Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2025. Pattern Recognition – 46th DAGM German Conference, DAGM GCPR 2024, Munich, Germany, September 10–13, 2024, Proceedings, Part II. Ed.: D. Cremers, 348–364, Springer Nature Switzerland. doi:10.1007/978-3-031-85187-2_22

A comparative study on multi-task uncertainty quantification in semantic segmentation and monocular depth estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2025. tm - Technisches Messen. doi:10.1515/teme-2025-0004

Density uncertainty quantification with NeRF-Ensembles: Impact of data and scene constraints
Jäger, M.; Landgraf, S.; Jutzi, B.
2025. International Journal of Applied Earth Observation and Geoinformation, 137, Article no: 104406. doi:10.1016/j.jag.2025.104406

Semantic segmentation and uncertainty quantification with vision transformers for industrial applications
Wursthorn, K.; Gao, L.; Landgraf, S.; Ulrich, M.
2024. Forum Bildverarbeitung 2024. Hrsg.: T. Längle, M. Heizmann, 135 – 146, KIT Scientific Publishing. doi:10.58895/ksp/1000174496-12

A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2025. arxiv. doi:10.5445/IR/1000178524

A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation
Landgraf, S.; Qin, R.; Ulrich, M.
2025. arxiv. doi:10.48550/arXiv.2501.08188

Uncertainty-aware Cross-Entropy for Semantic Segmentation
Landgraf, S.; Hillemann, M.; Wursthorn, K.; Ulrich, M.
2024. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-2-2024, 129–136. doi:10.5194/isprs-annals-X-2-2024-129-2024

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, 92 (2), 101–114. doi:10.1007/s41064-024-00280-4

Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2024. arxiv. doi:10.48550/arXiv.2402.10580

U-CE: Uncertainty-aware Cross-Entropy for Semantic Segmentation
Landgraf, S.; Hillemann, M.; Wursthorn, K.; Ulrich, M.
2023. arxiv. doi:10.48550/arXiv.2307.09947

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

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

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

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

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

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