- Article
(5004 KB) - Metadata XML
- November 12, 2024
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B2-2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-383-2020
© Author(s) 2020. 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-XLIII-B2-2020-383-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
12 Aug 2020
| 12 Aug 2020
LONG-SHORT SKIP CONNECTIONS IN DEEP NEURAL NETWORKS FOR DSM REFINEMENT
K. Bittner
Remote Sensing Technology Institute, German Aerospace Center (DLR), Wessling, Germany
L. Liebel
Computer Vision Research Group, Chair of Remote Sensing Technology (LMF), Technical University of Munich (TUM), Munich, Germany
M. Körner
Computer Vision Research Group, Chair of Remote Sensing Technology (LMF), Technical University of Munich (TUM), Munich, Germany
P. Reinartz
Remote Sensing Technology Institute, German Aerospace Center (DLR), Wessling, Germany
Related authors
Shengxi Gui, Philipp Schuegraf, Ksenia Bittner, and Rongjun Qin
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 81–88, https://doi.org/10.5194/isprs-annals-X-2-2024-81-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-81-2024, 2024
Daniel Panangian and Ksenia Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 185–192, https://doi.org/10.5194/isprs-annals-X-2-2024-185-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-185-2024, 2024
Philipp Schuegraf, Zhixin Li, Jiaojiao Tian, Jie Shan, and Ksenia Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 217–222, https://doi.org/10.5194/isprs-annals-X-2-2024-217-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-217-2024, 2024
G. Mutreja and K. Bittner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 315–321, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-315-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-315-2023, 2023
K. Bittner, S. Zorzi, T. Krauß, and P. d’Angelo
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 925–933, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-925-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-925-2023, 2023
P. Schuegraf, M. Fuentes Reyes, Y. Xu, and K. Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 971–979, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-971-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-971-2023, 2023
P. Schuegraf, J. Schnell, C. Henry, and K. Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 407–414, https://doi.org/10.5194/isprs-annals-V-2-2022-407-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-407-2022, 2022
K. Bittner, P. d’Angelo, M. Körner, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 103–108, https://doi.org/10.5194/isprs-archives-XLII-2-103-2018, https://doi.org/10.5194/isprs-archives-XLII-2-103-2018, 2018
Basil Kraft, Jacob A. Nelson, Sophia Walther, Fabian Gans, Ulrich Weber, Gregory Duveiller, Markus Reichstein, Weijie Zhang, Marc Rußwurm, Devis Tuia, Marco Körner, Zayd Mahmoud Hamdi, and Martin Jung
EGUsphere, https://doi.org/10.5194/egusphere-2024-2896, https://doi.org/10.5194/egusphere-2024-2896, 2024
Short summary
Short summary
Global evapotranspiration (ET) can be estimated using machine learning (ML) models optimized on local data and applied to global data. This study explores whether sequential neural networks, which consider past data, perform better than models that do not. The findings show that sequential models struggle with global upscaling, likely due to their sensitivity to data shifts from local to global scales. To improve ML-based upscaling, additional data or integration of physical knowledge is needed.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
Short summary
Short summary
We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
Shengxi Gui, Philipp Schuegraf, Ksenia Bittner, and Rongjun Qin
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 81–88, https://doi.org/10.5194/isprs-annals-X-2-2024-81-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-81-2024, 2024
Daniel Panangian and Ksenia Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 185–192, https://doi.org/10.5194/isprs-annals-X-2-2024-185-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-185-2024, 2024
Philipp Schuegraf, Zhixin Li, Jiaojiao Tian, Jie Shan, and Ksenia Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 217–222, https://doi.org/10.5194/isprs-annals-X-2-2024-217-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-217-2024, 2024
G. Mutreja and K. Bittner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 315–321, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-315-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-315-2023, 2023
M. Xiang, S. Azimi, R. Bahmanyar, U. Sörgel, and P. Reinartz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 629–634, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-629-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-629-2023, 2023
K. Bittner, S. Zorzi, T. Krauß, and P. d’Angelo
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 925–933, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-925-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-925-2023, 2023
P. Schuegraf, M. Fuentes Reyes, Y. Xu, and K. Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 971–979, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-971-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-971-2023, 2023
H. Li, J. Tian, Y. Xie, C. Li, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 501–508, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-501-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-501-2022, 2022
F. Kurz, P. Mendes, V. Gstaiger, R. Bahmanyar, P. d’Angelo, S. M. Azimi, S. Auer, N. Merkle, C. Henry, D. Rosenbaum, J. Hellekes, H. Runge, F. Toran, and P. Reinartz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2022, 221–226, https://doi.org/10.5194/isprs-annals-V-1-2022-221-2022, https://doi.org/10.5194/isprs-annals-V-1-2022-221-2022, 2022
P. Schuegraf, J. Schnell, C. Henry, and K. Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 407–414, https://doi.org/10.5194/isprs-annals-V-2-2022-407-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-407-2022, 2022
Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein
Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, https://doi.org/10.5194/hess-26-1579-2022, 2022
Short summary
Short summary
We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
