- Article
(1431 KB) - Metadata XML
- April 08, 2024
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-2
https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018
© Author(s) 2018. 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-XLII-2-1199-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 May 2018
| 30 May 2018
EXTRACTION OF BUILDING ROOF EDGES FROM LIDAR DATA TO OPTIMIZE THE DIGITAL SURFACE MODEL FOR TRUE ORTHOPHOTO GENERATION
E. Widyaningrum
Dept. of Geoscience and Remote Sensing, Delft University of Technology, The Netherlands
Centre for Topographic Base Mapping and Toponyms, Geospatial Information Agency, Indonesia
R. C. Lindenbergh
Dept. of Geoscience and Remote Sensing, Delft University of Technology, The Netherlands
B. G. H. Gorte
University of New South Wales, Australia
K. Zhou
Dept. of Geoscience and Remote Sensing, Delft University of Technology, The Netherlands
Related authors
E. Widyaningrum, M. K. Fajari, R. C. Lindenbergh, and M. Hahn
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 339–346, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-339-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-339-2020, 2020
Y. Chen, W. Gao, E. Widyaningrum, M. Zheng, and K. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 87–92, https://doi.org/10.5194/isprs-archives-XLII-4-87-2018, https://doi.org/10.5194/isprs-archives-XLII-4-87-2018, 2018
K. Zhou, B. Gorte, R. Lindenbergh, and E. Widyaningrum
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1229–1235, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, 2018
E. Widyaningrum and B. G. H. Gorte
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 557–565, https://doi.org/10.5194/isprs-archives-XLII-2-W7-557-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-557-2017, 2017
E. Widyaningrum and B. G. H. Gorte
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 365–369, https://doi.org/10.5194/isprs-archives-XLII-1-W1-365-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-365-2017, 2017
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023, https://doi.org/10.5194/nhess-23-3723-2023, 2023
Short summary
Short summary
Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.
D. Hulskemper, K. Anders, J. A. Á. Antolínez, M. Kuschnerus, B. Höfle, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 53–60, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, 2022
J. P. Meinderts, R. Lindenbergh, D. H. van der Heide, A. Amiri-Simkooei, and L. Truong-Hong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 69–76, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-69-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-69-2022, 2022
L. Truong-Hong, R. C. Lindenbergh, and M. J. Vermeij
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W4-2022, 161–168, https://doi.org/10.5194/isprs-archives-XLVIII-4-W4-2022-161-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W4-2022-161-2022, 2022
A. Nurunnabi, F. N. Teferle, R. C. Lindenbergh, J. Li, and S. Zlatanova
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 59–66, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022, 2022
H. S. Kathmann, A. L. van Natijne, and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1033–1040, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1033-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1033-2022, 2022
M. Kuschnerus, R. Lindenbergh, Q. Lodder, E. Brand, and S. Vos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1055–1061, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1055-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1055-2022, 2022
F. Dahle, J. Tanke, B. Wouters, and R. Lindenbergh
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 237–244, https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022, 2022
A. Nurunnabi, F. N. Teferle, D. F. Laefer, R. C. Lindenbergh, and A. Hunegnaw
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2-W1-2022, 401–408, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022, 2022
A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and S. Parvaz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W5-2021, 397–404, https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-397-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-397-2021, 2021
L. Truong-Hong, N. Nguyen, R. Lindenbergh, P. Fisk, and T. Huynh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W4-2021, 119–124, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-119-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-119-2021, 2021
A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and A. Hunegnaw
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 31–38, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021, 2021
Q. Bai, R. C. Lindenbergh, J. Vijverberg, and J. A. P. Guelen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 115–122, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-115-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-115-2021, 2021
M. Kuschnerus, D. Schröder, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 745–752, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-745-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-745-2021, 2021
K. Anders, L. Winiwarter, H. Mara, R. C. Lindenbergh, S. E. Vos, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 137–144, https://doi.org/10.5194/isprs-annals-V-2-2021-137-2021, https://doi.org/10.5194/isprs-annals-V-2-2021-137-2021, 2021
Mieke Kuschnerus, Roderik Lindenbergh, and Sander Vos
Earth Surf. Dynam., 9, 89–103, https://doi.org/10.5194/esurf-9-89-2021, https://doi.org/10.5194/esurf-9-89-2021, 2021
Short summary
Short summary
Sandy coasts are areas that undergo a lot of changes, which are caused by different influences, such as tides, wind or human activity. Permanent laser scanning is used to generate a three-dimensional representation of a part of the coast continuously over an extended period. By comparing three unsupervised learning algorithms, we develop a methodology to analyse the resulting data set and derive which processes are dominating changes in the beach and dunes.
