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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B1-2021
https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021
© 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-archives-XLIII-B1-2021-31-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
28 Jun 2021
| 28 Jun 2021
AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS
A. Nurunnabi
Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6, rue Richard Codenhove-Kalergi, L-1359, Luxembourg
F. N. Teferle
Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6, rue Richard Codenhove-Kalergi, L-1359, Luxembourg
J. Li
Geography and Environmental Management, University of Waterloo, Waterloo ON N2L 3G1, Canada
R. C. Lindenbergh
Geosciences and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
A. Hunegnaw
Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6, rue Richard Codenhove-Kalergi, L-1359, Luxembourg
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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.
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 207–213, https://doi.org/10.5194/isprs-annals-V-1-2020-207-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-207-2020, 2020
Lingfei Ma, Tianyu Wu, Ying Li, Jonathan Li, Yiping Chen, and Michael Chapman
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 1, 12, https://doi.org/10.5194/ica-adv-1-12-2019, https://doi.org/10.5194/ica-adv-1-12-2019, 2019
H. Guan, Y. Yu, W. Yan, D. Li, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 989–993, https://doi.org/10.5194/isprs-archives-XLII-2-W13-989-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-989-2019, 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
L. Ma, Z. Chen, Y. Li, D. Zhang, J. Li, and M. A. Chapman
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 79–86, https://doi.org/10.5194/isprs-archives-XLII-2-W13-79-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-79-2019, 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
D. Backes, G. Schumann, F. N. Teferle, and J. Boehm
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 181–187, https://doi.org/10.5194/isprs-archives-XLII-2-W13-181-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-181-2019, 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
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
E. Widyaningrum, R. C. Lindenbergh, B. G. H. Gorte, and K. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1199–1205, https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018, 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
F. Chen, S. Lou, Q. Fan, J. Li, C. Wang, and M. Claverie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2619–2624, https://doi.org/10.5194/isprs-archives-XLII-3-2619-2018, https://doi.org/10.5194/isprs-archives-XLII-3-2619-2018, 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
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
Anna Klos, Addisu Hunegnaw, Felix Norman Teferle, Kibrom Ebuy Abraha, Furqan Ahmed, and Janusz Bogusz
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-385, https://doi.org/10.5194/amt-2016-385, 2016
Revised manuscript not accepted
Short summary
Short summary
GPS can remotely sense integrated atmospheric water vapour and in-doing so improve e.g. the accuracy of assimilated numerical weather models. Specially, GPS is well suited in the study of the atmospheric conditions since it is increasingly deployed ever widely around the globe. In this research, we used trend estimates of Zenith Total Delay series and provide a most recent picture of trend and their uncertainties which are used widely to interpret a change in climate.
Tzvetan Simeonov, Dmitry Sidorov, Felix Norman Teferle, Georgi Milev, and Guergana Guerova
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-152, https://doi.org/10.5194/amt-2016-152, 2016
Revised manuscript not accepted
Weihong Cui, Guofeng Wang, Chenyi Feng, Yiwei Zheng, Jonathan Li, and Yi Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 195–203, https://doi.org/10.5194/isprs-archives-XLI-B7-195-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-195-2016, 2016
Shanxin Zhang, Cheng Wang, Zhuang Yang, Yiping Chen, and Jonathan Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 615–619, https://doi.org/10.5194/isprs-archives-XLI-B5-615-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-615-2016, 2016
Yuenan Li, Jianjun Wang, Cheng Chen, Yifei Chen, and Jonathan Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 721–727, https://doi.org/10.5194/isprs-archives-XLI-B2-721-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-721-2016, 2016
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
Yifei Chen, Yuenan Li, and Jonathan Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 695–702, https://doi.org/10.5194/isprs-archives-XLI-B2-695-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-695-2016, 2016
Haocheng Zhang, Jonathan Li, Ming Cheng, and Cheng Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 717–723, https://doi.org/10.5194/isprs-archives-XLI-B1-717-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-717-2016, 2016
Xinqu Chen and Jonathan Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 545–549, https://doi.org/10.5194/isprs-archives-XLI-B1-545-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-545-2016, 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
H. Yan, Z. Wang, and J. Li
Geosci. Model Dev., 6, 1591–1599, https://doi.org/10.5194/gmd-6-1591-2013, https://doi.org/10.5194/gmd-6-1591-2013, 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.
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