The　composition　and　arrangement　of　spatial　entities,　i.e.,　land　cover　objects,　play　a　key　role　in　distinguishing　land　use　types　from　very　high　resolution　(VHR)　remote　sensing　images,　in　particular　in　urban　environments.　This　paper　presents　a　new　method　to　characterize　the　spatial　arrangement　for　urban　land　use　extraction　using　VHR　images.　We　derive　an　adjacency　unit　matrix　to　represent　the　spatial　arrangement　of　land　cover　objects　obtained　from　a　VHR　image,　and　use　a　graph　convolutional　network　to　quantify　the　spatial　arrangement　by　extracting　hidden　features　from　adjacency　unit　matrices.　The　distribution　of　the　spatial　arrangement　variables,　i.e.,　hidden　features,　and　the　spatial　composition　variables,　i.e.,　widely　used　land　use　indicators,　are　then　estimated.　We　use　a　Bayesian　method　to　integrate　the　variables　of　spatial　arrangement　and　composition　for　urban　land　use　extraction.　Experiments　were　conducted　using　three　VHR　images　acquired　in　two　urban　areas:　a　Pleiades　image　in　Wuhan　in　2013,　a　Superview　image　in　Wuhan　in　2019,　and　a　GeoEye　image　in　Oklahoma　City　in　2012.　Our　results　show　that　the　proposed　method　provides　an　effective　means　to　characterize　the　spatial　arrangement　of　land　cover　objects,　and　produces　urban　land　use　extractions　with　overall　accuracies　(i.e.,　86%　and　93%)　higher　than　existing　methods　(i.e.,　83%　and　88%)　that　use　spatial　arrangement　information　based　on　building　types　on　the　Pleiades　and　GeoEye　datasets.　Moreover,　it　is　unnecessary　to　further　categorize　the　dominant　land　cover　type　into　finer　types　for　the　characterization　of　spatial　arrangement.　We　conclude　that　the　proposed　method　has　a　high　potential　for　the　characterization　of　urban　structure　using　different　VHR　images,　and　for　the　extraction　of　urban　land　use　in　different　urban　areas.