- 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)
- Guest editor, Special Issue on "Image Segmentation for Environmental Monitoring" (2018-2019), Remote Sensing.
- Object-based Image Analysis
- Land Cover and Land Use Change
- High resolution image analysis
- Time Series Analysis
- GIS Application
- 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)