Innovative multidisciplinary method using Machine Learning to define human behaviors and environments during the Caune de l'Arago (Tautavel, France) Middle Pleistocene occupations. - Archive ouverte HAL
Conference Papers Year : 2021

Innovative multidisciplinary method using Machine Learning to define human behaviors and environments during the Caune de l'Arago (Tautavel, France) Middle Pleistocene occupations.

Matthieu Boussard
  • Function : Author
Caroline Chopinaud
  • Function : Author
Agnès Testu
Thibaud Saos
Henry de Lumley
Philippe Carrez
Thierry Hervé
  • Function : Author

Abstract

SCHOPPER ANR project aim to develop a method to test research hypotheses using Machine Learning and immersives virtual environments. After 54 years of excavations, the Caune de l'Arago cave delivers a large amount of raw data and many archaeometric databases were developed through the study of this Lower Paleolithic site. The current tests focused on the evaluation of paleoenvironmental conditions and behavioral ones in each archaeological levels between 560,000 years and 90,000 years BP. All archaeological and environmental variable providing information on these issues were collected and organized by the multidisciplinary team to be studied by Machine Learning approach able to build models based on current repositories or Expert hypothesis, to learn on these models and then classify the archeological levels according to these patterns. The different steps of this innovative method are presented in this paper.
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Dates and versions

hal-03555248 , version 1 (13-10-2022)

Identifiers

  • HAL Id : hal-03555248 , version 1

Cite

Sophie Grégoire, Nicolas Boulbes, Bernard Quinio, Matthieu Boussard, Caroline Chopinaud, et al.. Innovative multidisciplinary method using Machine Learning to define human behaviors and environments during the Caune de l'Arago (Tautavel, France) Middle Pleistocene occupations.. XVIII UISPP World Congress, UISPP, Jun 2018, Paris, France. pp.28-47. ⟨hal-03555248⟩
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