J. Barton, B. Gorte, M. S. R. S. Eusuf, and S. Zlatanova
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-3-W1-2020, 3–10, https://doi.org/10.5194/isprs-annals-VI-3-W1-2020-3-2020, https://doi.org/10.5194/isprs-annals-VI-3-W1-2020-3-2020, 2020
W. Li, S. Zlatanova, and B. Gorte
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-3-W1-2020, 35–42, https://doi.org/10.5194/isprs-annals-VI-3-W1-2020-35-2020, https://doi.org/10.5194/isprs-annals-VI-3-W1-2020-35-2020, 2020
M. Rutzinger, K. Anders, M. Bremer, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, M. Scaioni, and T. Zieher
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 243–250, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, 2020
M. S. R. S. Eusuf, J. Barton, B. Gorte, and S. Zlatanova
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1199–1206, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1199-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1199-2020, 2020
E. Widyaningrum, M. K. Fajari, R. C. Lindenbergh, and M. Hahn
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 339–346, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-339-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-339-2020, 2020
L. Truong-Hong and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 501–506, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-501-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-501-2020, 2020
B. Gorte, M. Aleksandrov, and S. Zlatanova
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4-W9, 43–47, https://doi.org/10.5194/isprs-annals-IV-4-W9-43-2019, https://doi.org/10.5194/isprs-annals-IV-4-W9-43-2019, 2019
M. Aleksandrov, S. Zlatanova, L. Kimmel, J. Barton, and B. Gorte
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4-W8, 11–17, https://doi.org/10.5194/isprs-annals-IV-4-W8-11-2019, https://doi.org/10.5194/isprs-annals-IV-4-W8-11-2019, 2019
R. Lindenbergh, S. van der Kleij, M. Kuschnerus, S. Vos, and S. de Vries
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1039–1046, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1039-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1039-2019, 2019
L. Truong-Hong, D. F. Laefer, and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1135–1140, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1135-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1135-2019, 2019
B. B. van der Horst, R. C. Lindenbergh, and S. W. J. Puister
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1141–1148, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1141-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1141-2019, 2019
Y. Zang and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1169–1175, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1169-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1169-2019, 2019
Y. Ao, J. Wang, M. Zhou, R. C. Lindenbergh, and M. Y. Yang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 13–20, https://doi.org/10.5194/isprs-archives-XLII-2-W13-13-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-13-2019, 2019
K. Zhou, Y. Chen, I. Smal, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 155–161, https://doi.org/10.5194/isprs-archives-XLII-2-W13-155-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-155-2019, 2019
B. Gorte, S. Zlatanova, and F. Fadli
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 279–283, https://doi.org/10.5194/isprs-annals-IV-2-W5-279-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-279-2019, 2019
K. Anders, R. C. Lindenbergh, S. E. Vos, H. Mara, S. de Vries, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 317–324, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, 2019
M. Soilán, R. Lindenbergh, B. Riveiro, and A. Sánchez-Rodríguez
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 445–452, https://doi.org/10.5194/isprs-annals-IV-2-W5-445-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-445-2019, 2019
B. G. H. Gorte, K. Zhou, C. J. van der Sande, and C. Valk
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4, 67–71, https://doi.org/10.5194/isprs-annals-IV-4-67-2018, https://doi.org/10.5194/isprs-annals-IV-4-67-2018, 2018
Y. Chen, W. Gao, E. Widyaningrum, M. Zheng, and K. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 87–92, https://doi.org/10.5194/isprs-archives-XLII-4-87-2018, https://doi.org/10.5194/isprs-archives-XLII-4-87-2018, 2018
R. P. A. Bormans, R. C. Lindenbergh, and F. Karimi Nejadasl
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 141–148, https://doi.org/10.5194/isprs-archives-XLII-2-141-2018, https://doi.org/10.5194/isprs-archives-XLII-2-141-2018, 2018
I. Puente, R. Lindenbergh, A. Van Natijne, R. Esposito, and R. Schipper
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 923–929, https://doi.org/10.5194/isprs-archives-XLII-2-923-2018, https://doi.org/10.5194/isprs-archives-XLII-2-923-2018, 2018
B. Riveiro, G. Cubreiro, B. Conde, M. Cabaleiro, R. Lindenbergh, M. Soilán, and J. C. Caamaño
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 969–974, https://doi.org/10.5194/isprs-archives-XLII-2-969-2018, https://doi.org/10.5194/isprs-archives-XLII-2-969-2018, 2018
A. L. van Natijne, R. C. Lindenbergh, and R. F. Hanssen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1137–1144, https://doi.org/10.5194/isprs-archives-XLII-2-1137-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1137-2018, 2018
J. Wang and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1163–1168, https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018, 2018
K. Zhou, B. Gorte, R. Lindenbergh, and E. Widyaningrum
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1229–1235, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, 2018
Y. Shen, R. Lindenbergh, B. Hofland, and R. Kramer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 139–147, https://doi.org/10.5194/isprs-annals-IV-2-W4-139-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-139-2017, 2017
E. Widyaningrum and B. G. H. Gorte
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 557–565, https://doi.org/10.5194/isprs-archives-XLII-2-W7-557-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-557-2017, 2017
K. L. Zhou and B. G. H. Gorte
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 579–586, https://doi.org/10.5194/isprs-archives-XLII-2-W7-579-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-579-2017, 2017
E. Widyaningrum and B. G. H. Gorte
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 365–369, https://doi.org/10.5194/isprs-archives-XLII-1-W1-365-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-365-2017, 2017
A. Nurunnabi, Y. Sadahiro, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 63–70, https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017, 2017
J. Böhm, M. Bredif, T. Gierlinger, M. Krämer, R. Lindenberg, K. Liu, F. Michel, and B. Sirmacek
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 301–307, https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016, 2016
V. H. Phan, R. C. Lindenbergh, and M. Menenti
The Cryosphere Discuss., https://doi.org/10.5194/tcd-8-2425-2014, https://doi.org/10.5194/tcd-8-2425-2014, 2014
Revised manuscript not accepted
J. F. Levinsen, K. Khvorostovsky, F. Ticconi, A. Shepherd, R. Forsberg, L. S. Sørensen, A. Muir, N. Pie, D. Felikson, T. Flament, R. Hurkmans, G. Moholdt, B. Gunter, R. C. Lindenbergh, and M. Kleinherenbrink
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-5433-2013, https://doi.org/10.5194/tcd-7-5433-2013, 2013
Revised manuscript not accepted
V. H. Phan, R. C. Lindenbergh, and M. Menenti
Hydrol. Earth Syst. Sci., 17, 4061–4077, https://doi.org/10.5194/hess-17-4061-2013, https://doi.org/10.5194/hess-17-4061-2013, 2013
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