{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:49:33Z","timestamp":1772556573578,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T00:00:00Z","timestamp":1721260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"National Science Centre, Poland","doi-asserted-by":"publisher","award":["2021\/40\/C\/ST10\/00240"],"award-info":[{"award-number":["2021\/40\/C\/ST10\/00240"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The digital representation of seafloor, a challenge in UNESCO\u2019s Ocean Decade initiative, is essential for sustainable development support and marine environment protection, aligning with the United Nations\u2019 2030 program goals. Accuracy in seafloor representation can be achieved through remote sensing measurements, including acoustic and laser sources. Ground truth information integration facilitates comprehensive seafloor assessment. The current seafloor mapping paradigm benefits from the object-based image analysis (OBIA) approach, managing high-resolution remote sensing measurements effectively. A critical OBIA step is the segmentation process, with various algorithms available. Recent artificial intelligence advancements have led to AI-powered segmentation algorithms development, like the Segment Anything Model (SAM) by META AI. This paper presents the SAM approach\u2019s first evaluation for seafloor mapping. The benchmark remote sensing dataset refers to Puck Lagoon, Poland and includes measurements from various sources, primarily multibeam echosounders, bathymetric lidar, airborne photogrammetry, and satellite imagery. The SAM algorithm\u2019s performance was evaluated on an affordable workstation equipped with an NVIDIA GPU, enabling CUDA architecture utilization. The growing popularity and demand for AI-based services predict their widespread application in future underwater remote sensing studies, regardless of the measurement technology used (acoustic, laser, or imagery). Applying SAM in Puck Lagoon seafloor mapping may benefit other seafloor mapping studies intending to employ AI technology.<\/jats:p>","DOI":"10.3390\/rs16142638","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T16:55:36Z","timestamp":1721321736000},"page":"2638","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Application and Evaluation of the AI-Powered Segment Anything Model (SAM) in Seafloor Mapping: A Case Study from Puck Lagoon, Poland"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2103-9230","authenticated-orcid":false,"given":"\u0141ukasz","family":"Janowski","sequence":"first","affiliation":[{"name":"Maritime Institute, Gdynia Maritime University, Roberta de Plelo 20, 80-548 Gda\u0144sk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2218-1570","authenticated-orcid":false,"given":"Rados\u0142aw","family":"Wr\u00f3blewski","sequence":"additional","affiliation":[{"name":"Department of Geophysics, University of Gdansk, Pi\u0142sudskiego 46, 81-378 Gdynia, Poland"},{"name":"MEWO S.A., Starogardzka 17A, 83-010 Straszyn, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guan, S., Qu, F., and Qiao, F. (2023). United Nations Decade of Ocean Science for Sustainable Development (2021\u20132030): From innovation of ocean science to science-based ocean governance. Front. Mar. Sci., 9.","DOI":"10.3389\/fmars.2022.1091598"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/BF01204150","article-title":"Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems","volume":"15","author":"Matsumoto","year":"1993","journal-title":"Mar. Geophys. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.1016\/j.jas.2012.12.021","article-title":"Airborne laser bathymetry\u2014Detecting and recording submerged archaeological sites from the air","volume":"40","author":"Doneus","year":"2013","journal-title":"J. Archaeol. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sagawa, T., Yamashita, Y., Okumura, T., and Yamanokuchi, T. (2019). Satellite derived bathymetry using machine learning and multi-temporal satellite images. Remote Sens., 11.","DOI":"10.3390\/rs11101155"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.joes.2021.02.006","article-title":"Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research","volume":"6","author":"Ashphaq","year":"2021","journal-title":"J. Ocean. Eng. