{"refrec":{"BRefID":405420,"RR":"<b>Fraser, A.I.; Landauer, J.; Gaffney, V.; Zieschang, E.</b> (2024). Artificial interpretation: An investigation into the feasibility of archaeologically focused seismic interpretation via machine learning. <i>Heritage 7(5)</i>: 2491-2506. <a href=\"https://dx.doi.org/10.3390/heritage7050119\" target=\"_blank\">https://dx.doi.org/10.3390/heritage7050119</a>","BEntID":403215,"PublicFlag":1,"CheckedFlag":0,"wosflag":0,"vabbflag":0,"RefStringPartII":". <i>Heritage 7(5)</i>: 2491-2506. <a href=\"https://dx.doi.org/10.3390/heritage7050119\" target=\"_blank\">https://dx.doi.org/10.3390/heritage7050119</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Fraser, A.I.; Landauer, J.; Gaffney, V.; Zieschang, E.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Fraser, A.I. <i>et al.</i>","Englishabstract":"The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated. In specific 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 the sea. 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 km<sup>2</sup> 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 significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for a further, more complex, leveraging of AI interpretation for the study of submarine palaeolandscapes.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Artificial interpretation: An investigation into the feasibility of archaeologically focused seismic interpretation via machine learning","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2025-01-21 01:33:57.754484","timezone_type":1,"timezone":"+01:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"AI; semantic segmentation; deep learning; CNN; archaeology; submerged landscape; marine geophysics","OtherDescriptors":null,"Notes":null,"AnaPub":2024,"MonPub":null,"DateUpdate":"2025-01-14","DateCreate":"2025-01-14","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":null,"VABBcode":null,"OpenAcc":1,"DOI":"10.3390/heritage7050119"},"refs":null,"anarec":{"AnaID":405420,"PubliDate":2024,"Pagination":"2491-2506","XtraPublOfAnaID":null,"ISBN":null,"Volume":"7","Issue":"5","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":302578,"SerRR":"Heritage. 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