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2019  B.S., Geographic Information Science, Sun Yat-Sen University, Guangzhou, China 

Since graduating from Sun Yat-Sen University, Shengjie Liu has spent the last two years at The University of Hong Kong as a research assistant in the Department of Physics. His work has been in the investigation of light pollution, examining the link between satellite-observed night lights and night sky brightness.

He also has worked at OneSpace Technology in Chongqing, China, as a remote sensing engineer. In that role, he applied satellite data to map crop types and assess water and air quality.

His publications include:

Liu, S., Shi, Q., and Zhang, L., 2020. Few-shot Hyperspectral Image Classification with Unknown Classes Using Multitask Deep Learning. IEEE Transactions on Geoscience and Remote Sensing, Early Access, 2020. doi:10.1109/TGRS.2020.3018879.

Liu, S., Luo, H., and Shi, Q., 2020. Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification. IEEE Geoscience and Remote Sensing Letters, Early Access, 2020. doi:10.1109/LGRS.2020.3005076.

Liu, S., and Shi, Q., 2020. Local Climate Zone Mapping as Remote Sensing Scene Classification Using Deep Learning: A Case Study of Metropolitan China. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 229242, 2020. doi:10.1016/j.isprsjprs.2020.04.008.

Liu, S., and Shi, Q., 2020. Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, Early Access, 2020. doi:10.1109/LGRS.2019.2962768.

Liu, S., Qi, Z., Li, X., and Yeh, A.G.O., 2019. Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data. Remote Sensing, 11(6), p.690. doi:10.3390/rs11060690.

In the Population, Health and Place program, Shengjie wants to study environmental health problems, including urban heat, light pollution and air pollution to analyze their impacts on residents’ physical and mental health. He is also interested in exploring the way in which high-rise and low-rise built environments affect people’s health, especially during the pandemic.