{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T09:36:28Z","timestamp":1775554588860,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,6]],"date-time":"2019-07-06T00:00:00Z","timestamp":1562371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water areas occupy over 70 percent of the Earth\u2019s surface and are constantly subject to research and analysis. Often, hydrographic remote sensors are used for such research, which allow for the collection of information on the shape of the water area bottom and the objects located on it. Information about the quality and reliability of the depth data is important, especially during coastal modelling. In-shore areas are liable to continuous transformations and they must be monitored and analyzed. Presently, bathymetric geodata are usually collected via modern hydrographic systems and comprise very large data point sequences that must then be connected using long and laborious processing sequences including reduction. As existing bathymetric data reduction methods utilize interpolated values, there is a clear requirement to search for new solutions. Considering the accuracy of bathymetric maps, a new method is presented here that allows real geodata to be maintained, specifically position and depth. This study presents a description of a developed method for reducing geodata while maintaining true survey values.<\/jats:p>","DOI":"10.3390\/rs11131610","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T03:01:31Z","timestamp":1562554891000},"page":"1610","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The Reduction Method of Bathymetric Datasets that Preserves True Geodata"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7489-8437","authenticated-orcid":false,"given":"Marta","family":"Wlodarczyk-Sielicka","sequence":"first","affiliation":[{"name":"Institute of Geoinformatics, Department of Navigation, Maritime University of Szczecin, 70-500 Szczecin, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4671-6827","authenticated-orcid":false,"given":"Andrzej","family":"Stateczny","sequence":"additional","affiliation":[{"name":"Department of Geodesy, Gdansk University of Technology, 80-233 Gdansk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9400-3426","authenticated-orcid":false,"given":"Jacek","family":"Lubczonek","sequence":"additional","affiliation":[{"name":"Institute of Geoinformatics, Department of Navigation, Maritime University of Szczecin, 70-500 Szczecin, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,6]]},"reference":[{"key":"ref_1","unstructured":"Brown, M.E., and Kraus, N.C. (2007). Tips for Developing Bathymetry Grids for Coastal Modeling System Applications, Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/j.proeng.2015.08.386","article-title":"Coastal Water Quality Monitoring and Modelling Off Chennai City","volume":"116","author":"Mishra","year":"2015","journal-title":"Procedia Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/00221686.2013.821678","article-title":"Coastal hydrodynamics\u2014Present and future","volume":"51","author":"Stansby","year":"2014","journal-title":"J. Hydraul. Res."},{"key":"ref_4","first-page":"23","article-title":"Fast Reduction of High Density Multibeam Echosounder Data for Near Real-Time Applications","volume":"98","author":"Bottelier","year":"2000","journal-title":"Hydrogr. J."},{"key":"ref_5","first-page":"21","article-title":"Interpolation of hydrographic survey data","volume":"99","author":"Burroughes","year":"2001","journal-title":"Hydrogr. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/TGRS.2011.2155071","article-title":"Challenges in Seafloor Imaging and Mapping with Synthetic Aperture Sonar","volume":"49","author":"Hansen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","unstructured":"Jong, C.D., Lachapelle, G., Skone, S., and Elema, I.A. (2010). Hydrography, DUP Blue Print. [2nd ed.]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"77","DOI":"10.2478\/pomr-2013-0009","article-title":"A novel method for archiving multibeam sonar data with emphasis on efficient record size reduction and storage","volume":"20","author":"Moszynski","year":"2013","journal-title":"Pol. Marit. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1080\/01490419.2015.1053639","article-title":"Robust Automatic Reduction of Multibeam Bathymetric Data Based on M-estimators","volume":"38","author":"Rezvani","year":"2015","journal-title":"Mar. Geod."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1029\/2002GC000486","article-title":"Automatic processing of high-rate, high-density multibeam echosounder data","volume":"4","author":"Calder","year":"2003","journal-title":"Geochem. Geophys. Geosyst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s11001-012-9164-2","article-title":"The filtering and compressing of outer beams to multibeam bathymetric data","volume":"34","author":"Yang","year":"2013","journal-title":"Mar. Geophys. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1515\/pomr-2016-0026","article-title":"Technology of Spatial Data Geometrical Simplification in Maritime Mobile Information System for Coastal Waters","volume":"23","author":"Kazimierski","year":"2016","journal-title":"Pol. Marit. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1515\/pomr-2017-0088","article-title":"Application of an Autonomous\/Unmanned Survey Vessel (ASV\/USV) in bathymetric measurements","volume":"24","author":"Specht","year":"2017","journal-title":"Pol. Marit. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kulawik, M., and Lubniewski, Z. (2016, January 2\u20134). Processing of LiDAR and multibeam sonar point cloud data for 3D surface and object shape reconstruction. Proceedings of the 2016 Baltic Geodetic Congress (BGC Geomatics), Gda\u0144sk, Poland.","DOI":"10.1109\/BGC.Geomatics.2016.41"},{"key":"ref_15","first-page":"379","article-title":"New Optimum Dataset method in LiDAR processing","volume":"13","year":"2016","journal-title":"Acta Geodyn. Geomater."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"075009","DOI":"10.1088\/1361-6501\/aa7444","article-title":"The OptD-multi method in LiDAR processing","volume":"28","author":"Kowalik","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Blaszczak-Bak, W., Koppanyi, Z., and Toth, C. (2018). Reduction Method for Mobile Laser Scanning Data. ISPRS Int. J. Geo Inf., 7.","DOI":"10.3390\/ijgi7070285"},{"key":"ref_18","first-page":"012016","article-title":"Hydrographic processing considerations in the \u201cBig Data\u201d age: An overview of technology trends in ocean and coastal surveys","volume":"34","author":"Holland","year":"2016","journal-title":"Earth Environ. Sci."