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Dr. Hamid Ebrahimy
Universität Osnabrück
Joint Lab Künstliche Intelligenz & Data Science
Coppenrath Innovation Centre
Joint Lab KI & DS
Hamburger Straße 24, LOK 15
49084 Osnabrück
Tel.: +49 541 969-6341
Room: 01.13.02A
Profiles
Research Interests
1. Satellite Remote Sensing for Landcover and Crop Mapping
2. Applications of Artificial Intelligence in Remote Sensing Data Analysis
3. Map Accuracy Assessment
4. Data Driven Algorithms for Small Sample Size Problem
Selected Publications
Ebrahimy, H., & Zhang, Z. (2023). Per-pixel accuracy as a weighting criterion for combiningensemble of extreme learning machine classifiers for satellite image classification.International Journal of Applied Earth Observation and Geoinformation, 122, 103390.
Ebrahimy, H., Wang, Y., & Zhang, Z. (2023). Utilization of synthetic minority oversamplingtechnique for improving potato yield prediction using remote sensing data and machinelearning algorithms with small sample size of yield data. ISPRS Journal of Photogrammetry andRemote Sensing, 201, 12-25.
Ebrahimy, H., Mirbagheri, B., Matkan, A. A, & Azadbakht, M. (2022). Effectiveness of theintegration of data balancing techniques and tree-based ensemble machine learningalgorithms 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 coveraccuracy 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 ForestRegression 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-decadalLandsat imagery and a sample migration technique within Google Earth Engine. Internationaljournal of applied earth observation and Geoinformation, 105, 102607.
Naboureh, A., Ebrahimy, H., Azadbakht, M., Bian, J., & Amani, M. (2020). RUESVMs: AnEnsemble Method to Handle the Class Imbalance Problem in Land Cover Mapping UsingGoogle Earth Engine. Remote Sensing, 12(21), 3484.
