Artificial Intelligence at the Interface between Cultural Heritage and Photography: A Systematic Literature Review

Abstract

Artificial intelligence has inspired a significant number of studies on the interface between cultural heritage and photography. The aims of these studies are, among others, to streamline damage monitoring or diagnoses for heritage preservation, enhance the production of high-fidelity 3D models of cultural assets, or improve the analysis of heritage images using computer vision. This article presents the results of a systematic literature review to highlight the recent state of these studies, published in the last five years and available in the Scopus, Web of Science, and JSTOR databases. The aim is to identify the potential and challenges of artificial intelligence through the connection between cultural heritage and photography, the latter of which represents a relevant methodological aspect in these investigations. In addition to the advances exemplified, the vast majority of studies indicate that there are also many obstacles to overcome. In particular, there is a need to improve artificial intelligence methods that still have significant flaws. These include inaccuracy in the automatic classification of images and limitations in the applications of the results. This article also aims to reflect on the meaning of these innovations when considering the direction of the relationship between cultural heritage and photography.

Keywords

• artificial intelligence
• cultural heritage
• photography
• machine learning
• deep learning
• neural network