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Zhou Fang: Revisit cities with computer vision: a novel deep learning powered framework for urban fabric analysis

Supervisor: Dr Ying Jin
Zhou Fang

 

 

 

Research overview:

Zhou Fang is a PhD Candidate with interest in deep learning techniques and their applications in the field of urban studies. His current research is focused on the development of computer vision techniques for urban physical fabric classification, and of Generative Adversarial Networks (GAN) for urban physical fabric generation. The research work explores ways to equip computers with urban planners’ capability to recognize, classify and design diverse urban layouts. A novel multisource heterogeneous 3-dimentional urban fabric dataset has been developed for four metropolitan areas with distinct urban fabric typologies in order to train, validate and test above models.

 

Biography:

Prior to working at the Cities and Transport Research Group in the Martin Centre for Architectural and Urban Studies, Zhou completed his MPhil from Department of Engineering at Cambridge, and his BEng in Civil Engineering from the University of Hong Kong. Outside the university setting, Zhou has worked for Arup as a tunnelling engineer for three years and participated in the design of several mega-scale infrastructure projects in the East-Asia region, including the Hong Kong-Zhuhai-Macau Bridge and the Shatin to Central Link (SCL) on Hong Kong’s MTR metro system.