AI-based experts’ knowledge visualization of cultural heritage: A case study of Terracotta Warriors

Abstract

Advancements in 3D modeling, digital display technologies, and the growing availability of digital cultural heritage data have significantly improved the accuracy of heritage depictions and expanded opportunities for analysis. However, while many studies focus on presenting specific cultural heritage figurines, an often overlooked aspect is the visualization of the Terracotta Warriors as a unified entity. This involves concisely representing the distribution of features and their relationships, providing a clear and insightful presentation that engages practitioners, academics, and wider audiences. To tackle the challenges mentioned above, this research seeks to explore the application of AI methods in processing cultural heritage data. It aims to optimize and augment the dataset, analyze the distribution and relationships of various attributes, and interpret the analysis results through visualization techniques. The Terracotta Warriors, among China’s most significant cultural heritages and renowned for their abundance, exquisite workmanship, and magnitude, are chosen as a case study. The contribution of this paper is primarily twofold. Firstly, we constructed a dataset of Terracotta Warriors from Pit No. 1, detailing the attributes significant for identifying different Terracotta Warriors. Secondly, we employ various AI methods, such as generative adversarial network(GAN) and random forest, to process and analyze these attributes, followed by visualizing the analysis results for an intuitive presentation. This study introduces a novel scheme for presenting information on a collection of cultural relics, offering a practical case for analyzing and visualizing the Terracotta Warriors’ attributes as a whole entity, rather than showcasing individual relics’ information in isolation. This approach can be applied to other cultural heritage artifacts, such as Chinese ceramics, and supports further multidisciplinary research, including AI-based cultural heritage analysis, processing, and presentation.