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MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters
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CITATION
P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, "MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355.
DISCLAIMER
This data set is provided "as is" and without any express or implied warranties, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose.
ACKNOWLEDGEMENTS
This work is part of MagicBathy project funded by the European Union’s HORIZON Europe research and innovation programme under the Marie Skłodowska-Curie GA 101063294. For more information about the project visit https://www.magicbathy.eu/.
R-CAUSTIC: Rippling CAUSTICs underwater Image dataset
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CITATION
P. Agrafiotis, K. Karantzalos and A. Georgopoulos, "Seafloor-Invariant Caustics Removal From Underwater Imagery," in IEEE Journal of Oceanic Engineering, vol. 48, no. 4, pp. 1300-1321, Oct. 2023, doi: 10.1109/JOE.2023.3277168.
DISCLAIMER
This data set is provided "as is" and without any express or implied warranties, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose.
ACKNOWLEDGEMENTS
This work was supported in part by the Special Account for Research Grants of National Technical University of Athens (NTUA). NTUA would like to thank NVIDIA Corporation for the support with the donation of GPU hardware to Dr. Agrafiotis through the NVIDIA Academic Hardware Grant Program. The publication of the article in OA mode was financially supported by HEAL-Link.
NTUA HDR PERSON DATASET
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CITATION
Agrafiotis P., Stathopoulou E., Georgopoulos A. and Doulamis A. (2015). HDR Imaging for Enchancing People Detection and Tracking in Indoor Environments. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015), ISBN 978-989-758-090-1, pages 623-630. DOI: 10.5220/0005456706230630
DISCLAIMER
This data set is provided "as is" and without any express or implied warranties, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose.
ACKNOWLEDGEMENTS
The research leading to these results has been supported by European Union funds and National funds (GSRT) from Greece and EU under the project JASON: Joint synergistic and integrated use of eArth obServation, navigatiOn and commuNication technologies for enhanced border security funded under the cooperation framework. The contribution of Ms. Elisavet K. Stathopoulou has been supported by the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement no 608013, titled ITNDCH: Initial Training Network for Digital Cultural Heritage: Projecting our Past to the Future.