Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data
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 ba...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/21/6207 |
| _version_ | 1827703118766276608 |
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| author | Marta Wlodarczyk-Sielicka Wioleta Blaszczak-Bak |
| author_facet | Marta Wlodarczyk-Sielicka Wioleta Blaszczak-Bak |
| author_sort | Marta Wlodarczyk-Sielicka |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:11:55Z |
| format | Article |
| id | doaj.art-5e00e64121e94b449659cbae3e051bc4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:11:55Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5e00e64121e94b449659cbae3e051bc42023-11-20T19:16:56ZengMDPI AGSensors1424-82202020-10-012021620710.3390/s20216207Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big DataMarta Wlodarczyk-Sielicka0Wioleta Blaszczak-Bak1Department of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, PolandFaculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Oczapowskiego 1, 10-719 Olsztyn, PolandFloating 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.https://www.mdpi.com/1424-8220/20/21/6207big data applicationsbathymetrydata reductiondata processingdata visualizationfusion of spatial data |
| spellingShingle | Marta Wlodarczyk-Sielicka Wioleta Blaszczak-Bak Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data Sensors big data applications bathymetry data reduction data processing data visualization fusion of spatial data |
| title | Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data |
| title_full | Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data |
| title_fullStr | Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data |
| title_full_unstemmed | Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data |
| title_short | Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data |
| title_sort | processing of bathymetric data the fusion of new reduction methods for spatial big data |
| topic | big data applications bathymetry data reduction data processing data visualization fusion of spatial data |
| url | https://www.mdpi.com/1424-8220/20/21/6207 |
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