Urban　building　type　information　is　crucial　to　many　urban　applications　such　as　the　identification　of　urban　functional　areas　and　estimation　of　urban　environmental　variables.　This　paper　presents　a　new　method　to　extract　urban　building　types　using　multi-scale　features　and　integrating　height　features　derived　from　high　resolution　remote　sensing　images.　We　first　conduct　an　image　semantic　segmentation　to　extract　building　and　shadow　objects　from　remote　sensing　images,　and　then　estimate　the　height　of　buildings　based　upon　the　directional　relationship　of　a　building　object　and　its　shadow　information.　Following　multi-scale　image　analysis　concept,　we　extract　a　series　of　multi-scale　features　regarding　the　height,　geometry,　and　spatial　structure　of　building　objects.　Last,　we　use　a　machine　learning　method　based　upon　random　forest　to　classify　building　types.　We　also　analyze　the　impact　of　different　spatial　units　of　building　types　on　classification　results.　Experiments　were　conducted　in　Fuzhou,　Fujian　province,　China,　using　a　Chinese　GF-2　satellite　images　acquired　on　February　18,　2020.　Our　results　show　that:　(1)　The　overall　accuracy　of　building　type　classification　combined　with　multi-scale　features　reached　82.98%,　and　the　kappa　coefficient　was　0.77,　which　was　better　than　other　conventional　methods,　namely　a　Multi-scale　Classification　Without　Height　Features　(MCNH),　a　Single-scale　Classification　Incorporating　Height　Features　(SC),　and　a　Single-scale　Classification　Without　Height　Features　(SCNH)　in　this　paper;　(2)　The　classification　accuracy　of　middle-low　residential　buildings　and　high-rise　commercial　and　residential　buildings　was　improved　by　adding　height　features.　Compared　with　classification　results　without　using　height　features,　the　overall　accuracy　was　improved　by　11.28%;　(3)　The　fusion　of　image　features　at　multiple　scales　can　reduce　the　misclassification　of　adjacent　buildings　into　dense　buildings.　Compared　with　a　single-scale　classification　method,　the　proposed　method　improved　overall　accuracy　by　2.77%.　We　conclude　that　the　use　of　high-resolution　remote　sensing　images　provides　an　effective　strategy　to　estimate　building　heights　based　upon　shadow　information　and　improves　the　classification　accuracy　of　urban　building　types,　particularly　when　detailed　digital　surface　model　data　are　absent.　In　addition,　the　fusion　of　multi-scale　image　features　can　improve　the　characterization　of　complex　building　types　in　urban　areas　and　the　subsequent　classification　accuracy　accordingly.　Nevertheless,　we　also　observed　that　the　results　of　classified　building　types　were　affected　by　the　initial　extraction　of　building　information　from　high　resolution　remote　sensing　images,　implying　that　a　further　improvement　of　building　type　classification　can　be　done　by　improving　the　extraction　methods,　e.g.,　using　a　more　advanced　semantic　segmentation　model.　2021,　Science　Press.　All　right　reserved.