Deep learning-based classification of Yinshan rock art and future research directions

Abstract Rock art is universally recognized as a significant cultural heritage. However, resources dedicated to its research remain insufficient, leading to a lack of sustained attention and conservation efforts. As immovable cultural relics, numerous rock art sites across China and the globe have yet to be discovered, documented, studied, or made publicly accessible, with fundamental classification work still pending. And a substantial amount of rock art is located in mountainous areas, gullies, or on cliffs, making on-site manual identification and classification challenging. Furthermore, even when working with collected image datasets, the task of applying objective and consistent classification standards across large volumes of materials is notoriously difficult. Emerging digital humanities methods facilitate the automation and intellectualization of certain research steps, providing relatively objective, convenient, and efficient basic classification measures. This study focuses on the Yinshan rock art, employing deep learning algorithms to conduct a classification experiment. The results show a test set classification accuracy of approximately 80% and an overall F1-score exceeding 78%, validating the feasibility of digital humanities approaches in rock art studies. Future applications of digital humanities in this field promise to be even more diverse, encompassing tasks such as object detection and recognition in rock art, and the construction of knowledge graphs. These methods can offer new perspectives for the preservation and cultural transmission of rock art, thereby enriching research on Chinese rock art.