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Prof. Ali Mohammadzadeh | Remote Sensing Award | Best Researcher Award

Prof. Ali Mohammadzadeh, K.N.Toosi University of Technology, Iran

Prof. Ali Mohammadzadeh is an accomplished educator and researcher specializing in LiDAR technology šŸŒ and remote sensing applications in disaster management šŸŒ. With a Ph.D. in Civil-Surveying Engineering, he leads the LiDAR Laboratory at K.N. Toosi University of Technology. His expertise includes artificial intelligence šŸ¤–, image processing, and calibration techniques. Serving as Faculty Vice-Dean and Associate Professor, he has extensive experience in academia, previously heading departments and contributing to various research projects. Prof. Mohammadzadeh’s work encompasses diverse fields such as bio-geomatics and evolutionary optimization, making significant contributions to both academia and industry.

 

Publication Profile

Orcid

Education šŸ“š

Prof. Ali Mohammadzadeh holds a Ph.D. in Civil-Surveying Engineering, earned in 2009 from K. N. Toosi University of Technology, Iran. Prior to this, he completed an M.Sc. (2003) and B.S. (2000) in Civil-Remote Sensing Engineering from the same institution.

Professional Experiences šŸ’¼

Prof. Mohammadzadeh has held key roles at K.N.Toosi University of Technology, including Faculty Vice-Dean, Associate Professor, and Head of the LiDAR Laboratory and Photogrammetry & Remote Sensing Department.

Awards and Honors šŸ†

Ali Mohammadzadeh’s journey is marked by a trail of academic excellence and prestigious awards. From being recognized as an outstanding student at the National Intellect School to securing top ranks in Geomatics & Geodesy and Remote Sensing, his dedication has been unwavering. Throughout his career, he has been honored with multiple Remote Sensing Exemplary Researcher Awards, showcasing his exceptional contributions to the field. Notably, he received scholarships for PhD studies and international training, highlighting his commitment to continuous learning and global collaboration. His achievements culminated in being selected as one of the top researchers at K.N.Toosi University, a testament to his remarkable scholarly pursuits. šŸ…

Teaching ExperiencesĀ šŸŽ“

Prof. Ali Mohammadzadeh brings a wealth of teaching experience spanning over a decade, enriching the academic landscape with his expertise. His courses cover a broad spectrum, from foundational Principles of Remote Sensing to advanced topics like Optimization and Fuzzy Logic in Remote Sensing. With a focus on practical applications, he nurtures students in areas such as Airborne and Terrestrial LiDAR Technologies, equipping them with essential skills for real-world challenges. Prof. Mohammadzadeh’s commitment to mentorship shines through the graduation of numerous M.Sc. and Ph.D. students, supported by a diverse network of co-supervisors. His dedication extends to establishing cutting-edge laboratory facilities, fostering a collaborative environment for research and innovation. šŸŽ“

Research Focus

Prof. Ali Mohammadzadeh’s research focus spans a wide array of topics in remote sensing and geomatics, blending technological innovation with practical applications šŸ›°ļø. His work encompasses post-disaster assessment using UAV data, relative radiometric normalization of remote sensing images, and forest change detection utilizing satellite imagery šŸŒ³. He explores cutting-edge methods such as deep learning and machine learning algorithms to enhance data analysis and interpretation. Mohammadzadeh’s contributions extend to diverse domains including disaster management, environmental monitoring, and urban planning, reflecting his commitment to leveraging technology for societal benefit while advancing scientific knowledge šŸŒ.

Publication Top Notes

A Novel Weighted Ensemble Transferred U-Net Based Model (WETUM) for Postearthquake Building Damage Assessment From UAV Data: A Comparison of Deep Learning- and Machine Learning-Based Approaches

LIRRN: Location-Independent Relative Radiometric Normalization of Bitemporal Remote-Sensing Images

Synthetic Aperture Radar (SAR) for Ocean: A Review

BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images

Enhancing suspended sediment concentration retrieval by integrating thermal infrared and optical bands of Landsat-8 and machine learning algorithms

Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features

FCD-R2U-net: Forest change detection in bi-temporal satellite images using the recurrent residual-based U-net

Satellite-based monitoring of lake Urmia basin health with focus on its ecosystem regulating services by applying total ecosystem health (TEH) model

Individual tree detection from unmanned aerial vehicle (UAV) derived point cloud data in a mixed broadleaf forest using hierarchical graph approach

A Rapid Self-Supervised Deep-Learning-Based Method for Post-Earthquake Damage Detection Using UAV Data (Case Study: Sarpol-e Zahab, Iran)

 

 

 

Ali Mohammadzadeh | Remote Sensing Award | Best Researcher Award

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