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1038\/s41597-024-03199-y","article-title":"High resolution optical and acoustic remote sensing datasets of the Puck Lagoon","volume":"11","author":"Janowski","year":"2024","journal-title":"Sci. Data"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.5194\/isprs-annals-X-1-W1-2023-1123-2023","article-title":"A decade of progress in topo-bathymetric laser scanning exemplified by the pielach river dataset","volume":"X-1\/W1-2023","author":"Mandlburger","year":"2023","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106615","DOI":"10.1016\/j.enggeo.2022.106615","article-title":"Automatic classification and mapping of the seabed using airborne LiDAR bathymetry","volume":"301","author":"Janowski","year":"2022","journal-title":"Eng. Geol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108699","DOI":"10.1016\/j.ecss.2023.108599","article-title":"Benthic habitat mapping: A review of three decades of mapping biological patterns on the seafloor","volume":"296","author":"Misiuk","year":"2024","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.ecss.2011.02.007","article-title":"Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques","volume":"92","author":"Brown","year":"2011","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"259","DOI":"10.3354\/meps11378","article-title":"Spatial scale and geographic context in benthic habitat mapping: Review and future directions","volume":"535","author":"Lecours","year":"2015","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_12","unstructured":"International Hydrographic Organization (2020). IHO Standards for Hydrographic Surveys S-44 ed. 6.0."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11001-017-9315-6","article-title":"Recommendations for improved and coherent acquisition and processing of backscatter data from seafloor-mapping sonars","volume":"39","author":"Lamarche","year":"2017","journal-title":"Mar. Geophys. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H.Z., Rolland, C., Gustafson, L., Xiao, T.T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023, January 2\u20136). Segment Anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_18","first-page":"103540","article-title":"The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot","volume":"124","author":"Osco","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nanni, L., Fusaro, D., Fantozzi, C., and Pretto, A. (2023). Improving existing segmentators performance with zero-shot segmentators. Entropy, 25.","DOI":"10.20944\/preprints202307.1729.v1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102918","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: An experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shi, P.L., Qiu, J.N., Abaxi, S.M.D., Wei, H., Lo, F.P.W., and Yuan, W. (2023). Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation. Diagnostics, 13.","DOI":"10.3390\/diagnostics13111947"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"211","DOI":"10.5582\/bst.2023.01128","article-title":"The ability of Segmenting Anything Model (SAM) to segment ultrasound images","volume":"17","author":"Chen","year":"2023","journal-title":"Biosci. Trends"},{"key":"ref_24","first-page":"5611711","article-title":"Adapting Segment Anything Model for Change Detection in VHR Remote Sensing Images","volume":"62","author":"Ding","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y.Q., Wang, D.D., Yuan, C., Li, H., and Hu, J. (2023). Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter. Sensors, 23.","DOI":"10.3390\/s23187884"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115797","DOI":"10.1016\/j.icarus.2023.115797","article-title":"A flexible deep learning crater detection scheme using Segment Anything Model (SAM)","volume":"408","author":"Giannakis","year":"2024","journal-title":"Icarus"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"R\u00e9by, E., Guilhelm, A., and De Luca, L. (2023, January 2\u20136). Semantic Segmentation using Foundation Models for Cultural Heritage: An Experimental Study on Notre-Dame de Paris. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCVW60793.2023.00184"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"113202","DOI":"10.1016\/j.oceaneng.2022.113202","article-title":"Object perception in underwater environments: A survey on sensors and sensing methodologies","volume":"267","author":"Huy","year":"2023","journal-title":"Ocean. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ecss.2012.11.001","article-title":"Do marine substrates \u2018look\u2019 and \u2018sound\u2019 the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images","volume":"117","author":"Lucieer","year":"2013","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Stephens, D., and Diesing, M. (2014). A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0093950"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.1093\/icesjms\/fsw118","article-title":"Image-based seabed classification: What can we learn from terrestrial remote sensing?","volume":"73","author":"Diesing","year":"2016","journal-title":"ICES J. Mar. