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Supervisor: Dr Emily So

YueZhu

 

Research overview:

The impacts of climate change are making cities more vulnerable to natural disasters, especially for the urban population living in the most risk-prone areas such as informal settlements. This makes it urgent to detect, understand and predict urban land use changes. At present, the understanding of spatial-temporal evolution of urban land use patterns (LUP) is heavily constrained by the lack of efficient and precise detection of urban land use changes, particularly in and around informal settlements for which there are few established data sources even in fairly prosperous cities in developing countries. 

The main aim of the research is to fill this gap and develop a new method to investigate urban land use changes with the necessary efficiency and precision. The research makes use of publicly available datasets of multi-temporal remote sensing (RS) data. The use of such datasets for large city regions is made feasible through the adoption of deep-learning (DL) based methods. More specifically, the algorithms of convolutional neural networks (CNNs) has been applied to achieve necessary performance for complex image analysis of LUPs. The hypothesis of this research is that DL methods, which hitherto have rarely been tested for LUP analysis, would bring significant new insights because of their capabilities to model the diversity and variability of LUPs based on RS data. 

 

Biography: 

Yue Zhu is currently pursuing an PhD degree in Architecture. She received an M.Eng. degree in Industrial Design Engineering from the Tongji University, Shanghai, China in 2014 and an M.Sc. degree in Emergent Technologies and Design from the Architectural Association, London, UK, in 2016. She is particularly interested in developing machine learning methods for the investigation of spatial-temporal evolution of land use patterns.

 

Links:

https://www.researchgate.net/profile/Yue-Zhu-27