授课教师：Wenzhi LIAO

国籍：比利时

职称：副教授

教师简介（中英文）：

中科院二区副主编

2020.02 至今，比利时佛兰德斯技术研究院(VITO)数据科学家(Data Scientist)；

2020.02 至今，比利时根特大学(Ghent University)教授,博士生导师；

2019.08-2020.01，英国Strathclyde University助理教授；

2016.01-2019.08，比利时根特大学(Ghent University)和欧洲微电子研究中心（IMEC）研究员 (FWO Research Fellow)；

2012.07-2016.09，比利时根特大学(Ghent University)博士后；

2009.10-2012.06，比利时根特大学博士研究生。

IEEE JSTARS Associate Editor

2020.02 to date,Belgian Flanders Technology Institute of Technology (VITO) Data scientist

2020.02 to date,Professor of Ghent University, Belgium,PhD Tutor;

2019.08-2020.01,Strathclyde University,United Kingdom assistant professor;

2016.01-2019.08,University of Ghent, Belgium And European Microelectronics Research Center researcher;

2012.07-2016.09,University of Ghent, Belgium Postdoctoral

课程简介（中英文）：

课程主要围绕遥感技术结合人工智能（AI）和大数据的实际应用。遥感数据在不同的应用研究中，受大数据如图像质量、样本质量和模型性能的影响较大，必须结合的多样性技术手段。因此课程首先讲解遥感在环境、土地利用、农业等不同领域中的重要性和应用情况，然后讲解随机森林和深度神经网络（包括最新的ChatGPT和Segment Anything Model）等典型的和热门的机器学习方法，最后介绍机器学习在遥感中的实际应用。

The course focuses on the practical applications of remote sensing technology combined with AI and big data. In many real applications of remote sensing, massive data were acquired, and their qualities, e.g., the received image quality, sample quality, and model performance have a greater impact, and a variety of technical means must be combined. Therefore, the course first explains the importance and application of remote sensing in different fields such as environment, land use, agriculture, etc., then explains typical and popular machine learning methods such as random forests and deep neural networks (including ChatGPT and Segment Anything Model), and finally introduces the practicality of machine learning in remote sensing. application.