Steven Landgraf, M.Sc.

Steven Landgraf, M.Sc.

Steven Landgraf

Curriculum Vitae

  • Since September 2021: PhD student at the Institute of Photogrammetry and Remote Sensing
  • Since September 2020: Research associate at the Geodetic Institute
  • 2018 to 2020: Master of Science
  • 2015 to 2018: Bachelor of Science

Research Interests

  • Image Segmentation with Deep Learning
  • Uncertainty Quantification in Deep Learning

Theses

  • Jung, Valentin (2022): Segmentation of Flames in Infrared Images with Traditional Image Processing [done]
  • Aberle, Moritz (2023): Segmentation of Flames in Infrared Images with Traditional Machine Learning [done]
  • Kapler, Theo (2023): Simultaneous Segmentation and Monocular Depth Estimation with Deep Learning [ongoing]
  • Gao, Lili (2024): Uncertainty Estimation with Vision Transformers in an Industrial Context [ongoing]

Teaching

  • Seminar Geodesy and Geoinformatics

Publikationen


2024
DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation
Landgraf, S.; Wursthorn, K.; Hillemann, M.; Ulrich, M.
2024. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. doi:10.1007/s41064-024-00280-4
2023
Improving data quality and management for remote sensing analysis : use-cases and emerging research questions
Breunig, M.; Kuper, P.; Reitze, F.; Landgraf, S.; Al-Doori, M.; Stefanakis, E.; Abdulmuttalib, H.; Kugler, Z.
2023. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences : Geospatial Week 2023, 2–7 September 2023, Cairo, Egypt, X-1 (W1), 41 – 49. doi:10.5194/isprs-annals-X-1-W1-2023-41-2023
Dataset for the Segmentation of Industrial Burner Flames
Landgraf, S.; Hillemann, M.; Ulrich, M.; Aberle, M.; Jung, V.
2023, June 20. doi:10.5445/IR/1000159497
Segmentation of industrial burner flames: a comparative study from traditional image processing to machine and deep learning
Landgraf, S.; Hillemann, M.; Aberle, M.; Jung, V.; Ulrich, M.
2023. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023, 953–960. doi:10.5194/isprs-annals-X-1-W1-2023-953-2023
Segmentation of Industrial Burner Flames: A Comparative Study from Traditional Image Processing to Machine and Deep Learning
Landgraf, S.; Hillemann, M.; Aberle, M.; Jung, V.; Ulrich, M.
2023. doi:10.5445/IR/1000159876
DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation
Landgraf, S.; Wursthorn, K.; Hillemann, M.; Ulrich, M.
2023
2022
Semantic segmentation with small training datasets: A case study for corrosion detection on the surface of industrial objects
Haitz, D.; Hübner, P.; Ulrich, M.; Landgraf, S.; Jutzi, B.
2022. Forum Bildverarbeitung 2022. Ed.: T. Längle; M. Heizmann, 73–85, KIT Scientific Publishing
Evaluation of self-supervised learning approaches for semantic segmentation of industrial burner flames
Landgraf, S.; Kühnlein, L.; Hillemann, M.; Hoyer, M.; Keller, S.; Ulrich, M.
2022. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2022, 601–607. doi:10.5194/isprs-archives-XLIII-B2-2022-601-2022
2021
Geodäsie und Geoinformatik am KIT studieren
Dalheimer, L.; Fuge, R.; Gschwind, C.; Juretzko, M.; Landgraf, S.; Meid, F.; Naab, C.; Ulrich, M.; Weisgerber, J.
2021. doi:10.5445/IR/1000137359
2019
Evaluation of the Microsoft HoloLens for the Mapping of Indoor Building Environments
Hübner, P.; Landgraf, S.; Weinmann, M.; Wursthorn, S.
2019. 39. Wissenschaftlich-Technische Jahrestagung der DGPF - Dreiländertagung OVG – DGPF – SGPF - Photogrammetrie - Fernerkundung - Geoinformation, Wien, 20 - 22. Februar 2019. Ed. T. B. Kersten, 44–53, Deutsche Gesellschaft für Photogrammetrie