{
    "o:id": 12858,
    "url": "https://chloe.cnr.it/s/BiDiAr/item/12858",
    "o:resource_template": "Academic Article",
    "o:resource_class": "bibo:AcademicArticle",
    "dcterms:title": [
        "A human–AI collaboration workflow for archaeological sites detection"
    ],
    "dcterms:creator": [
        "Casini, Luca",
        "Marchetti, Nicolò",
        "Montanucci, Andrea",
        "Orrù, Valentina",
        "Roccetti, Marco"
    ],
    "dcterms:date": [
        "2023"
    ],
    "dcterms:language": [
        "eng"
    ],
    "dcterms:rights": [
        "2023 The Author(s)"
    ],
    "dcterms: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."
    ],
    "dcterms:isPartOf": [
        "Scientific Reports"
    ],
    "bibo:citedBy": [
        "11112"
    ],
    "bibo:doi": [
        "https://doi.org/10.1038/s41598-023-36015-5"
    ],
    "bibo:issn": [
        "2045-2322"
    ],
    "bibo:issue": [
        "1"
    ],
    "bibo:pages": [
        "8699"
    ],
    "bibo:uri": [
        "https://www.nature.com/articles/s41598-023-36015-5"
    ],
    "bibo:volume": [
        "13"
    ],
    "foaf:homepage": [
        "https://www.zotero.org/groups/5293298/bidiar/items/T37LAM4I/item-list"
    ]
},