Systematic review: a bibliometric analysis of building technology and its potential applications to artificial intelligence in the field of cultural heritage conservation from 2013 to 2023

Abstract

The potential of AI in cultural heritage conservation (CHC) is well-recognized, yet past research has predominantly focused on application-oriented studies, lacking systematic reviews of technological advancements, trends, and challenges. This study addresses this gap using bibliometric analysis to examine developments in CHC from 2013 to 2023. AI’s role in CHC has progressed through three phases: marginalization (pre-2017), rapid growth (2017–2019), and stabilization (post-2019), with steady growth since 2016. CHC research now spans disciplines such as remote sensing, computer imaging science, geography, and electrical engineering, emphasizing topics like HBIM and VR. While applications of computer graphics and photogrammetry primarily focus on morphological analysis, structural analysis remains underexplored. Challenges persist in acquiring complete point cloud data due to observational and perspective limitations. Current efforts aim to enhance CHC management through diversified technological solutions and systemic frameworks, improving health monitoring, restoration, and risk management of historic structures. This study systematically reviews the state of AI applications in CHC, identifying key technological trends and challenges, and highlights critical issues in interdisciplinary integration and application development. By addressing research gaps from the past decade, this study proposes a dynamic theoretical framework, offering clear directions and actionable insights for future AI applications in CHC.

Keywords

• artificial intelligence (ai)
• cultural heritage conservation
• heritage building information model (hbim)
• application progress
• bibliometrics