Dr. Maximilian Kleebauer

• 2012 - 2015: Studied physics (B. Sc.) at the Philipps University of Marburg
• 2013 - 2019: Student assistant in the Department of Studies and Teaching at the Philipps University of Marburg
• 2015 - 2017: Studied Geography (B. Sc.) at the Philipps University of Marburg
• 2017: Bachelor thesis with the topic: "Spatial modeling of landslides in the Franconian Alb and identification and transferability of their influencing factors"
• 2017 - 2020: Study of Physical Geography with focus on Environmental Information Systems (M. Sc.) at the Philipps University of Marburg
• 2019 - 2021: Research assistant in the field of energy meteorology and geoinformation systems at the Fraunhofer Institute for Energy Economics and Energy System Technology, Kassel
• 2020: Master's thesis with the topic: "Development of a method for the detection of photovoltaic systems in high-resolution aerial images"
• 2025: Doctorate ( Dr. rer. nat.) (Topic: Remote Sensing based Renewable Energy System Recognition using Deep Learning), cooperation between Philipps University Marburg and University of Kassel & Fraunhofer IEE
• Since 2021: Research assistant in the field of energy meteorology and geoinformation systems at the Fraunhofer Institute for Energy Economics and Energy System Technology, Kassel
• Since 2022: Research assistant at the Department of Energy Management and Operation of Electrical Grids at the University of Kassel

• Geoinformation systems: development of intelligent data infrastructures to map the energy transition.
• Remote sensing: AI-supported analysis of satellite and aerial images for automated plant detection.
• Machine learning methods: Application of deep learning (CNN, GAN, SVM) to improve the quality of spatial data.
• Energy system analysis: Creation of high-precision master data registers for modeling renewable energies.

2026

• Hafdaoui, H., Kleebauer, M., Bouzekri, A., Belhaouas, N., Charki, A., & Bouchakour, S. (2026). Detection of Photovoltaic Power Plants in Satellite Images Using Artificial Intelligence Techniques. Next Research, 101385.

2025

• Kleebauer, M. (2025). Remote Sensing based Renewable Energy System Recognition using Deep Learning. Dissertation, Department of Geography, Philipps University Marburg.
• Kleebauer, M., Karamanski, S., Callies, D., & Braun, M. (2025). A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis. ISPRS International Journal of Geo-Information, 14(6), 232. https://doi.org/10.3390/ijgi14060232
• Botha, N., Coleman, T., Wessels, G., Kleebauer, M., & Karamanski, S. (2025). Power generation time series for solar energy generation: Modeling with ATlite in South Africa. Solar, 5(1), 8. https://doi.org/10.3390/solar5010008
• Kleebauer, M., Hafdaoui, H., Bouchakour, S., Häckner, B., & Lindenmeyer, M. (2025). Globally scalable, QGIS-integrated workflow for solar photovoltaic system segmentation and capacity estimation: A case study in Algeria. IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium, Brisbane, Australia, IEEE.
• Hirschmann, J., Rieß, B., Michaelis, L., Pauscher, L., Kleebauer, M., Basse, A., Geiger, D., Pape, C., & Callies, D. (2025). Automated time series-based loss calculation model for onshore wind turbines in Germany. 24th Wind & Solar Integration Workshop, Berlin.
• Häckner, B., Zink, C., Fetköter, J., & Kleebauer, M. (2025). Mapping Kenya's Green Ammonia Potential: A GIS-Based Assessment. 24th Wind & Solar Integration Workshop, Berlin.

2024

• Kleebauer, M., Zink, C., Krapf, S., Müller, U., Pogacar, S., Kucharczak, L., Petschelt, R., Wetzel, H., & Pape, C. (2024). Barometer of the energy transition for northern Hesse. Fraunhofer IEE. https://doi.org/10.24406/h-477852
• Kleebauer, M., Braun, A., Horst, D., & Pape, C. (2024). Enhancing wind turbine location accuracy: A deep learning-based object regression approach for validating wind turbine geo-coordinates. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 7863-7867. https://doi.org/10.1109/IGARSS53475.2024.10641018
• Kleebauer, M., Niemi, A., Putkonen, N., Kiviluoma, J., Boodhraj, K., van Reenen, T., Lindenmeyer, M., Dobschinski, J., & Braun, M. (2024). OASES: Open-Source and Data Strategy Report. Zenodo.https://doi.org/10.5281/zenodo.13365309
• Franken, L., Horst, D., & Kleebauer, M. (2024). Influence of building characteristics and socio-demographic factors on the suitability of roof areas for photovoltaic systems using SVM-One-Class classification. In J. Wittmann & M. Müller (Eds.), Simulation in Environmental and Geosciences (pp. 37-51). Shaker Verlag. https://doi.org/10.2370/9783844096767
• Kleebauer, M., Marz, C., & Horst, D. (2024). Sentinel-2 Super-resolution with Real-ESRGAN using satellite and aerial image pairs and color correction techniques. In KonKIS - Conference of the German AI Service Centers.https://doi.org/10.13140/RG.2.2.22240.70405

2023

• Kleebauer, M., Marz, C., Reudenbach, C., & Braun, M. (2023). Multi-resolution segmentation of solar photovoltaic systems using deep learning. Remote Sensing, 15(24), 5687. https://doi.org/10.3390/rs15245687
• Kleebauer, M., Horst, D., & Reudenbach, C. (2021). Semi-automatic generation of training samples for detecting renewable energy plants in high-resolution aerial images. Remote Sensing, 13(23), 4793. https://doi.org/10.3390/rs13234793