Hamid Ebrahimy

Position title: Research Associate

Email: ebrahimy@wisc.edu

233 Agricultural Engineering Building
460 Henry mall
Madison, WI 53706

Headshot of Hamid

Program Affiliations

Biological Systems Engineering

Education and Certificates

Ph.D. 2022 -Remote Sensing and GIS, Shahid Beheshti University

Fields of Interest

  • Machine learning algorithms
  • Map accuracy assessment
  • Satellite image classification and downscaling


  • Ebrahimy, H., Mirbagheri, B., Matkan, A. A, & Azadbakht, M. (2022). Effectiveness of the integration of data balancing techniques and tree-based ensemble machine learning algorithms for spatially-explicit land cover accuracy prediction. Remote Sensing Applications: Society and Environment, 27, 100785.
  • Ebrahimy, H., Mirbagheri, B., Matkan, A. A, & Azadbakht, M. (2021). Per-pixel land cover accuracy prediction: A random forest-based method with limited reference sample data. ISPRS Journal of Photogrammetry and Remote Sensing, 172, 17-27.
  • Ebrahimy, H., Aghighi, H., Azadbakht, M., Amani, M., Mahdavi, S., & Matkan, A. A. (2021). Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis over Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2103-2112.
  • Naboureh, A., Li, A., Ebrahimy, H., Bian, J., Azadbakht, M., Amani, M., Lei, G., & Nan, X. (2021). Assessing the effects of irrigated agricultural expansions on Lake Urmia using multi-decadal Landsat imagery and a sample migration technique within Google Earth Engine. International journal of applied earth observation and Geoinformation, 105, 102607.
  • Naboureh, A., Ebrahimy, H., Azadbakht, M., Bian, J., & Amani, M. (2020). RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine. Remote Sensing, 12(21), 3484.
  • Ebrahimy, H., & Azadbakht, M. (2019). Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels. Computers & geosciences, 124, 93-102.