[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124471-en":3,"doc-seo-124471-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124471,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","MACHINE LEARNING - XXXVIII cycle - Machine Learning for Automated Recognition of Seabed Morphologies","Seabed environments contain diverse morphological features that document geological and geomorphological processes along continental margins. Their systematic recognition is essential for marine geohazard assessment and for designing offshore infrastructures, yet conventional bathymetric mapping depends largely on manual expert interpretation, making results slow, subjective, and hard to reproduce consistently over large areas. This thesis investigates machine learning methods to automatically recognize seabed morphological elements from high-resolution bathymetric derivatives. A U-Net-based convolutional approach is trained on MaGIC Project data, incorporating preprocessing, data balancing, and a bidirectional neighborhood metric tailored for elongated and discontinuous structures, improving segmentation quality while supporting reproducible geological interpretation.","UNIVERSITÀ DEGLI STUDI DI TRIESTE  \nXXXVIII CICLO DEL DOTTORATO DI RICERCA IN  \nAPPLIED DATA SCIENCE AND ARTIFICIAL INTELLIGENCE  \nIstituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS)  \nMACHINE LEARNING  \nFOR AUTOMATED RECOGNITION  \nOF SEABED MORPHOLOGIES  \nSettore scientifico-disciplinare: INF/01  \nDOTTORANDO  \nUMBERTO DI LAUDO  \nCOORDINATORE  \nPROF. FRANCESCO PAULI  \nSUPERVISORE DI TESI  \nPROF. LUCA MANZONI  \nSUPERVISORE DI TESI  \nDOTT.SSA SILVIA CERAMICOLA  \nANNO ACCADEMICO 2024/2025  \nU!\"\\#$%&\"’( )$*+\" S’,)\" )\" T%\"$&’$  \nPh.D. in Applied Data Science & Arti!cial Intelligence  \nXXXVIII cycle  \nMachine Learning for Automated Recognition of Seabed Morphologies  \nCandidate  \nUmberto Di Laudo  \nSupervisors  \nProf. Luca Manzoni Dr. Silvia Ceramicola  \nSummary  \nThe seabed hosts a wide variety of morphological features that record the geological and geomorphological processes shaping continental margins. Their systematic recognition is fundamental for marine geohazard assessment and for the planning of o!shore infrastructures. However, conventional mapping relies heavily on manual interpretation of bathymetric data, which is time-consuming, subjective, and di\"cult to reproduce consistently across large areas. This thesis explores the use of machine learning for the automated recognition of seabed morphological elements from high-resolution bathymetric derivatives. A convolutional architecture based on U-Net is developed and applied to adataset from the MaGIC Project, covering selected regions of the Italian continental margins. The proposed framework includes dedicated preprocessing, data balancing, and abidirectional neighborhood-based metric speci\\#cally designed to evaluate elongated and discontinuous structures that are poorly captured by standard pixel-wise scores.  \nThree model con\\#gurations are tested, multi-class (reduced-class, and binary) to analyze how class granularity a!ects performance and interpretability. Overall, the proposed framework bridges quantitative image segmentation with geological interpretation, demonstrating that deep learning can e!ectively support and accelerate seabed mapping, while preserving consistency and reproducibility across extensive marine domains.  \nContents  \nSummary i  \n\" Introduction \"  \n$ . $ Motivations and Challenges ......................... $  \n$ . % Research Questions .............................. %  \n$ .& Structure of the Thesis ............................ &  \n$ .’ Publications .................................. ’  \nI Context and Background \\#  \n$ Background and Related Works %  \n% . $ Seabed Data .................................. (  \n% . % Seabed Morphological Elements ....................... )  \n% .& Machine Learning Techniques in Marine Geology ............. $*  \n% .& . $ From statistical to machine learning approaches .......... $$  \n% .& . % Object-Based Image Analysis (OBIA) ................ $$  \n% .& .& Deep learning for sea+oor characterization ............ $%  \n% .& .’ Towards the detection of linear features .............. $&  \n% .’ Deep Learning Foundations for Image Analysis .............. $&  \n% .’. $ Convolutional Neural Networks ................... $&  \n% .’. % Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . $,  \n% .’.& U-Net architecture . . . . . . . . . . . . . . . . . . . . . . . . . . %&  \nII Problem Statement and Experimental Framework $%  \n& Problem Statement and Dataset $’  \n& . $ Problem De\\#nition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . %)  \n& . % Dataset Description and Analysis . . . . . . . . . . . . . . . . . . . . . . &*  \n( Methodology &’  \n’. $ U-net-based Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . &)  \n’. % Pipeline +owchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ’$  \n’. % . $ Input preprocessing ......................... ’$  \n’. % . % Training ................................ ’&  \n’. % .& Post processing ............................ ’-  \n’","cbCaitLZIUJkebvK","https://ap.wps.com/l/cbCaitLZIUJkebvK","pdf",13612955,1,110,"English","en",105,"# Summary\n# Introduction\n## Motivations and Challenges\n## Research Questions\n## Structure of the Thesis\n## Publications\n# Context and Background\n## Background and Related Works\n## Seabed Data\n## Seabed Morphological Elements\n## Machine Learning Techniques in Marine Geology\n# Problem Statement and Experimental Framework\n## Problem Statement and Dataset\n## Methodology\n# Experiments and Results\n## Main multi-class model\n## Simplified model configurations\n## Discussion\n## Limitations\n# Conclusions and Open Research Questions\n## Conclusions\n## Open Research Directions","[{\"question\":\"Why is automated recognition of seabed morphologies important?\",\"answer\":\"Systematic recognition supports marine geohazard assessment and offshore infrastructure planning. Manual mapping is slow, subjective, and difficult to reproduce consistently over large regions.\"},{\"question\":\"What modeling approach does the thesis propose?\",\"answer\":\"The thesis develops a convolutional architecture based on U-Net for image segmentation of seabed morphological elements from high-resolution bathymetric derivatives.\"},{\"question\":\"How does the framework evaluate structures that are hard for standard metrics?\",\"answer\":\"It includes preprocessing, data balancing, and a bidirectional neighborhood-based metric specifically designed to better capture elongated and discontinuous structures that are poorly handled by pixel-wise scores.\"}]","MACHINE LEARNING - XXXVIII cycle - Machine Learning for Automated Recognition of Seabed Morphologies | PDF",1785822602,277,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-xxxviii-cycle-machine-learning-for-automated-recognition-of-seabed-morphologies","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-xxxviii-cycle-machine-learning-for-automated-recognition-of-seabed-morphologies/124471/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is automated recognition of seabed morphologies important?","Question",{"text":75,"@type":76},"Systematic recognition supports marine geohazard assessment and offshore infrastructure planning. Manual mapping is slow, subjective, and difficult to reproduce consistently over large regions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What modeling approach does the thesis propose?",{"text":80,"@type":76},"The thesis develops a convolutional architecture based on U-Net for image segmentation of seabed morphological elements from high-resolution bathymetric derivatives.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the framework evaluate structures that are hard for standard metrics?",{"text":84,"@type":76},"It includes preprocessing, data balancing, and a bidirectional neighborhood-based metric specifically designed to better capture elongated and discontinuous structures that are poorly handled by pixel-wise scores.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]