CC-BYWang, YujiaoWang, XiaoCai, ChenyiTang, Peng2024-11-282024-11-282024978-94-64981-82-7https://hdl.handle.net/20.500.14235/2253Game changer? Planning for just and sustainable urban regions, Paris, 8-12th July 2024Modern campuses in China display distinct morphological characteristics, evolving to form unique patterns as subsystems within the urban environment. Hence, the approaches for comprehensive analysis for those urbanized Chinese campus morphology (UCCM) are important. This study proposes a framework for dataset construction and morphology recognition of UCCM, using visual representing learning methods. Computer vision technologies are used to acquire the morphology patches of 1257 campuses. We analyse the campus morphology with our proposed multi-dimensional morphometrics. Then, we constructed multiple morphological cluster maps for UCCM in terms of road, building and landscape, respectively. The cluster maps show significant compliance with human visual perception. Compared with classic morphometrics, our approach excels in learning implicit morphological characteristics with lower data processing demands and less reliance on expert experience. Keywords: Urbanized Chinese campus morphology, Morphometric, Visual Representation, Self-organizing Map, Unsupervised learningEnglishopenAccessconferenceObject1913-1931