Digitising the Deep Past: Machine Learning for Rock Art Motif Classification in an Educational Citizen Science Application

IF 2.1 3区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Richard Turner-Jones, Gervase Tuxworth, Robert Haubt, Lynley A. Wallis
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引用次数: 0

Abstract

Digitising The Deep Past (DDP) is an interdisciplinary project based at Griffith University, Australia, that innovates in three areas: Indigenous cultural heritage, Indigenous education, and Machine Learning (ML) and Artificial Intelligence (AI). The project investigates the use of a purpose-built citizen science application that engages Indigenous youth in educational exercises rooted in local cultural heritage, specifically rock art, making learning more engaging and exposing them to digital technologies. Furthermore, ML models trained with the data gathered through these educational activities can then assist with classifying new rock art images and assisting rangers and archaeologists with site archiving and conservation efforts. This paper discusses the project's significance in enhancing Indigenous science and technology education and outlines its results in utilising ML for rock art classification. Adopting deep learning in rock art classification offers a compelling avenue for the automated analysis and interpretation of heritage objects and places. However, training deep neural networks from scratch often requires enormous datasets and computational resources, posing challenges for domain-specific applications with smaller datasets. With a dataset comprising approximately 3,100 labelled rock art images, we evaluated various tools within the transfer learning toolbox using three prominent pre-trained architectures: VGG19, ResNet50, and EfficientNet V2 S. Through the collaborative efforts of Indigenous students and ML, we demonstrate that even with limited training resources, using transfer learning to re-purpose an existing model can achieve motif classification Top-1 accuracy of 79.76% and Top-5 of 94.56%. The project ran from 2021 to 2023, including three week-long sessions with students of Laura State School to trial the citizen science app and the evaluation, development and refinement of the ML models.

The DDP project not only serves as a beacon for community-centric research but also forges a new frontier in integrating Indigenous cultural heritage with modern technology. The impact reaches beyond academia, directly enriching the educational experience for Indigenous students in Laura and equipping local rangers and archaeologists with advanced tools for rock art conservation.

数字化深邃的过去:教育性公民科学应用中岩画图案分类的机器学习
过去深处的数字化(Digitising The Deep Past,DDP)是澳大利亚格里菲斯大学的一个跨学科项目,在三个领域进行创新:该项目在三个领域进行创新:土著文化遗产、土著教育以及机器学习(ML)和人工智能(AI)。该项目研究如何使用专门构建的公民科学应用程序,让土著青年参与植根于当地文化遗产(特别是岩石艺术)的教育活动,使学习更具吸引力,并让他们接触数字技术。此外,通过这些教育活动收集的数据训练出的 ML 模型可以帮助对新的岩石艺术图像进行分类,并协助护林员和考古学家进行遗址归档和保护工作。本文讨论了该项目在加强土著科技教育方面的意义,并概述了其在利用 ML 进行岩画分类方面取得的成果。在岩石艺术分类中采用深度学习为遗产物品和场所的自动分析和解释提供了一条引人注目的途径。然而,从头开始训练深度神经网络往往需要庞大的数据集和计算资源,这给使用较小数据集的特定领域应用带来了挑战。我们利用一个由大约 3100 张标注了岩画图像的数据集,使用三种著名的预训练架构,对迁移学习工具箱中的各种工具进行了评估:通过土著学生和 ML 的共同努力,我们证明了即使在训练资源有限的情况下,使用迁移学习来重新利用现有模型也能实现 79.76% 的图案分类 Top-1 准确率和 94.56% 的 Top-5 准确率。该项目从 2021 年持续到 2023 年,其中包括与劳拉州立学校的学生进行为期三周的公民科学应用程序试用,以及评估、开发和完善 ML 模型。DDP 项目不仅是以社区为中心的研究的灯塔,还开辟了将土著文化遗产与现代技术相结合的新领域。其影响超越了学术界,直接丰富了劳拉土著学生的教育体验,并为当地护林员和考古学家提供了保护岩石艺术的先进工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Journal on Computing and Cultural Heritage
ACM Journal on Computing and Cultural Heritage Arts and Humanities-Conservation
CiteScore
4.60
自引率
8.30%
发文量
90
期刊介绍: ACM Journal on Computing and Cultural Heritage (JOCCH) publishes papers of significant and lasting value in all areas relating to the use of information and communication technologies (ICT) in support of Cultural Heritage. The journal encourages the submission of manuscripts that demonstrate innovative use of technology for the discovery, analysis, interpretation and presentation of cultural material, as well as manuscripts that illustrate applications in the Cultural Heritage sector that challenge the computational technologies and suggest new research opportunities in computer science.
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