Directory Listing
Ali
Gurbuz
Center for Advanced Vehicular Systems
Faculty
Gurbuz
Center for Advanced Vehicular Systems
Faculty
Research Interest
Signal processing & Machine Learning
Deep learning-based Inverse Problems and Signal Processing
Computational imaging, Sparse Signal Processing, Compressive Sensing
Machine Learning for Autonomous Systems, Off-Road Autonomy
UAV based Smart Sensing Systems
Machine Learning for Radar and Remote Sensing Systems
Radar and Array Signal Processing
Deep learning-based Inverse Problems and Signal Processing
Computational imaging, Sparse Signal Processing, Compressive Sensing
Machine Learning for Autonomous Systems, Off-Road Autonomy
UAV based Smart Sensing Systems
Machine Learning for Radar and Remote Sensing Systems
Radar and Array Signal Processing
Selected Publications
Total Publications: 12
Senyurek, V., Farhad, M. M., Gurbuz, A., Kurum, M., & Adeli, A. (2022). Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6843-6855. DOI:10.1109/JSTARS.2022.3197794. [Abstract] [Document Site]
Nabi, M., Senyurek, V., Gurbuz, A., & Kurum, M. (2022). Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay-Doppler Maps. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6876-6881. DOI:10.1109/JSTARS.2022.3196658. [Abstract] [Document Site]
Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Boyd, D., Moorhead, R. J., & Crow, W. T. (2022). Quasi-global Machine Learning-based Soil Moisture Estimates at High Spatio-temporal Scales Using CYGNSS and SMAP Observations. Remote Sensing of Environment. Elsevier. 276, 113041. DOI:10.1016/j.rse.2022.113041. [Abstract] [Document Site]
Senyurek, V., Lei, F., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). Machine Learning-based Global Soil Moisture Estimation Using GNSS-R. SoutheastCon 2022. Mobile, AL, USA: IEEE. 434-435. DOI:10.1109/SoutheastCon48659.2022.9764039. [Abstract] [Document Site]
Senyurek, V., Farhad, M., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). SoilMoistureMapper: a GNSS-R Approach for Soil Moisture Retrieval on UAV. UAAAI-22 AI for Agriculture and Food Systems (AIAFS) Workshop. Vancouver, BC (Canada). [Abstract] [Document Site]
Nabi, M., Senyurek, V., Gurbuz, A., & Kurum, M. (2022). Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay-Doppler Maps. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6876-6881. DOI:10.1109/JSTARS.2022.3196658. [Abstract] [Document Site]
Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Boyd, D., Moorhead, R. J., & Crow, W. T. (2022). Quasi-global Machine Learning-based Soil Moisture Estimates at High Spatio-temporal Scales Using CYGNSS and SMAP Observations. Remote Sensing of Environment. Elsevier. 276, 113041. DOI:10.1016/j.rse.2022.113041. [Abstract] [Document Site]
Senyurek, V., Lei, F., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). Machine Learning-based Global Soil Moisture Estimation Using GNSS-R. SoutheastCon 2022. Mobile, AL, USA: IEEE. 434-435. DOI:10.1109/SoutheastCon48659.2022.9764039. [Abstract] [Document Site]
Senyurek, V., Farhad, M., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). SoilMoistureMapper: a GNSS-R Approach for Soil Moisture Retrieval on UAV. UAAAI-22 AI for Agriculture and Food Systems (AIAFS) Workshop. Vancouver, BC (Canada). [Abstract] [Document Site]