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s11001-017-9338-z","article-title":"Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters","volume":"39","author":"Ierodiaconou","year":"2018","journal-title":"Mar. Geophys. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.oceano.2021.03.001","article-title":"Distribution and extent of benthic habitats in Puck Bay (Gulf of Gda\u0144sk, southern Baltic Sea)","volume":"63","author":"Jankowska","year":"2021","journal-title":"Oceanologia"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"220","DOI":"10.2478\/oandhs-2021-0019","article-title":"Characteristics of morphodynamic conditions in the shallows of Puck Bay (southern Baltic Sea)","volume":"50","author":"Szymczak","year":"2021","journal-title":"Oceanol. Hydrobiol. Stud."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1007\/s10750-022-04893-x","article-title":"The effect of salinity on the development of freshwater pike (Esox lucius) eggs in the context of drastic pike population decline in Puck Lagoon, Baltic Sea","volume":"849","author":"Greszkiewicz","year":"2022","journal-title":"Hydrobiologia"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3750\/AIP1991.21.S.09","article-title":"Long-term changes in the structure of underwater meadows of the Puck Lagoon","volume":"21","year":"1991","journal-title":"Acta Ichthyol. Piscat."},{"key":"ref_37","first-page":"1","article-title":"The role of benthic macrofauna in the trophic transfer of mercury in a low-diversity temperate coastal ecosystem (Puck Lagoon, southern Baltic Sea)","volume":"191","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0048-9697(97)00333-1","article-title":"Marine pollution in Gdansk Bay, Puck Bay and the Vistula lagoon, Poland: An overview","volume":"212","author":"Glasby","year":"1998","journal-title":"Sci. Total Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3750\/AIP1991.21.S.03","article-title":"A study on pollution of the Puck Lagoon and possibility of restoring the lagoon\u2032s original ecological state","volume":"21","author":"Ciszewski","year":"1991","journal-title":"Acta Ichthyol. Piscat."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105","DOI":"10.5200\/baltica.2014.27.20","article-title":"Sediment deposition in the Puck Lagoon (Southern Baltic Sea, Poland)","volume":"27","author":"Szmytkiewicz","year":"2014","journal-title":"Baltica"},{"key":"ref_41","first-page":"3","article-title":"Holocene shoreline migrations in the Puck Lagoon (Southern Baltic Sea) based on the Rzucewo Headland case study","volume":"4","year":"2003","journal-title":"Landf. Anal."},{"key":"ref_42","unstructured":"Kramarska, R., U\u015bcinowicz, S., Zachowicz, J., and Kawi\u0144ska, M. (1995). Origin and evolution of the Puck Lagoon. J. Coast. Res., 187\u2013191."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1177\/0959683620950451","article-title":"Climate and sea level variability on a centennial time scale over the last 1500\u2009years as inferred from the Coastal Peatland of Puck Lagoon (southern Baltic Sea)","volume":"30","author":"Witak","year":"2020","journal-title":"Holocene"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"100367","DOI":"10.1016\/j.atech.2023.100367","article-title":"The Segment Anything Model (SAM) for accelerating the smart farming revolution","volume":"6","author":"Carraro","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_45","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ren, S., Luzi, F., Lahrichi, S., Kassaw, K., Collins, L.M., Bradbury, K., and Malof, J.M. (2024, January 3\u20138). Segment anything, from space?. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV57701.2024.00817"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5663","DOI":"10.21105\/joss.05663","article-title":"samgeo: A Python package for segmenting geospatial data with the Segment Anything Model (SAM)","volume":"8","author":"Wu","year":"2023","journal-title":"J. Open Source Softw."},{"key":"ref_48","unstructured":"He, S., Bao, R., Li, J., Grant, P.E., and Ou, Y. (2023). Accuracy of segment-anything model (sam) in medical image segmentation tasks. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3654704","article-title":"Zero-shot segmentation of eye features using the segment anything model (sam)","volume":"7","author":"Maquiling","year":"2024","journal-title":"Proc. ACM Comput. Graph. Interact. Tech."