Qingyu Li

This is Qingyu Li’s personal homepage. Please also refer to my homepage in Technical University of Munich.

A short introduction

I am a Postdoctoral Researcher in the chair of Data Science in Earth Observation, Technical University of Munich working in the field of remote sensing. I received my Ph.D. from Technical University of Munich. The topic of my dissertation is “Deep Learning for Building Footrpint Generation from Optical Imagery”. I received my Bachelor degree in Remote Sensing Science and Technology from Wuhan University, China; I obtained my double Master Degree, which are (1) ESPACE, Technical University of Munich, Germany and (2) Photogrammetry and remote sensing, Wuhan University, China.

Research Interests:

• Remote sensing image understanding
• Remote sensing application
• Urban analysis
• Deep learning algorithms

Research Highlights:

• Development of novel deep learning alogrithms for building footprint generation
• Development of a novel deep learning alogrithm for building height retrieval
• Development of a novel deep learning-based framework for undocumented building detection and its application in urban analysis
• Development of a novel deep learning-based framework framework for rooftop solar potential analysis
• Development of a novel machine learning-based framework for land cover classification
• Development of a novel machine learning algorithm for change detetction

Selected Journal Publications

Li, Qingyu, Lichao Mou, Yao Sun, Yuansheng Hua, Yilei Shi, and Xiao Xiang Zhu. “A Review of Building Extraction from Remote Sensing Imagery: Geometrical Structures and Semantic Attributes.” IEEE Transactions on Geoscience and Remote Sensing 60 (2024): 1-15. link

Li, Qingyu, Lichao Mou, Yuansheng Hua, Yilei Shi, Sining Chen, Yao Sun and Xiao Xiang Zhu. “3DCentripetalNet: Building height retrieval from monocular remote sensing imagery.” International Journal of Applied Earth Observation and Geoinformation 120 (2023): 103311.link

Li, Qingyu, Sebastian Krapf, Yilei Shi, and Xiao Xiang Zhu. “SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery.” International Journal of Applied Earth Observation and Geoinformation 116 (2023): 103098.link

Li, Qingyu, Hannes Taubenböck, Yilei Shi, Stefan Auer, Robert Roschlaub, Clemens Glock, Anna Kruspe, and Xiao Xiang Zhu. “Identification of undocumented buildings in cadastral data using remote sensing: Construction period, morphology, and landscape.” International Journal of Applied Earth Observation and Geoinformation 112 (2022): 102909.link

Li, Qingyu, Yilei Shi, and Xiao Xiang Zhu. “Semi-supervised building footprint generation with feature and output consistency training.” IEEE Transactions on Geoscience and Remote Sensing (2022). link

Li, Qingyu, Lichao Mou, Yuansheng Hua, Yilei Shi, and Xiao Xiang Zhu. “CrossGeoNet: A Framework for Building Footprint Generation of Label-Scarce Geographical Regions.” International Journal of Applied Earth Observation and Geoinformation 111 (2022): 102824. link

Li, Qingyu, Lichao Mou, Yuansheng Hua, Yilei Shi, and Xiao Xiang Zhu. “Building footprint generation through convolutional neural networks with attraction field representation.” IEEE Transactions on Geoscience and Remote Sensing 60 (2021): 1-17. link

Li, Qingyu, Yilei Shi, Xin Huang, and Xiao Xiang Zhu. “Building footprint generation by integrating convolution neural network with feature pairwise conditional random field (FPCRF).” IEEE Transactions on Geoscience and Remote Sensing 58, no. 11 (2020): 7502-7519. link

Li, Qingyu, Yilei Shi, Stefan Auer, Robert Roschlaub, Karin Möst, Michael Schmitt, Clemens Glock, and Xiaoxiang Zhu. “Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network.” Remote Sensing 12, no. 21 (2020): 3537. link

Li, Qingyu, Chunping Qiu, Lei Ma, Michael Schmitt, and Xiao Xiang Zhu. “Mapping the land cover of Africa at 10 m resolution from multi-source remote sensing data with Google Earth Engine.” Remote Sensing 12, no. 4 (2020): 602. link

For more information

More info about Qingyu Li can be found in CV or downloaded CV.