Dr Yongjing Mao
Associate Lecturer

Dr Yongjing Mao

PhD 2022

Remote Sensing and Coastal Geomorphology,

The University of Queensland, Australia.

MSc 2018

Coastal and Marine Engineering and Management

Delft University of Technology (TU Delft), Netherlands

BSc (Eng) 2016

Harbour, Coastal and Offshore Engineering,

Hohai University, China

Engineering
Civil and Environmental Engineering

Dr. Yongjing Mao (preferred name: Mao) is an Associate Lecturer at the Water Research Laboratory within the School of Civil and Environmental Engineering. Mao holds a Bachelor's and Master's degree in Coastal Engineering and a PhD in Remote Sensing. Prior to his position at UNSW, he worked as a Postdoctoral Research Fellow at the University of Queensland.

Mao is an expert in integrating coastal morphology knowledge with remote sensing and machine learning methods to understand shoreline changes and predict future states at regional, continental, and global scales. His research interests include the application of remote sensing, deep learning, and data assimilation to shoreline monitoring and modelling, with a focus on improving satellite-derived shoreline mapping through SAR-Optical fusion, employing data assimilation for shoreline modelling, and using innovative deep learning spatio-temporal models to predict future shoreline changes.

Location
UNSW Water Research Laboratory, Manly Vale
  • Journal articles | 2023
    Mao Y; Van Niel TG; McVicar TR, 2023, 'Reconstructing cloud-contaminated NDVI images with SAR-Optical fusion using spatio-temporal partitioning and multiple linear regression', ISPRS Journal of Photogrammetry and Remote Sensing, 198, pp. 115 - 139, http://dx.doi.org/10.1016/j.isprsjprs.2023.03.003
    Journal articles | 2022
    Mao Y; Harris DL; Xie Z; Phinn S, 2022, 'Global coastal geomorphology – integrating earth observation and geospatial data', Remote Sensing of Environment, 278, http://dx.doi.org/10.1016/j.rse.2022.113082
    Journal articles | 2021
    Mao Y; Harris DL; Callaghan DP; Phinn S, 2021, 'Determining the Shoreline Retreat Rate of Australia Using Discrete and Hybrid Bayesian Networks', Journal of Geophysical Research: Earth Surface, 126, http://dx.doi.org/10.1029/2021JF006112
    Journal articles | 2021
    Mao Y; Harris DL; Xie Z; Phinn S, 2021, 'Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine', ISPRS Journal of Photogrammetry and Remote Sensing, 181, pp. 385 - 399, http://dx.doi.org/10.1016/j.isprsjprs.2021.09.021
    Journal articles | 2019
    de Boer W; Mao Y; Hagenaars G; de Vries S; Slinger J; Vellinga T, 2019, 'Mapping the sandy beach evolution around seaports at the scale of the African continent', Journal of Marine Science and Engineering, 7, http://dx.doi.org/10.3390/jmse7050151
  • Theses / Dissertations |
    Mao Y, Predicting long-term shoreline response to sea-level rise on continental and global scales with data-driven models, http://dx.doi.org/10.14264/d566c79

2022. UQ Dean's Award for Outstanding HDR Theses

2016. Erasmus Mundus Partner Country Scholarship (€49,000)

I apply spatio-temporal deep learning methods to model the shoreline change.

I extract shoreline from satellite images at regional, continental and global scales.

I study SAR-Optical satellite image fusion and explore its potential for shoreline monitoring.

I developed spatio-temporal deep learning models (e.g. ConvLSTM and PredRNN) for ground cover prediction in Great Barrier Reef Catchments.

I mapped coastal geomorphology on the global scale with the application of Google Earth Engine and machine learning.

My Teaching

CVEN9620 Rivers, Estuaries and Wetlands

ENGG2500 Fluid Mechanics for Engineers