Hanqing Bao | Land cover | Best Researcher Award

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Mr. Hanqing Bao, Land cover, Best Researcher Award

Hanqing Bao at Ludwig-Maximilians-Universität, Germany

Summary:

Mr. Hanqing Bao is an accomplished researcher and Ph.D. candidate specializing in Physical Geography and Environmental Remote Sensing at Ludwig-Maximilians-Universität (Munich). With extensive experience in remote sensing and deep learning, his work focuses on the intelligent processing and analysis of remote sensing images to enhance urban planning and environmental monitoring.

Professional Profile:

👩‍🎓Education:

Ph.D. Candidate in Physical Geography and Environmental Remote Sensing

  • Ludwig-Maximilians-Universität (Munich) (2020 – Present)
  • Research Team: Physical Geography and Environmental Remote Sensing
  • Supervisor: Prof. Dr. Lukas Lehnert

Master’s Degree in Surveying Science and Technology (Remote Sensing)

  • China University of Geosciences (Beijing) (2017 – 2020)
  • Research Team: Intelligent Remote Sensing Image Processing and Analysis
  • Supervisor: Dr. Dongping Ming

Bachelor’s Degree in Surveying and Mapping Engineering

  • Lanzhou Jiaotong University (2013 – 2017)

Professional Experience:

As a Ph.D. candidate at Ludwig-Maximilians-Universität, Mr. Bao has been integral to research initiatives focusing on the application of deep learning in remote sensing. His research encompasses the development of advanced techniques for land use change detection, high-resolution image processing, and the exploration of urban spatial structures. His work leverages the capabilities of convolutional neural networks (CNN) and graph convolutional networks (GCN) to improve the accuracy and efficiency of remote sensing data analysis.

Research Interest:

Mr. Bao’s research interests lie at the intersection of remote sensing, deep learning, and urban planning. Key areas of focus include:

  • Land Use Change Detection: Utilizing Siamese Networks to detect changes in land use over time, aiding in sustainable urban development.
  • High-Resolution Image Processing: Applying deep learning techniques to process and analyze very high-resolution remote sensing images.
  • Scale Effect Mitigation: Developing methods to reduce the impact of scale effect on remote sensing image information extraction through stratification and spatial statistics.
  • Intelligent Image Understanding: Segmentation, classification, and semantic recognition of remote sensing images to extract meaningful information about natural and built environments.

Mr. Bao’s contributions to the field of remote sensing are aimed at enhancing our understanding of urban environments and supporting effective environmental management practices.

Publications Top Noted: 

Title: DFCNN-based semantic recognition of urban functional zones by integrating remote sensing data and POI data

  • Authors: Hanqing Bao, Dongping Ming, Yuan Guo, et al.
  • Journal: Remote Sensing
  • Year: 2020
  • Volume: 12
  • Issue: 7

Title: SO–CNN based urban functional zone fine division with VHR remote sensing image

  • Authors: Wenchao Zhou, Dongping Ming, Xudong Lv, et al.
  • Journal: Remote Sensing of Environment
  • Year: 2020
  • Volume: 236

Title: Farmland extraction from high spatial resolution remote sensing images based on stratified scale pre-estimation

  • Authors: Ling Xu, Dongping Ming, Wenchao Zhou, et al.
  • Journal: Remote Sensing
  • Year: 2019
  • Volume: 11
  • Issue: 2

Title: A new method for region-based majority voting CNNs for very high resolution image classification

  • Authors: Xudong Lv, Dongping Ming, Tian Lu, et al.
  • Journal: Remote Sensing
  • Year: 2018
  • Volume: 10
  • Issue: 12

Title: Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

  • Authors: Wenchao Zhou, Dongping Ming, Ling Xu, et al.
  • Journal: Journal of Spectroscopy
  • Year: 2018
  • Volume: 2018