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Articles | Volume XLII-2/W10
https://doi.org/10.5194/isprs-archives-XLII-2-W10-9-2019
© 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-W10-9-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
17 Apr 2019
| 17 Apr 2019
SHALLOW WATER BATHYMETRY MAPPING FROM UAV IMAGERY BASED ON MACHINE LEARNING
P. Agrafiotis
National Technical University of Athens, School of Rural and Surveying Engineering, Department of Topography, Zografou Campus, 9 Heroon Polytechniou str., 15780, Athens, Greece
Cyprus University of Technology, Civil Engineering and Geomatics Dept., Lab of Photogrammetric Vision, 2-8 Saripolou str., 3036, Limassol, Cyprus
D. Skarlatos
Cyprus University of Technology, Civil Engineering and Geomatics Dept., Lab of Photogrammetric Vision, 2-8 Saripolou str., 3036, Limassol, Cyprus
A. Georgopoulos
National Technical University of Athens, School of Rural and Surveying Engineering, Department of Topography, Zografou Campus, 9 Heroon Polytechniou str., 15780, Athens, Greece
K. Karantzalos
National Technical University of Athens, School of Rural and Surveying Engineering, Department of Topography, Zografou Campus, 9 Heroon Polytechniou str., 15780, Athens, Greece
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W3, 25–31, https://doi.org/10.5194/isprs-archives-XLII-2-W3-25-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W3-25-2017, 2017
Eleni Savvidou, Andreas Efstratiadis, Antonis D. Koussis, Antonis Koukouvinos, and Dimitrios Skarlatos
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-627, https://doi.org/10.5194/hess-2016-627, 2016
Manuscript not accepted for further review
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A systematic framework for delineating Hydrological Response Units (HRUs) is proposed, based on a modified Curve Number (CN) approach that accounts, by means of classes, for the soil permeability, land cover, and drainage capacity of a river basin. The aim is to reduce the subjectivity introduced by HRUs and provide a parsimonious modelling scheme. Results showed optimal performance of the CN approach in calibration and particularly in validation, compared against alternative HRU approaches.
A. Georgopoulos
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W1, 99–106, https://doi.org/10.5194/isprs-annals-IV-2-W1-99-2016, https://doi.org/10.5194/isprs-annals-IV-2-W1-99-2016, 2016
Andreas Georgiou and Dimitrios Skarlatos
Geosci. Instrum. Method. Data Syst., 5, 321–332, https://doi.org/10.5194/gi-5-321-2016, https://doi.org/10.5194/gi-5-321-2016, 2016
Short summary
Short summary
The objective of this paper is to present a general integrated framework to evaluate land suitability for the optimal placement of solar park, which is based on a combination of a GIS, remote sensing techniques, and multi-criteria decision-making methods. Innovative aspect derives from the proposed tool as currently implemented within a local GIS, which provides a versatile platform of analysis and semi-automation of the operations, which might also extended into full automation.
P. Agrafiotis, A. Georgopoulos, and K. Karantzalos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 625–630, https://doi.org/10.5194/isprs-archives-XLI-B7-625-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-625-2016, 2016
A. D. Vaiopoulos and K. Karantzalos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 723–730, https://doi.org/10.5194/isprs-archives-XLI-B7-723-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-723-2016, 2016
M. Skamantzari and A. Georgopoulos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 961–968, https://doi.org/10.5194/isprs-archives-XLI-B5-961-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-961-2016, 2016
M. S. Tryfona and A. Georgopoulos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 969–975, https://doi.org/10.5194/isprs-archives-XLI-B5-969-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-969-2016, 2016
A. Georgopoulos, C. Oikonomou, E. Adamopoulos, and E. K. Stathopoulou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 355–362, https://doi.org/10.5194/isprs-archives-XLI-B5-355-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-355-2016, 2016
D. Skarlatos and S. Yiatros
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 35–41, https://doi.org/10.5194/isprs-annals-III-5-35-2016, https://doi.org/10.5194/isprs-annals-III-5-35-2016, 2016
C. Platias, M. Vakalopoulou, and K. Karantzalos
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 121–128, https://doi.org/10.5194/isprs-annals-III-1-121-2016, https://doi.org/10.5194/isprs-annals-III-1-121-2016, 2016