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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume V-2-2021
https://doi.org/10.5194/isprs-annals-V-2-2021-35-2021
© Author(s) 2021. 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-annals-V-2-2021-35-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
17 Jun 2021
| 17 Jun 2021
A FRAMEWORK FOR GENERIC SPATIAL SEARCH IN 3D POINT CLOUDS
J. Otepka
TU Wien, Department of Geodesy and Geoinformation, Vienna, Austria
G. Mandlburger
TU Wien, Department of Geodesy and Geoinformation, Vienna, Austria
W. Karel
TU Wien, Department of Geodesy and Geoinformation, Vienna, Austria
B. Wöhrer
TU Wien, Department of Geodesy and Geoinformation, Vienna, Austria
TU Wien, Department of Geodesy and Geoinformation, Vienna, Austria
N. Pfeifer
TU Wien, Department of Geodesy and Geoinformation, Vienna, Austria
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Jakob Rom, Florian Haas, Tobias Heckmann, Moritz Altmann, Fabian Fleischer, Camillo Ressl, Sarah Betz-Nutz, and Michael Becht
Nat. Hazards Earth Syst. Sci., 23, 601–622, https://doi.org/10.5194/nhess-23-601-2023, https://doi.org/10.5194/nhess-23-601-2023, 2023
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Short summary
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N. Homainejad, S. Zlatanova, S. M. E. Sepasgozar, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W2-2022, 113–119, https://doi.org/10.5194/isprs-annals-X-4-W2-2022-113-2022, https://doi.org/10.5194/isprs-annals-X-4-W2-2022-113-2022, 2022
R. Arav, F. Pöppl, and N. Pfeifer
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 697–704, https://doi.org/10.5194/isprs-annals-V-3-2022-697-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-697-2022, 2022
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The Cryosphere, 15, 5345–5369, https://doi.org/10.5194/tc-15-5345-2021, https://doi.org/10.5194/tc-15-5345-2021, 2021
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 469–473, https://doi.org/10.5194/isprs-archives-XLII-2-W13-469-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-469-2019, 2019
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G. Mandlburger and B. Jutzi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 109–116, https://doi.org/10.5194/isprs-annals-IV-1-109-2018, https://doi.org/10.5194/isprs-annals-IV-1-109-2018, 2018
M. Cramer, N. Haala, D. Laupheimer, G. Mandlburger, and P. Havel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 115–120, https://doi.org/10.5194/isprs-archives-XLII-1-115-2018, https://doi.org/10.5194/isprs-archives-XLII-1-115-2018, 2018
G. Mandlburger, J. Kremer, F. Steinbacher, and R. Baran
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 275–282, https://doi.org/10.5194/isprs-archives-XLII-1-275-2018, https://doi.org/10.5194/isprs-archives-XLII-1-275-2018, 2018
G. Mandlburger
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 659–666, https://doi.org/10.5194/isprs-archives-XLII-2-659-2018, https://doi.org/10.5194/isprs-archives-XLII-2-659-2018, 2018
N. Li, N. Pfeifer, and C. Liu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 107–114, https://doi.org/10.5194/isprs-annals-IV-2-W4-107-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-107-2017, 2017
G. Mandlburger, N. Pfeifer, and U. Soergel
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 123–130, https://doi.org/10.5194/isprs-annals-IV-2-W4-123-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-123-2017, 2017
A. Roncat, N. Pfeifer, and C. Briese
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 131–137, https://doi.org/10.5194/isprs-annals-IV-2-W4-131-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-131-2017, 2017
D. Wang, M. Hollaus, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 157–164, https://doi.org/10.5194/isprs-annals-IV-2-W4-157-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-157-2017, 2017
G. Mandlburger, K. Wenzel, A. Spitzer, N. Haala, P. Glira, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 259–266, https://doi.org/10.5194/isprs-annals-IV-2-W4-259-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-259-2017, 2017
M. Pöchtrager, G. Styhler-Aydın, M. Döring-Williams, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W2, 195–202, https://doi.org/10.5194/isprs-annals-IV-2-W2-195-2017, https://doi.org/10.5194/isprs-annals-IV-2-W2-195-2017, 2017
A. Zlinszky, B. Deák, A. Kania, A. Schroiff, and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1293–1299, https://doi.org/10.5194/isprs-archives-XLI-B8-1293-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-1293-2016, 2016
G. Mandlburger, M. Pfennigbauer, M. Wieser, U. Riegl, and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 933–939, https://doi.org/10.5194/isprs-archives-XLI-B1-933-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-933-2016, 2016
Livia Piermattei, Luca Carturan, Fabrizio de Blasi, Paolo Tarolli, Giancarlo Dalla Fontana, Antonio Vettore, and Norbert Pfeifer
Earth Surf. Dynam., 4, 425–443, https://doi.org/10.5194/esurf-4-425-2016, https://doi.org/10.5194/esurf-4-425-2016, 2016
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Short summary
We investigated the applicability of the SfM–MVS approach for calculating the geodetic mass balance of a glacier and for the detection of the surface displacement rate of an active rock glacier located in the eastern Italian Alps. The results demonstrate that it is possible to reliably quantify the investigated glacial and periglacial processes by means of a quick ground-based photogrammetric survey that was conducted using a consumer grade SRL camera and natural targets as ground control points.
Mathias Harzhauser, Ana Djuricic, Oleg Mandic, Thomas A. Neubauer, Martin Zuschin, and Norbert Pfeifer
Biogeosciences, 13, 1223–1235, https://doi.org/10.5194/bg-13-1223-2016, https://doi.org/10.5194/bg-13-1223-2016, 2016
Short summary
Short summary
We present the first analysis of population structure and cohort distribution in a fossil oyster reef. Data are derived from Terrestrial Laser Scanning of a Miocene shell bed covering 459 m². A growth model was calculated, revealing this species as the giant oyster Crassostrea gryphoides was the fastest growing oyster known so far. The shell half-lives range around few years, indicating that oyster reefs were geologically short-lived structures, which were degraded on a decadal scale.
A. Zlinszky, G. Timár, R. Weber, B. Székely, C. Briese, C. Ressl, and N. Pfeifer
Solid Earth, 5, 355–369, https://doi.org/10.5194/se-5-355-2014, https://doi.org/10.5194/se-5-355-2014, 2014
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