},{"key":"ref_19","unstructured":"International Hydrographic Organization (IHO) (2002). Transfer Standard for Digital Hydrographic Data, International Hydrographic Organization. [3rd ed.]. Special Publication No. 57."},{"key":"ref_20","unstructured":"International Hydrographic Organization (IHO) (2008). Standards for Hydrographic Surveys, International Hydrographic Organization. [5th ed.]. Special Publication No. 44."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1017\/S0373463316000035","article-title":"Clustering bathymetric data for electronic navigational charts","volume":"69","author":"Stateczny","year":"2016","journal-title":"J. Navig."},{"key":"ref_22","first-page":"9","article-title":"Multibeam data processing","volume":"102","author":"Lenk","year":"2001","journal-title":"Hydrogr. J."},{"key":"ref_23","first-page":"466","article-title":"Interpolation Methods and the Accuracy of Bathymetric Seabed Models Based on Multibeam Echosounder Data","volume":"7198","author":"Maleika","year":"2012","journal-title":"Lect. Notes Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cao, J., Cui, H., Shi, H., and Jiao, L. (2016). Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0157551"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5334","DOI":"10.1109\/TGRS.2018.2814012","article-title":"Deriving Bathymetry from Optical Images with a Localized Neural Network Algorithm","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1007\/978-3-540-24844-6_181","article-title":"Hybrid neural model of the sea bottom surface, Artificial Intelligence and Soft Computing-ICAISC","volume":"3070","author":"Lubczonek","year":"2004","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_27","first-page":"53","article-title":"Distribution of shallow water soft and hard bottom seabeds in the Isla del Coco National Park, Pacific Costa Rica","volume":"60","author":"Troncoso","year":"2012","journal-title":"Rev. Biol. Trop."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1515\/geocart-2016-0007","article-title":"Neural networks for the generation of sea bed models using airborne lidar bathymetry data","volume":"65","author":"Kogut","year":"2016","journal-title":"Geod. Cartogr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.5194\/isprs-archives-XLI-B8-1123-2016","article-title":"Costal Bathymetry Estimation from Multispectral Image with Back Propagation Neural Network","volume":"41","author":"Huang","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Z. (2007). Algorithmic Foundation of Multi-Scale Spatial Representation, CRC Press.","DOI":"10.1201\/9781420008432"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.ijinfomgt.2016.05.013","article-title":"Big data reduction framework for value creation in sustainable enterprises","volume":"36","author":"Chang","year":"2016","journal-title":"Int. J. Inf. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Habib ur Rehman, M., Jayaraman, P., Malik, S., Khan, A., and Medhat Gaber, M. (2017). RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments. J. Sens. Actuator Netw., 6.","DOI":"10.3390\/jsan6030017"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.cie.2016.07.013","article-title":"Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives","volume":"101","author":"Zhong","year":"2016","journal-title":"Comput. Ind. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s10596-013-9347-1","article-title":"Hydrographic data modeling methods for determining precise seafloor topography","volume":"17","author":"Aykut","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/BF00337288","article-title":"Self-organized formation of topologically correct feature maps","volume":"43","author":"Kohonen","year":"1982","journal-title":"Biol. Cybern."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/5.58325","article-title":"The self-organizing map","volume":"78","author":"Kohonen","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.cogsys.2018.07.016","article-title":"Fuzzy clustering based self-organizing neural network for real time evaluation of wind music","volume":"52","author":"Tang","year":"2018","journal-title":"Cogn. Syst. Res."},{"key":"ref_38","unstructured":"Osowski, S. (2000). Artificial Neural Networks for Information Processing, Warsaw University of Technology Publishing House. (In Polish)."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wlodarczyk-Sielicka, M., Lubczonek, J., and Stateczny, A. (2016, January 10\u201312). Comparison of Selected Clustering Algorithms of Raw Data Obtained by Interferometric Methods Using Artificial Neural Networks. Proceedings of the 2016 17th International Radar Symposium (IRS), Krakow, Poland.","DOI":"10.1109\/IRS.2016.7497290"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dregvaite, G., and Damasevicius, R. (2016). Importance of neighborhood parameters during clustering of bathymetric data using neural network. International Conference on Information and Software Technologies, Springer.","DOI":"10.1007\/978-3-319-46254-7"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wlodarczyk-Sielicka, M., and Stateczny, A. (2015, January 24\u201326). Selection of SOM Parameters for the Needs of Clusterisation of Data Obtained by Interferometric Methods. Proceedings of the 2015 16th International Radar Symposium (IRS), Dresden, Germany.","DOI":"10.1109\/IRS.2015.7226268"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wlodarczyk-Sielicka, M., and Lubczonek, J. (2019). The Use of an Artificial Neural Network to Process Hydrographic Big Data during Surface Modeling. Computers, 8.","DOI":"10.3390\/computers8010026"},{"key":"ref_43","unstructured":"(Caris, Bathy DataBASE Manager\/Editor Reference Guide, 2011). Caris, Bathy DataBASE Manager\/Editor Reference Guide."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1610\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:15Z","timestamp":1760187795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1610"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,6]]},"references-count":43,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131610"],"URL":"https:\/\/doi.org\/10.3390\/rs11131610","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,6]]}}}