id
	12858
Url
	https://chloe.cnr.it/s/BiDiAr/item/12858
Resource template
	Academic Article
Resource class
	bibo:AcademicArticle
Title
	A human–AI collaboration workflow for archaeological sites detection
Creator
	Casini, Luca
	Marchetti, Nicolò
	Montanucci, Andrea
	Orrù, Valentina
	Roccetti, Marco
Date
	2023
Language
	eng
Rights
	2023 The Author(s)
Abstract
	This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly available satellite imagery and vector shapes coming from a large corpus of annotations (i.e., surveyed sites). A randomized test showed that the best model reaches a detection accuracy in the neighborhood of 80%. Integrating domain expertise was crucial to define how to build the dataset and how to evaluate the predictions, since defining if a proposed mask counts as a prediction is very subjective. Furthermore, even an inaccurate prediction can be useful when put into context and interpreted by a trained archaeologist. Coming from these considerations we close the paper with a vision for a Human–AI collaboration workflow. Starting with an annotated dataset that is refined by the human expert we obtain a model whose predictions can either be combined to create a heatmap, to be overlaid on satellite and/or aerial imagery, or alternatively can be vectorized to make further analysis in a GIS software easier and automatic. In turn, the archaeologists can analyze the predictions, organize their onsite surveys, and refine the dataset with new, corrected, annotations.
Is Part Of
	Scientific Reports
Cited by
	11112
Doi
	https://doi.org/10.1038/s41598-023-36015-5
Issn
	2045-2322
Issue
	1
Pages
	8699
Uri
	https://www.nature.com/articles/s41598-023-36015-5
Volume
	13
Homepage
	https://www.zotero.org/groups/5293298/bidiar/items/T37LAM4I/item-list

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