P. d’Angelo and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 77–82, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-77-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-77-2021, 2021
B. Kraft, M. Jung, M. Körner, and M. Reichstein
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1537–1544, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1537-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1537-2020, 2020
M. Rußwurm, C. Pelletier, M. Zollner, S. Lefèvre, and M. Körner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1545–1551, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1545-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1545-2020, 2020
Y. Xia, P. d’Angelo, J. Tian, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 521–525, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-521-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-521-2020, 2020
J. Tian, T. Schneider, C. Kempf, Y. Xia, M. Lusseau, J. Hill, E. Jachmann, and P. Reinartz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 781–787, https://doi.org/10.5194/isprs-annals-V-2-2020-781-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-781-2020, 2020
X. Yuan, J. Tian, and P. Reinartz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 279–285, https://doi.org/10.5194/isprs-annals-V-3-2020-279-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-279-2020, 2020
M. Schmitt, J. Prexl, P. Ebel, L. Liebel, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 795–802, https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020, 2020
R. Bahmanyar, S. M. Azimi, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W18, 163–170, https://doi.org/10.5194/isprs-archives-XLII-4-W18-163-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W18-163-2019, 2019
M. Madadikhaljan, R. Bahmanyar, S. M. Azimi, P. Reinartz, and U. Sörgel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W18, 687–692, https://doi.org/10.5194/isprs-archives-XLII-4-W18-687-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W18-687-2019, 2019
S. Aigner and M. Körner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W16, 3–11, https://doi.org/10.5194/isprs-archives-XLII-2-W16-3-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W16-3-2019, 2019
J. Knöttner, D. Rosenbaum, F. Kurz, P. Reinartz, and A. Brunn
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 95–101, https://doi.org/10.5194/isprs-annals-IV-2-W7-95-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-95-2019, 2019
L. Wagner, L. Liebel, and M. Körner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 189–196, https://doi.org/10.5194/isprs-annals-IV-2-W7-189-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-189-2019, 2019
Y. Xia, J. Tian, P. d’Angelo, and P. Reinartz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 205–210, https://doi.org/10.5194/isprs-annals-IV-2-W7-205-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-205-2019, 2019
C. Kempf, J. Tian, F. Kurz, P. d’Angelo, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 43–50, https://doi.org/10.5194/isprs-archives-XLII-2-W13-43-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-43-2019, 2019
S. Azimi, E. Vig, F. Kurz, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 19–23, https://doi.org/10.5194/isprs-archives-XLII-1-19-2018, https://doi.org/10.5194/isprs-archives-XLII-1-19-2018, 2018
F. Kurz, D. Waigand, P. Pekezou-Fouopi, E. Vig, C. Henry, N. Merkle, D. Rosenbaum, V. Gstaiger, S. Azimi, S. Auer, P. Reinartz, and S. Knake-Langhorst
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 251–256, https://doi.org/10.5194/isprs-archives-XLII-1-251-2018, https://doi.org/10.5194/isprs-archives-XLII-1-251-2018, 2018
K. Bittner, P. d’Angelo, M. Körner, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 103–108, https://doi.org/10.5194/isprs-archives-XLII-2-103-2018, https://doi.org/10.5194/isprs-archives-XLII-2-103-2018, 2018
M. Schmitt, L. H. Hughes, M. Körner, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1045–1051, https://doi.org/10.5194/isprs-archives-XLII-2-1045-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1045-2018, 2018
Y. Xia, J. Tian, P. d’Angelo, and P. Reinartz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 303–309, https://doi.org/10.5194/isprs-annals-IV-2-303-2018, https://doi.org/10.5194/isprs-annals-IV-2-303-2018, 2018
Ekaterina Chuprikova, Lukas Liebel, and Liqiu Meng
Proc. Int. Cartogr. Assoc., 1, 23, https://doi.org/10.5194/ica-proc-1-23-2018, https://doi.org/10.5194/ica-proc-1-23-2018, 2018
J. Mu, S. Cui, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 159–165, https://doi.org/10.5194/isprs-archives-XLII-1-W1-159-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-159-2017, 2017
D. Cerra, D. Traganos, P. Gege, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 279–282, https://doi.org/10.5194/isprs-archives-XLII-1-W1-279-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-279-2017, 2017
D. Traganos, D. Cerra, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 283–287, https://doi.org/10.5194/isprs-archives-XLII-1-W1-283-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-283-2017, 2017
K. Bittner, S. Cui, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 481–486, https://doi.org/10.5194/isprs-archives-XLII-1-W1-481-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-481-2017, 2017
M. Rußwurm and M. Körner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 551–558, https://doi.org/10.5194/isprs-archives-XLII-1-W1-551-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-551-2017, 2017
T. Partovi, F. Fraundorfer, S. Azimi, D. Marmanis, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 653–657, https://doi.org/10.5194/isprs-archives-XLII-1-W1-653-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-653-2017, 2017
L. Liebel and M. Körner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 883–890, https://doi.org/10.5194/isprs-archives-XLI-B3-883-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-883-2016, 2016
F. Kurz, D. Rosenbaum, H. Runge, D. Cerra, G. Mattyus, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 521–525, https://doi.org/10.5194/isprs-archives-XLI-B2-521-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-521-2016, 2016
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
Useful Links
Useful External Links
Our Contact
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org