刘莉，中共党员，1995.8，博士，讲师，硕士生导师。

2022年9月毕业于浙江大学，农业遥感与信息技术专业，博士。2022年11月入职三峡大学，现为硕士生导师和本科班主任。先后承担了本科生的《机器学习》、《算法分析与设计》、《现代图像处理理论》和研究生的《学科前沿与实践》等课程的教学工作。近五年发表论文12篇，其中一作 SCI TOP/EI/核心6篇。申请国家发明专利5项，软件著作权6项。主持国家自然科学基金1项，湖北省自然科学基金1项，宜昌市自然科学研究项目1项，横向项目1项；作为项目骨干参与国家自然科学基金项目2项、国家重点研发计划重点专项1项、公益性行业（农业）科研专项1项。担任SCI二区期刊《Remote Sensing》客座编辑，以及《Remote Sensing of Environment》在内等SCI期刊的审稿人。

主要研究方向：遥感图像处理、人工智能、农业气象灾害监测、地物信息遥感提取等。

My name is Li Liu, a member of the Communist Party of China, born in August 1995. I hold a Ph.D. and currently serve as a Lecturer and Master’s Supervisor at China Three Gorges University.

I obtained my Ph.D. in Agricultural Remote Sensing and Information Technology from Zhejiang University in September 2022. In November 2022, I joined China Three Gorges University, where I now serve as a graduate supervisor and undergraduate class advisor. I have taught several undergraduate courses including Machine Learning, Algorithm Analysis and Design, and Modern Image Processing Theory, as well as the graduate-level course Frontiers and Practices in the Discipline.

Over the past five years, I have published 12 academic papers, including 6 as the first author in SCI Top-tier, EI, or core journals. I have applied for 5 national invention patents and obtained 6 software copyrights. I currently lead one project funded by the National Natural Science Foundation of China (NSFC), one by the Natural Science Foundation of Hubei Province, one by Yichang City’s Scientific Research Program, and one industry-funded (horizontal) project. I also serve as a key member in two additional NSFC projects, one National Key R&D Program, and one Public Welfare Industry (Agriculture) Research Special Project.

I am a guest editor for the SCI Q2 journal Remote Sensing, and also serve as a reviewer for multiple SCI journals, including Remote Sensing of Environment.

My research interests include:

1.Remote sensing image processing
2.Artificial intelligence
3.Monitoring of agrometeorological disasters
4.Remote sensing-based information extraction of land surface features