Assigning　detailed　use　categories　to　buildings　is　a　challenging　and　relevant　task　in　urban　land　use　classification　with　applications　in　urban　planning,　digital　city　modelling　and　twinning.　This　study　aims　to　provide　the　categorisation　of　buildings　with　detailed　use　information　by　considering　the　possibilities　of　mixed-use.　Mixed-use　combines　different　use　forms,　and　serves　as　a　new　type　of　use　category.　We　obtain　attributive　information　by　combining　satellite　imagery　that　reflects　spatial　information　and　textual　information　from　publicly　available　point-of-interest　data　collected　by　citizens　and　available　on　online　maps.　We　propose　a　multimodal　transformerbased　building-use　classification　method　to　capture　and　fuse　these　different　data　sources　within　an　end-to-end　learning　workflow.　We　evaluate　the　effectiveness　of　our　proposed　method　on　four　urban　areas　in　China.　Experiments　show　that　the　proposed　method　effectively　maps　building　use　according　to　eight　types　of　fine-grain　categories,　with　a　Micro　F-1　score　equal　to　80.9%,　and　a　Macro　F-1　score　equal　to　62%　for　Wuhan　research　area.　The　proposed　method　is　able　to　harness　the　relationship　between　the　features　obtained　from　the　different　data　sources　and　results　in　higher　accuracy　than　the　state-of-the-art　fusion-based　multimodal　integration　methods.　The　proposed　method　can　effectively　increase　the　attributive　grain　of　building　use　resulting　in　high　classification　accuracy.