[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122927-en":3,"doc-seo-122927-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122927,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Artificial Interpretation - An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning","Artificial intelligence and machine learning increasingly support heritage research where remote-sensing datasets grow in size and resolution, making manual interpretation difficult and skilled human expertise limited. Geophysical interpretation for prehistoric submerged landscapes is especially challenging due to inaccessibility and extensive, sediment-covered features. Following the Last Glacial Maximum, large habitable landscapes were inundated and only became accessible through offshore remote-sensing data. This study applies machine learning to shallow seismic data from the southern North Sea, providing a proof-of-concept model for archaeologically significant feature detection and interpretable, verifiable results.","2.0  \n2.9  \nArticle  \nArtificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning  \nAndrew Iain Fraser, Jürgen Landauer, Vincent Gaffney and Elizabeth Zieschang  \nSpecial Issue  \nXR and Artificial Intelligence for Heritage  \nEdited by  \nDr. Bruno Fanini  \n[https://doi.org/10.3390/heritage70501](https://doi.org/10.3390/heritage70501) 19  \n heritage  \nArticle  \nArti􀀂cial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning  \nAndrew Iain Fraser 1, Jürgen Landauer 2, Vincent Gaffney 1, * and Elizabeth Zieschang 1  \nCitation: Fraser, A.I.; Landauer, J.; Gaffney, V.; Zieschang, E. Arti􀀂cial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning. Heritage 2024, 7, 2491–2506. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)heritage7050119  \nAcademic Editor: Nicola Masini  \nReceived: 28 February 2024  \nRevised: 26 April 2024  \nAccepted: 9 May 2024  \nPublished: 10 May 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Submerged Landscape Research Centre, School of Archaeological and Forensic Sciences, University of Bradford, Richmond Road, Bradford BD7 1DP, UK; [a.i.fraser1@bradford.ac.uk](a.i.fraser1@bradford.ac.uk) (A.I.F.); [ejzieschang@gmail.com](ejzieschang@gmail.com) (E.Z.)  \n2 Landauer Research, 71642 Ludwigsburg, Germany; [juergenlandauer@gmx.de](juergenlandauer@gmx.de)  \n* [Correspondence: v.gaffney@bradford.ac.uk](Correspondence: v.gaffney@bradford.ac.uk)  \nAbstract: The value of arti􀀂cial intelligence and machine learning applications for use in heritage research is increasingly appreciated. In speci􀀂c areas, notably remote sensing, datasets have increased in extent and resolution to the point that manual interpretation is problematic and the availability of skilled interpreters to undertake such work is limited. Interpretation of the geophysical datasets associated with prehistoric submerged landscapes is particularly challenging. Following the Last Glacial Maximum, sea levels rose by 120 m globally, and vast, habitable landscapes were lost to thesea. These landscapes were inaccessible until extensive remote sensing datasets were provided by the offshore energy sector. In this paper, we provide the results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c. 188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7000 BP, along with the cultural heritage it contained. As part of this project, machine learning tools were applied to detect and interpret features with potential archaeological signi􀀂cance from shallow seismic data. The output provides a proof-of-concept model demonstrating veri􀀂able results and the potential for a further, more complex, leveraging of AI interpretation for the study of submarine palaeolandscapes.  \nKeywords: AI; semantic segmentation; deep learning; CNN; archaeology; submerged landscape; marine geophysics  \n1. Introduction  \nArti􀀂cial intelligence (AI) has demonstrated ef􀀂cient, consistent, and high-quality solutions in many industries [1] where datasets are too large to be processed manually due to high resolution data acquisition, the vast geographic scales involved, or where resources are limited and investigation is costly and\\or time dependent [2] . The investigation of submerged landscapes suffers many of these problems. Areas of potential interest are frequently inaccessible and often beyond the scope of diver investigation. Locations of interest ","cbCaikMKq05N7bpC","https://ap.wps.com/l/cbCaikMKq05N7bpC","pdf",7808831,1,17,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"Why is manual interpretation difficult for submerged-landscape geophysical data?\",\"answer\":\"Remote-sensing datasets are large and high-resolution, skilled interpreters are limited, and potential sites are often hard to access and obscured by thick modern sediments. These factors make traditional, manual approaches costly and time-dependent.\"},{\"question\":\"What kind of data and study region are used in this research?\",\"answer\":\"The project uses shallow seismic data from the southern North Sea, where a large area of previously habitable land was inundated between roughly 20,000 BP and 7000 BP. The focus is on detecting features with potential archaeological significance.\"},{\"question\":\"What does the machine learning approach provide as an outcome?\",\"answer\":\"The study produces a proof-of-concept model that can detect and interpret potential archaeological features from shallow seismic data. The results are presented as verifiable, with potential for more complex AI-driven interpretation in future work.\"}]","Artificial Interpretation - An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning | PDF",1785813719,43,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"artificial-interpretation-an-investigation-into-the-feasibility-of-archaeologically-focused-seismic-interpretation-via-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/artificial-interpretation-an-investigation-into-the-feasibility-of-archaeologically-focused-seismic-interpretation-via-machine-learning/122927/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is manual interpretation difficult for submerged-landscape geophysical data?","Question",{"text":76,"@type":77},"Remote-sensing datasets are large and high-resolution, skilled interpreters are limited, and potential sites are often hard to access and obscured by thick modern sediments. These factors make traditional, manual approaches costly and time-dependent.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What kind of data and study region are used in this research?",{"text":81,"@type":77},"The project uses shallow seismic data from the southern North Sea, where a large area of previously habitable land was inundated between roughly 20,000 BP and 7000 BP. The focus is on detecting features with potential archaeological significance.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the machine learning approach provide as an outcome?",{"text":85,"@type":77},"The study produces a proof-of-concept model that can detect and interpret potential archaeological features from shallow seismic data. 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