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 areas where numerous rock art sites exist, much of the rock art is unidentified and therefore remains, unrecorded and unresearched. Manually identifying rock art is time-consuming, tedious, and expensive. Therefore, it is necessary to automate many processes in rock art research, which can be accomplished by Machine Learning. Artificial Intelligence (AI) and Machine Learning (ML) can greatly facilitate rock art research in many ways, such as through Object Recognition and Detection, Motif Extraction, Object Reconstruction, Image Knowledge Graphs, and Representations. This article is a reflective work on the future of ML for rock art research. As a proof-of-concept, it presents a machine learning method based on recent advances in deep learning to train a model to identify images with painted rock art (pictograms). The efficacy of the proposed method is shown using data collected from fieldwork in Australia. Furthermore, our proposed method can be used to train models that are specific to the rock art found in different regions. We provide the code and the trained models in the supplementary section.

岩石艺术在全球范围内被认为具有重要意义，但分配给研究和探索这一重要文化遗产形式的资源往往稀缺。在存在众多岩画遗址的地区，大部分岩画都无法辨认，因此未被记录和研究。手动识别岩石艺术既费时又乏味且成本​​高昂。因此，有必要将岩石艺术研究中的许多过程自动化，这可以通过机器学习来完成。人工智能 (AI) 和机器学习 (ML) 可以通过多种方式极大地促进岩画研究，例如通过对象识别和检测、图案提取、对象重建、图像知识图和表示。本文是关于 ML 用于岩石艺术研究的未来的反思性工作。作为概念验证，它提出了一种基于深度学习最新进展的机器学习方法，用于训练模型以识别带有彩绘岩石艺术（象形图）的图像。使用从澳大利亚实地工作收集的数据显示了所提出方法的有效性。此外，我们提出的方法可用于训练特定于不同地区岩石艺术的模型。我们在补充部分提供代码和训练好的模型。