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Sizhuo LiView profile

Research Fellow

• Deep learning
• remote sensing
• forest management
• +3 more

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.

Sayed M. BateniView profile

Professor

• Earth Remote Sensing
• Land-Atmosphere Interaction
• Data Assimilation
• +3 more

Sayed M. Bateni serves as a Professor in the Department of Civil, Environmental and Construction Engineering at the University of Hawaii at Manoa, where he has advanced from Assistant Professor (2013) to Associate Professor (2019) and full Professor (2023). Previously, he held a Lecturer and Postdoctoral Scholar position at UCLA (2011-2013). His academic foundation includes a Ph.D. from MIT (2011), M.S. from Sharif University of Technology (2005), and B.S. from Isfahan University of Technology (2003). His research spans Earth Remote Sensing, Land-Atmosphere Interaction, and Data Assimilation techniques, with significant emphasis on Optimization in Hydrology and Artificial Intelligence applications. Key methodologies include variational assimilation, ensemble Kalman filtering, and machine learning for environmental modeling. His work integrates satellite data with hydrological processes to analyze surface energy balance, evaporation dynamics, snowpack characterization, and soil properties. Recent publications reveal a clear progression toward AI-driven environmental monitoring, with 2025 articles focusing on neural network applications for geotechnical geometry and land-atmosphere coupling, while earlier works (2007-2013) established foundational methods in microwave-based cryosphere analysis and hydraulic jump modeling. The research consistently bridges computational techniques with practical civil engineering challenges. His scientific recognition includes: MIT Graduate Study Scholarship (2006-2010) F.S Chia Recruitment Ph.D Fellowship (Canada, 2005) DAAD Ph.D Fellowship (Germany, 2005) ORS Ph.D Award (England, 2005) Silver Medal of Physics Olympiad (1998) Professor Bateni teaches core courses including Fluid Mechanics, Engineering Hydraulics, and Applied Hydrology, and maintains professional affiliations with the American Geophysical Union and IEEE Geoscience and Remote Sensing Society. No documented advising relationships or grant funding details were found in the source material.