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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-2/W13
https://doi.org/10.5194/isprs-archives-XLII-2-W13-13-2019
© Author(s) 2019. 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-W13-13-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
04 Jun 2019
| 04 Jun 2019
FULLY CONVOLUTIONAL NETWORKS FOR STREET FURNITURE IDENTIFICATION IN PANORAMA IMAGES
Y. Ao
Dept. of Earth Observation Science, Faculty ITC, University of Twente, The Netherlands
J. Wang
Dept. of Geoscience and Remote Sensing, Delft University of Technology, The Netherlands
M. Zhou
Academy of Opto-Electronics, Chinese Academy of Sciences, China
R. C. Lindenbergh
Dept. of Geoscience and Remote Sensing, Delft University of Technology, The Netherlands
M. Y. Yang
Dept. of Earth Observation Science, Faculty ITC, University of Twente, The Netherlands
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Short summary
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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.
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A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and A. Hunegnaw
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C. Mallet, F. Lafarge, M. Poreba, T. Wu, G. Bahl, M. Yu, A. Garioud, Y. Chen, S. Jiang, M. Y. Yang, and N. Paparoditis
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K. Anders, L. Winiwarter, H. Mara, R. C. Lindenbergh, S. E. Vos, and B. Höfle
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C. Mallet, F. Lafarge, M. Poreba, T. Wu, G. Bahl, M. Yu, A. Garioud, Y. Chen, S. Jiang, M. Y. Yang, and N. Paparoditis
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C. Mallet, F. Lafarge, M. Poreba, T. Wu, G. Bahl, M. Yu, A. Garioud, Y. Chen, S. Jiang, M. Y. Yang, and N. Paparoditis
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C. Mallet, F. Lafarge, M. Poreba, T. Wu, G. Bahl, M. Yu, A. Garioud, Y. Chen, S. Jiang, M. Y. Yang, and N. Paparoditis
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Short summary
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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., IV-2, 319–326, https://doi.org/10.5194/isprs-annals-IV-2-319-2018, https://doi.org/10.5194/isprs-annals-IV-2-319-2018, 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
K. Velumani, S. Oude Elberink, M. Y. Yang, and F. Baret
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 149–156, https://doi.org/10.5194/isprs-annals-IV-2-W4-149-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-149-2017, 2017
L. Ma, M. Zhou, and C. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 263–268, https://doi.org/10.5194/isprs-archives-XLII-2-W7-263-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-263-2017, 2017
G. E. Teng, M. Zhou, C. R. Li, H. H. Wu, W. Li, F. R. Meng, C. C. Zhou, and L. Ma
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 297–300, https://doi.org/10.5194/isprs-archives-XLII-2-W7-297-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-297-2017, 2017
S. Crommelinck, R. Bennett, M. Gerke, M. N. Koeva, M. Y. Yang, and G. Vosselman
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W3, 9–16, https://doi.org/10.5194/isprs-annals-IV-2-W3-9-2017, https://doi.org/10.5194/isprs-annals-IV-2-W3-9-2017, 2017
W. Liao, C. Yang, M. Ying Yang, and B. Rosenhahn
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1-W1, 19–26, https://doi.org/10.5194/isprs-annals-IV-1-W1-19-2017, https://doi.org/10.5194/isprs-annals-IV-1-W1-19-2017, 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
M. Zhou, C. R. Li, L. Ma, and H. C. Guan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 447–452, https://doi.org/10.5194/isprs-archives-XLI-B3-447-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-447-2016, 2016
Jinhu Wang, Roderik Lindenbergh, Yueqian Shen, and Massimo Menenti
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 97–103, https://doi.org/10.5194/isprs-annals-III-5-97-2016, https://doi.org/10.5194/isprs-annals-III-5-97-2016, 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.
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