Sizhuo Li

Sizhuo Li is a Postdoctoral Researcher at the Department of Geosciences and Natural Resource Management within the Faculty of Science at the University of Copenhagen. Working at the intersection of artificial intelligence and environmental science, Dr. Li applies advanced neural networks to solve critical challenges in forest management and carbon stock estimation using remote sensing technologies.

Dr. Li's research interests center on developing and adapting deep learning methodologies for environmental monitoring applications. Their primary focus areas include intelligent forest management systems, tree resource quantification, and carbon stock estimation. Current research involves creating an automatic and scalable tree inventory framework based on UNet architecture that enables individual crown segmentation, precise tree counting, and accurate height estimation at country-wide scales.

Dr. Li's publication record demonstrates a clear trajectory from technical deep learning methodologies to practical environmental applications across diverse geographical contexts, from the Mediterranean region to the Sahel and Rwanda. This work effectively bridges computer science and environmental science to address urgent climate change and sustainability challenges through innovative technological solutions.

Dr. Li has published in prestigious journals including Nature Food, Nature Ecology and Evolution, Nature Reviews Electrical Engineering, and Frontiers in Remote Sensing, with multiple publications receiving significant citations and media attention. Their research has been highlighted by numerous news outlets and academic platforms, indicating growing influence in the field of AI-powered environmental monitoring.