A deep-learning model for predictive archaeology and archaeological community detection

Abstract Deep learning is a powerful tool for exploring large datasets and discovering new patterns. This work presents an account of a metric learning-based deep convolutional neural network (CNN) applied to an archaeological dataset. The proposed account speaks of three stages: training, testing/v...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Abraham Resler, Reuven Yeshurun, Filipe Natalio, Raja Giryes
Formato:	article
Lenguaje:	EN
Publicado:	Springer Nature 2021
Materias:	History of scholarship and learning. The humanities AZ20-999 Social Sciences H
Acceso en línea:	https://doaj.org/article/9d943c62d5c046279d89872e62d1ab2c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9d943c62d5c046279d89872e62d1ab2c
record_format	dspace
spelling	oai:doaj.org-article:9d943c62d5c046279d89872e62d1ab2c2021-11-28T12:25:52ZA deep-learning model for predictive archaeology and archaeological community detection10.1057/s41599-021-00970-z2662-9992https://doaj.org/article/9d943c62d5c046279d89872e62d1ab2c2021-11-01T00:00:00Zhttps://doi.org/10.1057/s41599-021-00970-zhttps://doaj.org/toc/2662-9992Abstract Deep learning is a powerful tool for exploring large datasets and discovering new patterns. This work presents an account of a metric learning-based deep convolutional neural network (CNN) applied to an archaeological dataset. The proposed account speaks of three stages: training, testing/validating, and community detection. Several thousand artefact images, ranging from the Lower Palaeolithic period (1.4 million years ago) to the Late Islamic period (fourteenth century AD), were used to train the model (i.e., the CNN), to discern artefacts by site and period. After training, it attained a comparable accuracy to archaeologists in various periods. In order to test the model, it was called to identify new query images according to similarities with known (training) images. Validation blinding experiments showed that while archaeologists performed as well as the model within their field of expertise, they fell behind concerning other periods. Lastly, a community detection algorithm based on the confusion matrix data was used to discern affiliations across sites. A case-study on Levantine Natufian artefacts demonstrated the algorithm’s capacity to discern meaningful connections. As such, the model has the potential to reveal yet unknown patterns in archaeological data.Abraham ReslerReuven YeshurunFilipe NatalioRaja GiryesSpringer NaturearticleHistory of scholarship and learning. The humanitiesAZ20-999Social SciencesHENHumanities & Social Sciences Communications, Vol 8, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	History of scholarship and learning. The humanities AZ20-999 Social Sciences H
spellingShingle	History of scholarship and learning. The humanities AZ20-999 Social Sciences H Abraham Resler Reuven Yeshurun Filipe Natalio Raja Giryes A deep-learning model for predictive archaeology and archaeological community detection
description	Abstract Deep learning is a powerful tool for exploring large datasets and discovering new patterns. This work presents an account of a metric learning-based deep convolutional neural network (CNN) applied to an archaeological dataset. The proposed account speaks of three stages: training, testing/validating, and community detection. Several thousand artefact images, ranging from the Lower Palaeolithic period (1.4 million years ago) to the Late Islamic period (fourteenth century AD), were used to train the model (i.e., the CNN), to discern artefacts by site and period. After training, it attained a comparable accuracy to archaeologists in various periods. In order to test the model, it was called to identify new query images according to similarities with known (training) images. Validation blinding experiments showed that while archaeologists performed as well as the model within their field of expertise, they fell behind concerning other periods. Lastly, a community detection algorithm based on the confusion matrix data was used to discern affiliations across sites. A case-study on Levantine Natufian artefacts demonstrated the algorithm’s capacity to discern meaningful connections. As such, the model has the potential to reveal yet unknown patterns in archaeological data.
format	article
author	Abraham Resler Reuven Yeshurun Filipe Natalio Raja Giryes
author_facet	Abraham Resler Reuven Yeshurun Filipe Natalio Raja Giryes
author_sort	Abraham Resler
title	A deep-learning model for predictive archaeology and archaeological community detection
title_short	A deep-learning model for predictive archaeology and archaeological community detection
title_full	A deep-learning model for predictive archaeology and archaeological community detection
title_fullStr	A deep-learning model for predictive archaeology and archaeological community detection
title_full_unstemmed	A deep-learning model for predictive archaeology and archaeological community detection
title_sort	deep-learning model for predictive archaeology and archaeological community detection
publisher	Springer Nature
publishDate	2021
url	https://doaj.org/article/9d943c62d5c046279d89872e62d1ab2c
work_keys_str_mv	AT abrahamresler adeeplearningmodelforpredictivearchaeologyandarchaeologicalcommunitydetection AT reuvenyeshurun adeeplearningmodelforpredictivearchaeologyandarchaeologicalcommunitydetection AT filipenatalio adeeplearningmodelforpredictivearchaeologyandarchaeologicalcommunitydetection AT rajagiryes adeeplearningmodelforpredictivearchaeologyandarchaeologicalcommunitydetection AT abrahamresler deeplearningmodelforpredictivearchaeologyandarchaeologicalcommunitydetection AT reuvenyeshurun deeplearningmodelforpredictivearchaeologyandarchaeologicalcommunitydetection AT filipenatalio deeplearningmodelforpredictivearchaeologyandarchaeologicalcommunitydetection AT rajagiryes deeplearningmodelforpredictivearchaeologyandarchaeologicalcommunitydetection
_version_	1718407972651008000

A deep-learning model for predictive archaeology and archaeological community detection

Ejemplares similares