Tao Liu

Assistant Professor of Precision Forestry

Professional Experience

Assistant Professor of Smart Forestry, University of Georgia, Aug 2025–Present
Assistant Professor of GIS and Remote Sensing,Michigan Technological University, Aug 2020–June 2025
Postdoc Research Associate in Geospatial Computation, Oak Ridge National Laboratory, Aug 2018–Aug 2020

Education

Education:

PhD, University of Florida, Geomatics, 2018
MS, University of Florida, Statistics, 2018
MS, ESF State University of New York, RS and GIS Engineering, 2014
BS, Northwest A&F University, Forestry, 2011

Research

Selected Publications:

Ma, L., Yan, Z., Li, M., Liu, T., Tan, L., Wang, X., ... & Blaschke, T. (2024). Deep Learning Meets Object-Based Image Analysis: Tasks, challenges, strategies, and perspectives. IEEE Geoscience and Remote Sensing Magazine.
Kayastha, M.B., Liu, T., Titze, D., Havens, T.C., Huang, C. and Xue, P. (2023). Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach. Remote Sensing, 15(17), p.4253.
Zheng, C., Liu, T., Abd-Elrahman, A., Whitaker, V.M. and Wilkinson, B. (2023). Object-Detection from Multi-View remote sensing Images: A case study of fruit and flower detection and counting on a central Florida strawberry farm. International Journal of Applied Earth Observation and Geoinformation, 123, p.103457.
Monahan, W.B., Arnspiger, C.E., Bhatt, P., An, Z., Krist, F.J., Liu, T., Richard, R.P., Edson, C., Froese, R.E., Steffenson, J. and Lammers, T.C. (2022). A spectral three-dimensional color space model of tree crown health. PLOS ONE, 17(10), p.e0272360.
Qin, R., Liu, T. (2022). A Review of Landcover Classification with Very-high Resolution Remotely Sensed Optical Images – Analysis Unit, Model Scalability and Transferability. Remote Sensing, 14(3), 646.
Liu, T., Yang, L., and Lunga, D.D. (2021). Change Detection Using Deep Learning Approach with Object-based Image Analysis. Remote Sensing of Environment, 256, 112308.
Liu, T., Abd-Elrahman, A., Dewitt, B., Smith, S., Morton, J. and Wilhelm, V.L. (2019). Evaluating the potential of multi-view data extraction from small Unmanned Aerial Systems (UASs) for object-based classification for Wetland land covers. GIScience & Remote Sensing, 56(1), pp.130-159.
Liu, T., Abd-Elrahman, A., Zare, A., Dewitt, B.A., Flory, L. and Smith, S.E. (2018). A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems. Remote Sensing of Environment, 216, pp.328-344.
Liu, T. and Abd-Elrahman, A. (2018). Multi-view object-based classification of wetland land covers using unmanned aircraft system images. Remote Sensing of Environment, 216, pp.122-138.
Liu, T. and Abd-Elrahman, A. (2018). Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification. ISPRS Journal of Photogrammetry and Remote Sensing, 139, pp.154-170.