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Summers, G., Lim, A., and Wheeler, A.J. (2021). A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach. Remote Sens., 13.","DOI":"10.3390\/rs13122317"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Prampolini, M., Angeletti, L., Castellan, G., Grande, V., Le Bas, T., Taviani, M., and Foglini, F. (2021). Benthic Habitat Map of the Southern Adriatic Sea (Mediterranean Sea) from Object-Based Image Analysis of Multi-Source Acoustic Backscatter Data. Remote Sens., 13.","DOI":"10.3390\/rs13152913"},{"key":"ref_52","unstructured":"Baatz, M., and Schape, A. (2000). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation, Angewandte Geographische Informationsverarbeitung."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.csr.2014.05.004","article-title":"Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches","volume":"84","author":"Diesing","year":"2014","journal-title":"Cont. Shelf Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ecss.2015.12.014","article-title":"Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats\u2014Application to the Venice Lagoon, Italy","volume":"170","author":"Madricardo","year":"2016","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"ett.3998","DOI":"10.1002\/ett.3998","article-title":"Review on remote sensing methods for landslide detection using machine and deep learning","volume":"32","author":"Mohan","year":"2020","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_59","first-page":"397","article-title":"Accuracy assessment: A user\u2019s perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_61","unstructured":"Wei, Y., Luo, S., Xu, C., Fu, Y., Dong, Q., Zhang, Y., Qu, F., Cheng, G., Ho, Y.-P., and Ho, H.-P. (2024). SAM-dPCR: Real-Time and High-throughput Absolute Quantification of Biological Samples Using Zero-Shot Segment Anything Model. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1080\/17451000.2014.962541","article-title":"Diversity and environmental control of benthic harpacticoids of an offshore post-dredging pit in coastal waters of Puck Bay, Baltic Sea","volume":"11","author":"Kotwicki","year":"2015","journal-title":"Mar. Biol. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"109","DOI":"10.2478\/v10009-009-0016-6","article-title":"The Puck Bay as an example of deep dredging unfavorably affecting the aquatic environment","volume":"38","author":"Graca","year":"2009","journal-title":"Oceanol. Hydrobiol. Stud."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.ecss.2006.02.018","article-title":"Benthic re-colonization in post-dredging pits in the puck bay (Southern Baltic sea)","volume":"68","author":"Szymelfenig","year":"2006","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Masetti, G., Mayer, L., and Ward, L. (2018). A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation. Geosciences, 8.","DOI":"10.3390\/geosciences8010014"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nke, M., Wiesenberg, L., Schulze, I., Wilken, D., Darr, A., Papenmeier, S., and Feldens, P. (2019). Impact of Sparse Benthic Life on Seafloor Roughness and High-Frequency Acoustic Scatter. Geosciences, 9.","DOI":"10.3390\/geosciences9100454"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11001-017-9318-3","article-title":"Analysis of seafloor backscatter strength dependence on the survey azimuth using multibeam echosounder data","volume":"39","author":"Lurton","year":"2017","journal-title":"Mar. Geophys. Res."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hao, S., Cui, Y., and Wang, J. (2021). Segmentation scale effect analysis in the object-oriented method of high-spatial-resolution image classification. Sensors, 21.","DOI":"10.3390\/s21237935"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"149712","DOI":"10.1016\/j.scitotenv.2021.149712","article-title":"Offshore benthic habitat mapping based on object-based image analysis and geomorphometric approach. A case study from the Slupsk Bank, Southern Baltic Sea","volume":"801","author":"Janowski","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_70","unstructured":"Janowski, \u0141., Skarlatos, D., Agrafiotis, P., Tysi\u0105c, P., Pydyn, A., Popek, M., Kotarba-Morley, A., Mandlburger, G., Gajewski, \u0141., and Kolakowski, M. (2023). Bathymetry and Remote Sensing Data of the Puck Lagoon, Southern Baltic Sea, Interdisciplinary Earth Data Alliance."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2638\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:19:21Z","timestamp":1760109561000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2638"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,18]]},"references-count":70,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16142638"],"URL":"https:\/\/doi.org\/10.3390\/rs16142638","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,18]]}}}