Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/19891 |
Resumo: | Mapping 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. |
id |
RCAP_47a2e555ce407e1c8a70c973aa2619bf |
---|---|
oai_identifier_str |
oai:sapientia.ualg.pt:10400.1/19891 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligenceArchaeologyData-centric artificial intelligenceData augmentationDeep learningLiDARLocation-based rankingObject detectionMapping 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.This 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.IEEESapientiaCanedo, DanielFonte, JoãoSeco, Luis GonçalvesVázquez, MartaDias, RitaPereiro, Tiago doHipólito, JoãoMenéndez-Marsh, FernandoGeorgieva, PetiaNeves, António J. R.2023-07-28T11:10:13Z2023-072023-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/19891eng2169-353610.1109/ACCESS.2023.3290305info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-02T02:02:14Zoai:sapientia.ualg.pt:10400.1/19891Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:10:26.216778Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence |
title |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence |
spellingShingle |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence Canedo, Daniel Archaeology Data-centric artificial intelligence Data augmentation Deep learning LiDAR Location-based ranking Object detection |
title_short |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence |
title_full |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence |
title_fullStr |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence |
title_full_unstemmed |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence |
title_sort |
Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence |
author |
Canedo, Daniel |
author_facet |
Canedo, Daniel Fonte, João Seco, Luis Gonçalves Vázquez, Marta Dias, Rita Pereiro, Tiago do Hipólito, João Menéndez-Marsh, Fernando Georgieva, Petia Neves, António J. R. |
author_role |
author |
author2 |
Fonte, João Seco, Luis Gonçalves Vázquez, Marta Dias, Rita Pereiro, Tiago do Hipólito, João Menéndez-Marsh, Fernando Georgieva, Petia Neves, António J. R. |
author2_role |
author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Canedo, Daniel Fonte, João Seco, Luis Gonçalves Vázquez, Marta Dias, Rita Pereiro, Tiago do Hipólito, João Menéndez-Marsh, Fernando Georgieva, Petia Neves, António J. R. |
dc.subject.por.fl_str_mv |
Archaeology Data-centric artificial intelligence Data augmentation Deep learning LiDAR Location-based ranking Object detection |
topic |
Archaeology Data-centric artificial intelligence Data augmentation Deep learning LiDAR Location-based ranking Object detection |
description |
Mapping 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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-28T11:10:13Z 2023-07 2023-07-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.1/19891 |
url |
http://hdl.handle.net/10400.1/19891 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2169-3536 10.1109/ACCESS.2023.3290305 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799133354065395712 |