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M.Sc. Maximilian Kleebauer

Research assistant

Telephone
+49 561 7294-1585
Email

Career  (M.Sc. Maximilian Kleebauer)

  • 2012 - 2015: Studies of physics (B. Sc.) at the Philipps-University Marburg
  • 2013 - 2019: Student assistant in the department of studies and teaching at Philipps-University Marburg
  • 2015 - 2017: Study of geography (B. Sc.) at the Philipps-University Marburg
  • 2017: Bachelor thesis with the topic: "Spatial modeling of landslides in the Franconian Alb and identification as well as transferability of their influencing factors"
  • 2017 - 2020: Studies of physical geography with focus on environmental information systems (M. Sc.) at the Philipps-University 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, Germany
  • 2020: Master thesis with the topic: "Development of a method for the detection of photovoltaic plants in high-resolution aerial images"
  • Since 2021: Research assistant in the field of energy meteorology and geoinformation systems at the Fraunhofer Institute for Energy Economics and Energy Systems Technology, Kassel
  • Since2022: Research associate at the Department of Energy Management and Operation of Electrical Networks at the University of Kassel

Research focus  (M.Sc. Maximilian Kleebauer)

  • Geoinformation systems
  • Remote Sensing
  • Machine learning methods
  • Energy system analysis

Publications  (M.Sc. Maximilian Kleebauer)

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

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