Transforming Environmental Monitoring with Computer Vision for Climate Impact
About Me
I am a second-year PhD student at the Computer Laboratory, University of Cambridge, supervised by Srinivasan Keshav, Andrew Blake, and David Coomes. I am also affiliated with the UKRI Centre for Doctoral Training in AI for Environmental Risks. My research lies at the intersection of computer vision and climate change, developing a data driven system for scalable forest monitoring.
Prior to Cambridge, I completed my Master’s at ETH Zurich in Switzerland with an Excellence Scholarship and my Bachelor’s at Nanjing University in China, both in Geomatics—a field that, despite its somehow confusing name, has laid down the foundation for my work today, combining information science with geospatial data.
Feel free to explore this website to learn more about my past projects, ongoing research, and future directions. I also use this space to share blog posts reflecting on my research thoughts. Beyond academics, I enjoy choral singing and Chinese calligraphy in my leisure time.
If you’re interested in my work or would like to connect, please feel free to reach out!
News
[02/2025] Our SPREAD (Synthetic Photorealistic Arboreal Dateset) paper has been accepted by Ecological Informatics.
[01/2025] I have completed the EnterpriseTECH programme at Cambridge Judge Buisness School!
PhD Research Focus
My PhD research focuses on developing data-driven systems for scalable forest monitoring, integrating remote sensing and close-range observations.
On the remote sensing end, I leverage Earth Observation data by treating the radiative transfer model as a graphics renderer and its inversion as inverse graphics. Through an end-to-end machine learning pipeline, I retrieve interpretable biophysical variables from satellite imagery to characterize forests at a large scale.
On the close-range sensing end, I focus on training tree segmentation algorithms for 3D point clouds derived from drone-based laser scans. To overcome the challenges of limited annotated data, I generate synthetic datasets using graphics engines to train 3D vision algorithms for forest carbon quantification. The key challenge lies in bridging the domain gap between real and synthetic forests, ensuring robust algorithmic generalization.
Before completing my PhD, my goal is to develop a complete pipeline for an innovative forest monitoring system by connecting both ends—from remote to close-range sensing, from real to simulation. By inverting the radiative transfer process, we can extract biophysical forest parameters at scale. These variables, serving as meta-data to describe forest structure, will enable the creation of digital forests, facilitating the generation of synthetic data to train close-range sensing algorithms for precise forest monitoring anywhere on Earth.