Andrea Jalandoni - Machine learning applications for rock art research

Rock art is globally recognized as significant, yet the resources allocated to the study and exploration of this important form of cultural heritage are often scarce. In 2022, I co-authored a paper that reflected on the potential of machine learning for rock art research to bypass this problem. As a proof-of-concept, we used open-source deep learning methods (VGG, ResNet , and Inception) to train a model to identify images with painted rock art (pictograms). In this presentation, I will evaluate what has developed so far and what is still needed.