Affiliation:
1. Griffith University, Australia
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.
Publisher
Association for Computing Machinery (ACM)
Reference47 articles.
1. Multi-scale 3D rock-art recording;Alexander Craig;Digital Applications in Archaeology and Cultural Heritage,2015
2. Rock Art Styles Rock Art Australia. 2022. Rock Art Styles - Rock Art Australia. https://rockartaustralia.org.au/rock-art/rock-art-styles/.
3. Léon Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT’2010: 19th International Conference on Computational Statistics Paris France, August 22-27, 2010 Keynote, Invited and Contributed Papers. Springer, Springer, Paris, France, 177–186.
4. Documenting and analyzing rock, paintings from Torres Strait, NE Australia, with digital photography and computer image enhancement;Brady Liam M;Journal of Field Archaeology,2006
5. An assessment of methods for the digital enhancement of rock paintings: the rock art from the precordillera of Arica (Chile) as a case study