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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume V-5-2020
https://doi.org/10.5194/isprs-annals-V-5-2020-109-2020
© 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-annals-V-5-2020-109-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Aug 2020
| 03 Aug 2020
SEMCITY TOULOUSE: A BENCHMARK FOR BUILDING INSTANCE SEGMENTATION IN SATELLITE IMAGES
R. Roscher
Institute of Geodesy and Geoinformation, University of Bonn, Germany
Institute of Computer Science, University of Osnabrueck, Germany
Swiss Data Science Center, ETHZ and EPF Lausanne, Switzerland
C. Mallet
Univ. Gustave Eiffel, IGN-ENSG, LaSTIG/Strudel, Saint-Mandé, France
L. Drees
Institute of Geodesy and Geoinformation, University of Bonn, Germany
J. D. Wegner
EcoVision Lab, ETH Zurich, Switzerland
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 1013–1019, https://doi.org/10.5194/isprs-annals-V-2-2020-1013-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-1013-2020, 2020
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 1–6, https://doi.org/10.5194/isprs-annals-V-3-2020-1-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-1-2020, 2020
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Short summary
Short summary
This work demonstrates a new approach to obtain flood level trend information from surveillance footage with minimal prior information. A neural network trained to detect flood water is applied to video frames to create a qualitative flooding metric (namely, SOFI). The correlation between the real water trend and SOFI was found to be 75 % on average (based on six videos of flooding under various circumstances). SOFI could be used for flood model calibration, to increase model reliability.
P. Chaudhary, S. D’Aronco, M. Moy de Vitry, J. P. Leitão, and J. D. Wegner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 5–12, https://doi.org/10.5194/isprs-annals-IV-2-W5-5-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-5-2019, 2019
C. Mallet, I. Dowman, G. Vosselman, U. Stilla, L. Halounova, and N. Paparoditis
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 53–58, https://doi.org/10.5194/isprs-annals-IV-5-53-2018, https://doi.org/10.5194/isprs-annals-IV-5-53-2018, 2018
J. Oehrlein, A. Förster, D. Schunck, Y. Dehbi, R. Roscher, and J.-H. Haunert
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4-W7, 107–114, https://doi.org/10.5194/isprs-annals-IV-4-W7-107-2018, https://doi.org/10.5194/isprs-annals-IV-4-W7-107-2018, 2018
S. Giordano, A. Le Bris, and C. Mallet
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 105–112, https://doi.org/10.5194/isprs-annals-IV-2-105-2018, https://doi.org/10.5194/isprs-annals-IV-2-105-2018, 2018
C. Stucker, A. Richard, J. D. Wegner, and K. Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 263–270, https://doi.org/10.5194/isprs-annals-IV-2-263-2018, https://doi.org/10.5194/isprs-annals-IV-2-263-2018, 2018
W. Ouerghemmi, A. Le Bris, N. Chehata, and C. Mallet
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 167–174, https://doi.org/10.5194/isprs-archives-XLII-1-W1-167-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-167-2017, 2017
L. Drees and R. Roscher
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1-W1, 133–139, https://doi.org/10.5194/isprs-annals-IV-1-W1-133-2017, https://doi.org/10.5194/isprs-annals-IV-1-W1-133-2017, 2017
Ribana Roscher, Jan Behmann, Anne-Katrin Mahlein, Jan Dupuis, Heiner Kuhlmann, and Lutz Plümer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 89–96, https://doi.org/10.5194/isprs-annals-III-7-89-2016, https://doi.org/10.5194/isprs-annals-III-7-89-2016, 2016
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