Dr. Lei Ma



E-mail: lei.ma@tum.de
More information: Google Scholar


Curriculum Vitae

  • Since 03.2019, Alexander von Humboldt Research Fellow, TUM, Germany
  • Since 06.2016, Research Associate, School of Geographic and Oceanographic Sciences, Nanjing University
  • 03.2018 - 01.2019, Research Associate, Texas Tech University, USA
  • 12.2014 - 12.2015, Joint PhD. Student, Department of Geoinformatics - Z_GIS, University of Salzburg, Austria


  • Alexander von Humboldt Fellowship for postdoctoral researcher (2018)
  • Jiangsu Province-level outstanding Diploma thesis award (2017)
  • CSC scholarship (2014)

Editorial Activities

  • Guest editor, Special Issue on "Image Segmentation for Environmental Monitoring" (2018-2019), Remote Sensing.

Research Interests

  • Object-based Image Analysis
  • Land Cover and Land Use Change
  • High resolution image analysis
  • Time Series Analysis
  • GIS Application

Key Publications

  • Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G.,... Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166-177.
  • Ma, L. , Li, M. C., Ma, X. X. (2017): A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277-293. (ESI hot paper)

  • Ma, L. , Cheng, L., Li, M. C., Liu, Y., Ma, X. X. (2015): Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 14-27.
  • Li, M. C., Ma, L.*, Blaschke, T., Cheng, L., Tiede, D. (2016): A systematic comparison of different object-based classification techniques using high spatial resolution imagery. International Journal of Applied Earth Observation and Geoinformation, 49, 87-98. (ESI Highly Cited Paper)
  • Ma, L., Fu, T., Blaschke, T., et al. (2017): Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers. ISPRS International Journal of Geo-Information (2017) 6(2) 51. (2017 Highly Cited Paper in ISPRS IJGI)