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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Dataset for surface waves height prediction through the video and image processing

Authors: Salin, Mikhail;

Dataset for surface waves height prediction through the video and image processing

Abstract

Image-based study of surface waves is a long lasting topic in ocean science and remote sensing. We believe that modern computers and new programming techniques can make a break-through in this area. This dataset provides some video files of surface wind waves of two kinds. First is a video snapshot of a quite large area. Second one is a zoom-in video of a spar-buoy (a stick) located in this field. According to the zoom-in video we may see the actual height of the wave in this particular point. This should be treated as a reliable data and so it can be used to calibrate the brightness field. I.e. the users of this dataset are welcome to train their model to obtain the height of the wave out of its brightness on the zoom-out large-area videos. All video files are readable by a conventional software. Records were taken at mild wind conditions in a gulf (fjord or skerry) of the Ladoga Lake. See "readme.pdf" for the details

{"references": ["M. Salin. Wind-wave optic lab // Github, webpage: https://github.com/mikesalin/wwol accessed: 04-05-2023", "B. M. Salin, M. B. Salin. Combined Method for Measuring 3D Wave Spectra. II. Examples of Using the Basic Measurement Procedures and Analysis of the Obtained Results // Radiophysics and Quantum Electronics, 2015. V. 58, No. 3. P. 185-196 https://doi.org/10.1007/s11141-015-9592-3", "M.B. Salin, A.V. Vitalsky. Usage of single-camera video recording to measure sea surface roughness with machine learning methods // arXiv 2303.09967, 2023 https://doi.org/10.48550/arXiv.2303.09967", "M. B. Salin, O. A. Potapov, B. M. Salin, A. S. Chashchin. Measuring the characteristics of backscattering of sound on a rough surface in the near-field zone of a phased array // Acoustical Physics, 2016. Vol. 62, No. 1, P. 74-88. https://doi.org/10.1134/S106377101506010X"]}

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Keywords

surface roughness, video processing, wind waves spectrum, image processing

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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