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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-2/W11-2025
https://doi.org/10.5194/isprs-archives-XLVIII-2-W11-2025-211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-2-W11-2025-211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 Oct 2025
| 30 Oct 2025
Addressing GNSS Vulnerabilities in AAM: A Multi-Modal UAV Testbed for Redundant and Reliable Navigation
Felix Rocco Matzke
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Dresden, Germany
Tushar Jayesh Barot
Institute of Logistics and Aviation, TUD Dresden University of Technology, Dresden, Germany
Shreeja Sridharan
Deutsche Telekom Chair of Communication Networks, TUD Dresden University of Technology, Dresden, Germany
Nikolaus Ammann
Institute of Flight Systems, German Aerospace Center (DLR), Braunschweig, Germany
Christoph Kessler
Institute of Flight Systems, German Aerospace Center (DLR), Braunschweig, Germany
Hans-Gerd Maas
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Dresden, Germany
Frank H. P. Fitzek
Deutsche Telekom Chair of Communication Networks, TUD Dresden University of Technology, Dresden, Germany
Anette Eltner
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Dresden, Germany
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The present study offers a unique, nested, high-resolution dataset that captures soil surface changes every 20 seconds during rainfall events, as well as over seasonal variations, across a range of spatial scales – plot, slope, and catchment. These data collected over a period of 3.5 years was facilitated by a camera-based approach. This open-access resource assists scientists in evaluating and refining existing models, improving process understanding, and training artificial intelligence.
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Revised manuscript not accepted
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Intensified extreme weather events due to climate change can result in changes of soil erosion. These unclear developments make an improvement of soil erosion modelling all the more important. Assuming that soil erosion models cannot keep up with the current data, this work gives an overview of 44 models, their strengths and weaknesses and discusses their potential for further development with respect to new and improved soil and soil erosion assessment techniques.
F. Liebold, R. Lorenzoni, I. Curosu, F. Léonard, V. Mechtcherine, S. Paciornik, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 533–539, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-533-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-533-2021, 2021
K. Richter, D. Mader, P. Westfeld, and H.-G. Maas
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A. Eltner, D. Mader, N. Szopos, B. Nagy, J. Grundmann, and L. Bertalan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 717–722, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-717-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-717-2021, 2021
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