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Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence

2023-07Journal article Open access
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dc.contributor.authorCanedo, Daniel
dc.contributor.authorFonte, João
dc.contributor.authorSeco, Luis Gonçalves
dc.contributor.authorVázquez, Marta
dc.contributor.authorDias, Rita
dc.contributor.authorPereiro, Tiago do
dc.contributor.authorHipólito, João
dc.contributor.authorMenéndez-Marsh, Fernando
dc.contributor.authorGeorgieva, Petia
dc.contributor.authorNeves, António J. R.
dc.date.accessioned2023-07-28T11:10:13Z
dc.date.available2023-07-28T11:10:13Z
dc.date.issued2023-07
dc.description.abstractMapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.pt_PT
dc.description.sponsorshipThis work was supported by the Project Odyssey: Platform for Automated Sensing in Archaeology Co-Financed by COMPETE 2020 and Regional Operational Program Lisboa 2020 through Portugal 2020 and FEDER under Grant ALG-01-0247-FEDER-070150.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/ACCESS.2023.3290305pt_PT
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.1/19891
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArchaeologypt_PT
dc.subjectData-centric artificial intelligencept_PT
dc.subjectData augmentationpt_PT
dc.subjectDeep learningpt_PT
dc.subjectLiDARpt_PT
dc.subjectLocation-based rankingpt_PT
dc.subjectObject detectionpt_PT
dc.titleUncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligencept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage65619pt_PT
oaire.citation.startPage65608pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume11pt_PT
person.familyNameDias
person.familyNamedo Pereiro
person.givenNameRita
person.givenNameTiago
person.identifier.ciencia-idF81F-AC62-BBC0
person.identifier.orcid0000-0003-2999-3133
person.identifier.orcid0000-0003-2691-4583
person.identifier.scopus-author-id55458374800
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication70a82b34-2ca4-494d-9789-6a4da9c97e27
relation.isAuthorOfPublicationaa2ce8de-97a5-459c-b28c-2b75e7d31405
relation.isAuthorOfPublication.latestForDiscovery70a82b34-2ca4-494d-9789-6a4da9c97e27

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