Deep Learning-Driven Morphological Dataset and Analysis Methods for Chinese Campuses

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dc.contributor.authorWang, Yujiao
dc.contributor.authorWang, Xiao
dc.contributor.authorCai, Chenyi
dc.contributor.authorTang, Peng
dc.date.accessioned2024-11-28T11:18:45Z
dc.date.available2024-11-28T11:18:45Z
dc.date.issued2024en
dc.descriptionGame changer? Planning for just and sustainable urban regions, Paris, 8-12th July 2024en
dc.description.abstractModern 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 learning
dc.description.versionpublished versionen
dc.identifier.isbn978-94-64981-82-7en
dc.identifier.pageNumber1913-1931
dc.identifier.urihttps://hdl.handle.net/20.500.14235/2253
dc.language.isoEnglishen
dc.publisherAESOPen
dc.rightsopenAccessen
dc.rights.licenseCC-BYen
dc.sourceGame changer? Planning for just and sustainable urban regions, Paris, 8-12th July 2024en
dc.titleDeep Learning-Driven Morphological Dataset and Analysis Methods for Chinese Campuses
dc.typeconferenceObjecten
dc.type.versionpublishedVersionen
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