Zhiwei Li, Ph.D. (李志伟 博士)

Research Assistant Professor, The Hong Kong Polytechnic University

Dr. Zhiwei Li

Office: ZN613, Block Z, PolyU, HK

Email: zhiwei.li(AT)polyu.edu.hk

I am currently a Research Assistant Professor in the Department of Land Surveying and Geo-Informatics (LSGI) at The Hong Kong Polytechnic University (PolyU). I am also affiliated with the PolyU JC STEM Lab of Earth Observations (POLEIS) and the Research Centre for Artificial Intelligence in Geomatics (RCAIG), both led by Prof. Qihao Weng.

My research primarily focuses on Remote Sensing of Cloudy and Rainy Environments, which involves applying big Earth data and geospatial artificial intelligence (GeoAI) for environmental research and applications, especially in cloudy and rainy tropical/subtropical regions. The specific research topics include urban remote sensing, cloud detection and removal, multi-source data fusion, land cover/use mapping, flood monitoring. I have authored more than 20 journal and conference papers in RSE, ISPRS P&RS, IEEE TGRS/JSTARS, etc., with a total of over 2200 citations (as of June 2024 in Google Scholar), among which four have been selected as ESI Highly Cited Papers.

I obtained my Ph.D. degree in Cartography and Geoinformation Engineering from Wuhan University in June 2020 under the supervision of Prof. Huanfeng Shen and Prof. Zongyi He. Prior to my current position, I worked as a Postdoctoral Fellow at Wuhan University from July 2020 to June 2022. I have been the PI of one project funded by the National Natural Science Foundation of China (NSFC) and two projects funded by the China Postdoctoral Science Foundation. The methods and tools I developed have been widely applied in preprocessing images from multiple satellites, supporting national land resource monitoring by the China Land Survey and Planning Institute and other related departments.

News

May 10, 2024 Congratulations to MSc student Huan ZHOU on successfully completing his dissertation oral presentation.:clap::tada:
May 5, 2024 Our new paper entitled Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas: The case of Pearl River Delta has been published in Remote Sensing of Environment (HTML, PDF).
Research Spotlight💡: An integrated time series mapping method enhances LULC accuracy and frequency in cloud-prone areas using spectral-indices-fused deep learning models and reconstruction techniques. The assessment shows variations in accuracy with different cloud masks, highlighting their importance in LULC mapping.
Mar 26, 2024 Our new book chapter entitled Urban Flooding Monitoring and Management in Geospatial Perspective: Data, Techniques, and Platforms has been published in the book ‘Handbook of Geospatial Approaches to Sustainable Cities‘ (HTML).
Mar 16, 2024 Our abstract entitled CAN WE RECONSTRUCT CLOUD-COVERED FLOODING AREAS IN HARMONIZED LANDSAT AND SENTINEL-2 IMAGE TIME SERIES? has been accepted for presentation in the IGARSS 2024 (Link).
Feb 19, 2024 My authored papers have reached a new milestone, receiving over 2,000 citations according to the statistics in Google Scholar as of February 2024 (Link)!:tada:
Feb 16, 2024 Our new paper entitled Transferring Deep Models for Cloud Detection in Multi-Sensor Images via Weakly Supervised Learning has been published in IEEE Transactions on Geoscience and Remote Sensing (HTML, PDF).
Dec 8, 2023 Congratulations to MSc students Sukanta BAG and Yim-ting CHEUNG on successfully completing their project oral presentations.:clap::tada:
Nov 10, 2023 Research assistant Shaofen Xu’s paper entitled Polyline simplification using a region proposal network integrating raster and vector features has been published in GIScience & Remote Sensing (HTML, PDF).