{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T15:32:07Z","timestamp":1777995127385,"version":"3.51.4"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Cult. Herit."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>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 grounded 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 article discusses the project\u2019s significance in enhancing Indigenous science and technology education and outlines its results in using 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 labeled 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 3 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.<\/jats:p>\n          <jats:p>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.<\/jats:p>","DOI":"10.1145\/3665796","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T15:00:52Z","timestamp":1716390052000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Digitising the Deep Past: Machine Learning for Rock Art Motif Classification in an Educational Citizen Science Application"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0367-9701","authenticated-orcid":false,"given":"Richard N.","family":"Turner-Jones","sequence":"first","affiliation":[{"name":"Griffith University, Nathan, Queensland, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9278-4695","authenticated-orcid":false,"given":"Gervase","family":"Tuxworth","sequence":"additional","affiliation":[{"name":"Griffith University, Nathan, Queensland, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0542-5281","authenticated-orcid":false,"given":"Robert A.","family":"Haubt","sequence":"additional","affiliation":[{"name":"Griffith University, Nathan, Queensland, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9324-8069","authenticated-orcid":false,"given":"Lynley","family":"Wallis","sequence":"additional","affiliation":[{"name":"Griffith University, Nathan, Queensland, Australia"}]}],"member":"320","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.daach.2015.02.003"},{"key":"e_1_3_1_3_2","unstructured":"Rock Art Styles Rock Art Australia. 2022. 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