Markerless detection of ancient rock carvings in the wild: rock art in Vathy, Astypalaia

Tsigkas, Giorgos; Sfikas, Giorgos; Pasialis, Anastasios; Vlachopoulos, Andreas; Nikou, Christophoros

doi:10.1016/j.patrec.2020.03.026

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…Various approaches exist, such as the extraction of structural elements of buildings using deep neural network architectures [47]. The detection of ancient rock carvings using a deep-learning-based approach has also been described [48]. Another approach extracts monument architecture and important features of monuments from images using Neural Network [49].…”

Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

Digital Tools for Data Acquisition and Heritage Management in Archaeology and Their Impact on Archaeological Practices

Moullou,

Vital,

Sylaiou

et al. 2023

Heritage

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The significance of data acquisition in archaeological practice has consistently held great importance. Over the past few decades, the growing prevalence of digitization in acquiring data has significantly transformed the landscape of archaeological fieldwork, influencing both methodology and interpretation. The integration of digital photogrammetry and laser scanning technologies in archaeology has transformed data acquisition, enabling efficient and precise documentation. However, this digital shift raises concerns about information overload, the potential loss of on-site insights, and the need for suitable data management methods. Over the past 15 years, digital tools like photogrammetry, laser scanning, and unmanned aerial vehicles have advanced cultural heritage documentation. These methods offer detailed 3D models of archaeological sites, artifacts, and monuments, with evolving accessibility and user friendliness. This paper delves into methods for documenting cultural heritage, examining the implications of various approaches on the archaeologist’s workflow and on the field as a whole.

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Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

Digital Tools for Data Acquisition and Heritage Management in Archaeology and Their Impact on Archaeological Practices

Moullou,

Vital,

Sylaiou

et al. 2023

Heritage

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“…The forms of the images vary from photographs to stylized drawings of archaeological objects. Typically, ML has been used to identify “objects” within images, describe rock art and structural elements of buildings (Kogou et al 2020; Prasomphan and Jung 2017; Tsigkas et al 2020), and analyze designs as well as tool and vessel forms (e.g., Bevan et al 2014; Gualandi et al 2021; Nash and Prewitt 2016; Pawlowicz et al 2017); to identify shell or animal bone (Bickler 2018b; Huffer and Graham 2018); and to document use wear and damage on tools and ecofacts (Byeon et al 2019; Cifuentes-Alcobendas and Domínguez-Rodrigo 2019; Grove and Blinkhorn 2020).…”

Section: Machine Learning For Archaeological Datamentioning

confidence: 99%

Machine Learning Arrives in Archaeology

Bickler¹

2021

Adv. archaeol. pract.

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OverviewMachine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those approaches. Small datasets associated with archaeological work make ML vulnerable to hidden complexity, systemic bias, and high validation costs if not managed appropriately. ML's scalability, flexibility, and rapid development, however, make it an essential part of twenty-first-century archaeological practice. This review briefly describes what ML is, how it is being used in archaeology today, and where it might be used in the future for archaeological purposes.

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“…Machine learning approaches have been applied in archaeological context for object detection and more recently for stylistic analysis (Cintas et al 2020;Horr et al 2014;Tsigkas et al 2020;Wang et al 2017). Recent research efforts into computer vision and machine learning have shown the ability of learning algorithms to discriminate between stylistic categories of painted art, with reasonable accuracy (Karayev et al 2014;Saleh et al 2016;Shamir et al 2010).…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Reconstructing rock art chronology with transfer learning: A case study from Arnhem Land, Australia

Kowlessar

Keal²,

Wesley

et al. 2021

Australian Archaeology

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In recent years, machine learning approaches have been used to classify and extract style from media and have been used to reinforce known chronologies from classical art history. In this work we employ the first ever machine learning analysis of Australian rock art using a data efficient transfer learning approach to identify features suitable for distinguishing styles of rock art. These features are evaluated in a one-shot learning setting. Results demonstrate that known Arnhem Land Rock art styles can be resolved without knowledge of prior groupings. We then analyse the activation space of learned features and report on the relationships between styles and arrange these classes into a stylistic chronology based on distance within the activation space. By generating a stylistic chronology, it is shown that the model is sensitive to both temporal and spatial patterns in the distribution of rock art in the Arnhem Land Plateau region. More broadly, this approach is ideally suited to evaluating style within any material culture assemblage and overcomes the common constraint of small training data sets in archaeological machine learning studies.

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Markerless detection of ancient rock carvings in the wild: rock art in Vathy, Astypalaia

Cited by 12 publications

References 10 publications

Digital Tools for Data Acquisition and Heritage Management in Archaeology and Their Impact on Archaeological Practices

Digital Tools for Data Acquisition and Heritage Management in Archaeology and Their Impact on Archaeological Practices

Machine Learning Arrives in Archaeology

Reconstructing rock art chronology with transfer learning: A case study from Arnhem Land, Australia

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