Dr. Nafiseh Kakhani, Eberhard Karls University of Tübingen, Germany
Dr. Nafiseh Kakhani is a researcher and lecturer at Eberhard Karls University of Tübingen, Germany, specializing in Earth observation, remote sensing, and machine learning. She holds a PhD in Remote Sensing Engineering from K. N. Toosi University of Technology, Tehran, where she focused on spectral-spatial classification of high-resolution multispectral images. Her research interests include AI-driven geospatial analysis, explainable machine learning, digital soil mapping, and land cover classification. She has extensive experience in deep learning, computer vision, and statistical modeling for environmental applications. Dr. Kakhani has received prestigious awards, including the Best Poster Award at the 2023 IEEE IADF-GRSS School, and has published widely in her field.
Scopus
Summary:
Dr. Kakhani is a strong candidate for the Best Researcher Award due to her innovative contributions to explainable AI applications in soil science. Her interdisciplinary research and ability to integrate AI-driven approaches into environmental studies make her work highly relevant and impactful. She has contributed significantly to understanding soil organic carbon dynamics, a crucial factor in climate change mitigation and sustainable agriculture.
🎓 Education
Nafiseh Kakhani holds a PhD in Remote Sensing Engineering from K N Toosi University of Technology in Tehran, Iran, where she specialized in spectral-spatial classification of high-resolution multispectral images using segmentation methods. She completed her MSc in Remote Sensing Engineering from the same university, focusing on integrating neural networks and fuzzy systems to improve classification methods in hyperspectral images. She earned her BSc in Geomatics and Geodesy Engineering from the University of Isfahan, where she developed a statistical classification method for Multi-Beam Echo Sounder data.
💼Experience
She is currently an Earth Observation and Machine Learning Scientist at The LandBanking Group GmbH in Munich, where she applies machine learning techniques to environmental monitoring and land assessment. Previously, she was a Postdoctoral Researcher and Lecturer at the University of Tübingen, where she conducted research in remote sensing and taught courses on deep learning, photogrammetry, and the fundamentals of remote sensing. She has also worked at the Iranian Space Agency, focusing on remote sensing sensor calibration and spectral library preparation. Additionally, she has mentored Master’s students in GIS, remote sensing, and machine learning and has been a guest speaker at international workshops and conferences.
🔬Research Focus
Her research focuses on computer vision for Earth observation, explainable machine learning, deep learning, statistical machine learning, pattern recognition, and Earth system science. She has worked extensively on AI-driven geospatial analysis, digital soil mapping, land cover classification, and the development of deep learning frameworks for remote sensing applications.
🛠️Skills
She is proficient in Python, using libraries such as PyTorch, TensorFlow, and Scikit-learn, as well as MATLAB and JavaScript for Google Earth Engine applications. She has expertise in geospatial software, including ArcGIS, QGIS, ENVI, ERDAS IMAGINE, and Agisoft Metashape. She is experienced in cloud computing platforms such as Google Cloud, AWS, and Tübingen ML Cloud, enabling her to process large-scale remote sensing datasets efficiently.
🏆Awards
She received the Best Poster Award at the 2023 IEEE IADF-GRSS School for her work on SoilNet, a spatio-temporal deep learning framework for digital soil mapping. She was a top-ranked graduate student in both her MSc and BSc programs and was admitted to her MSc without an entrance exam, a privilege reserved for exceptional students in Iran.
📚 Publications
Publication: Towards Explainable AI: Interpreting Soil Organic Carbon Prediction Models Using a Learning-Based Explanation Method
Authors: N. Kakhani, R.T. Mehrjardi, D. Omarzadeh, U. Heiden, T.J. Scholten
Journal: European Journal of Soil Science
Year: 2025
Conclusion:
Dr. Nafiseh Kakhani is a highly suitable candidate for the Best Researcher Award. While there is always room for further growth, her expertise and achievements in applying AI to soil science place her among the leading researchers in this field.