.,　feature　engineering,　graph　kernels,　and　GCNs.　Based　upon　the　extracted　arrangement　features　and　features　regarding　the　spatial　composition　of　land　cover　objects,　we　formulated　ten　land　use　classifications.　We　tested　those　on　two　different　remote　sensing　images,　which　were　acquired　from　GaoFen-2　(with　a　spatial　resolution　of　0.8　m)　and　ZiYuan-3　(of　2.5　m)　satellites　in　2020　on　Fuzhou　City,　China.　Our　results　showed　that　land　use　classifications　that　are　based　on　the　arrangement　features　derived　from　GCNs　achieved　the　highest　classification　accuracy　than　using　graph　kernels　and　handcrafted　graph　features　for　both　images.　We　also　found　that　the　contribution　to　separating　land　use　types　by　arrangement　features　varies　between　GaoFen-2　and　ZiYuan-3　images,　due　to　the　difference　in　the　spatial　resolution.　This　study　offers　a　set　of　approaches　for　effectively　mapping　land　use　types　from　(very)　high　resolution　satellite　images.