Mr. Shuai YUAN was funded by the National Natural Science Foundation of China Young Student Basic Research Program

DEC2024

Congratulations! One Ph.D. student in our department was funded by the National Natural Science Foundation of China Young Student Basic Research Program (Ph.D. candidate)

Recently, the National Natural Science Foundation of China (NSFC) has determined the list of projects funded by the National Natural Science Foundation of China for the Young Student Basic Research Program (Ph.D. Candidate) ((國家自然科學基金青年學生基礎研究項目 (博士研究生)) in 2024. One Ph.D. student in our department, Mr. YUAN Shuai, has been successfully approved for this project, with a funding amount of ¥300,000 CNY for each project.

Mr. YUAN Shuai's approved funded project is "High-frequency fine wetland dynamic mapping method based on multi-source remote sensing adaptive fusion and time-series sample auto-labeling". The supervisor is Professor GONG Peng. This project focuses on the large-scale continuous and accurate daily wetland dynamic mapping, making full use of spatiotemporal features of multi-source remote sensing data to address the issues where existing methods fail to capture wetland dynamics and inadequately mine spatiotemporal information, thereby bridging the gaps of wetland dynamic mapping and providing comprehensive support for wetland dynamic understanding and wetland protection.

Mr. YUAN Shuai was admitted to the Department of Geography in Jan, 2024 and majors in urban remote sensing intelligent interpretation and analysis. Urban scenarios are complex and traditional and common AI models have poor performance because of the poor feature extraction ability. Mr. YUAN Shuai focuses on how to effectively utilize the characteristics of urban geographical objects to guide the AI models to conduct large-scale and reliable urban remote sensing image intelligent interpretation. He has successfully published 8 papers in authoritative journals and conferences such as IEEE Transactions on Cybernetics (IF=9.4), IEEE Geoscience and Remote Sensing Magazine (IF=16.2), and NeurIPS (top conference on machine learning).