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The Synergy between artificial intelligence, remote sensing, and archaeological fieldwork validation

2024-05-28Journal article Open access
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dc.contributor.authorCanedo, Daniel
dc.contributor.authorHipólito, João
dc.contributor.authorFonte, João
dc.contributor.authorDias, Rita
dc.contributor.authorPereiro, Tiago do
dc.contributor.authorGeorgieva, Petia
dc.contributor.authorGonçalves-Seco, Luís
dc.contributor.authorVázquez, Marta
dc.contributor.authorPires, Nelson
dc.contributor.authorFábrega-Álvarez, Pastor
dc.contributor.authorMenéndez-Marsh, Fernando
dc.contributor.authorNeves, António J. R.
dc.date.accessioned2024-07-16T12:46:29Z
dc.date.available2024-07-16T12:46:29Z
dc.date.issued2024-05-28
dc.description.abstractThe increasing relevance of remote sensing and artificial intelligence (AI) for archaeological research and cultural heritage management is undeniable. However, there is a critical gap in this field. Many studies conclude with identifying hundreds or even thousands of potential sites, but very few follow through with crucial fieldwork validation to confirm their existence. This research addresses this gap by proposing and implementing a fieldwork validation pipeline. In northern Portugal's Alto Minho region, we employed this pipeline to verify 237 potential burial mounds identified by an AI-powered algorithm. Fieldwork provided valuable information on the optimal conditions for burial mounds and the specific factors that led the algorithm to err. Based on these insights, we implemented two key improvements to the algorithm. First, we incorporated a slope map derived from LiDAR-generated terrain models to eliminate potential burial mound inferences in areas with high slopes. Second, we trained a Vision Transformer model using digital orthophotos of both confirmed burial mounds and previously identified False Positives. This further refines the algorithm's ability to distinguish genuine sites.eng
dc.description.sponsorshipALG-01-0247-FEDER-070150; UIDP/50008/2020
dc.identifier.doi10.3390/rs16111933
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10400.1/25637
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationInstituto de Telecomunicações
dc.relation.ispartofRemote Sensing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligence
dc.subjectRemote sensing
dc.subjectFieldwork validation
dc.subjectObject detection
dc.subjectVision transformer
dc.subjectLiDAR
dc.subjectArchaeology
dc.subjectBurial mounds
dc.titleThe Synergy between artificial intelligence, remote sensing, and archaeological fieldwork validationeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.citation.issue11
oaire.citation.startPage1933
oaire.citation.titleRemote Sensing
oaire.citation.volume16
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameDias
person.givenNameRita
person.identifier.ciencia-idF81F-AC62-BBC0
person.identifier.orcid0000-0003-2999-3133
person.identifier.scopus-author-id55458374800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication70a82b34-2ca4-494d-9789-6a4da9c97e27
relation.isAuthorOfPublication.latestForDiscovery70a82b34-2ca4-494d-9789-6a4da9c97e27
relation.isProjectOfPublicationfcbe2d59-4ccb-49ff-a429-eb61bca54349
relation.isProjectOfPublication.latestForDiscoveryfcbe2d59-4ccb-49ff-a429-eb61bca54349

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