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Article

Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data

by
Marta Wlodarczyk-Sielicka
1,* and
Wioleta Blaszczak-Bak
2
1
Department of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
2
Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Oczapowskiego 1, 10-719 Olsztyn, Poland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6207; https://doi.org/10.3390/s20216207
Submission received: 29 September 2020 / Revised: 27 October 2020 / Accepted: 28 October 2020 / Published: 30 October 2020
(This article belongs to the Special Issue Sensors and Sensor's Fusion in Autonomous Vehicles)

Abstract

Floating autonomous vehicles are very often equipped with modern systems that collect information about the situation under the water surface, e.g., the depth or type of bottom and obstructions on the seafloor. One such system is the multibeam echosounder (MBES), which collects very large sets of bathymetric data. The development and analysis of such large sets are laborious and expensive. Reduction of the spatial data obtained from bathymetric and other systems collecting spatial data is currently widely used. In commercial programs used in the development of data from hydrographic systems, methods of interpolation to a specific mesh size are very frequently used. The authors of this article previously proposed original the true bathymetric data reduction method (TBDRed) and Optimum Dataset (OptD) reduction methods, which maintain the actual position and depth for each of the measured points, without their interpolation. The effectiveness of the proposed methods has already been presented in previous articles. This article proposes the fusion of original reduction methods, which is a new and innovative approach to the problem of bathymetric data reduction. The article contains a description of the methods used and the methodology of developing bathymetric data. The proposed fusion of reduction methods allows the generation of numerical models that can be a safe, reliable source of information, and a basis for design. Numerical models can also be used in comparative navigation, during the creation of electronic navigation maps and other hydrographic products.
Keywords: big data applications; bathymetry; data reduction; data processing; data visualization; fusion of spatial data big data applications; bathymetry; data reduction; data processing; data visualization; fusion of spatial data

Share and Cite

MDPI and ACS Style

Wlodarczyk-Sielicka, M.; Blaszczak-Bak, W. Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data. Sensors 2020, 20, 6207. https://doi.org/10.3390/s20216207

AMA Style

Wlodarczyk-Sielicka M, Blaszczak-Bak W. Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data. Sensors. 2020; 20(21):6207. https://doi.org/10.3390/s20216207

Chicago/Turabian Style

Wlodarczyk-Sielicka, Marta, and Wioleta Blaszczak-Bak. 2020. "Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data" Sensors 20, no. 21: 6207. https://doi.org/10.3390/s20216207

APA Style

Wlodarczyk-Sielicka, M., & Blaszczak-Bak, W. (2020). Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data. Sensors, 20(21), 6207. https://doi.org/10.3390/s20216207

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