Development of a neural network approach for automated recognition of prehistoric carvings at Stonehenge

    Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review

    Abstract

    Dense lichen covers roughly a quarter of the above-ground stone surfaces at Stonehenge, which renders them inaccessible to study. A to¬tal of 72 Early Bronze Age carvings have recently been found on the bare stone surfaces, prompt-ing concerns that lichen may be obscuring pre¬historic rock art. As a first step towards creat¬ing a technique for revealing carvings beneath lichen, photography-derived 3D modelling and machine learning were combined to create a method for identifying carvings on bare stone surfaces. Tasked with differentiating between areas of the stone surfaces with and without carvings, the method achieved 84.2% accuracy. With further development, this work could be used by rock art conservators and archaeolo¬gists to verify carving findings, search for pre¬viously unidentified carvings and eventually reveal carvings hidden by lichen.
    Original languageEnglish
    Title of host publicationTranscending Boundaries: Integrated Approaches to Conservation.
    Subtitle of host publicationICOM-CC 19th Triennial Conference Preprints
    EditorsJanet Bridgland
    Place of PublicationParis
    PublisherInternational Council of Museums
    Publication statusAccepted/In press - 20 Nov 2020
    EventThe 19th ICOM-CC Triennial Conference - Beijing, China
    Duration: 17 May 202122 May 2021

    Conference

    ConferenceThe 19th ICOM-CC Triennial Conference
    Country/TerritoryChina
    CityBeijing
    Period17/05/2122/05/21

    Keywords

    • Stonehenge
    • rock art
    • lichen
    • Early Bronze Age
    • digital archaeology
    • machine learning
    • 3D models

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