Design Application and Evolution of 3D Visualization Technology in Architectural Heritage Conservation: A CiteSpace-Based Knowledge Mapping and Systematic Review (2005–2024)

Abstract

This study integrates quantitative scientometric analysis with a qualitative systematic review to comprehensively examine the evolution, core research themes, and emerging trends of three-dimensional (3D) visualization technology in architectural heritage conservation from 2005 to 2024. A total of 813 relevant publications were retrieved from the Web of Science Core Collection and analyzed using CiteSpace to construct a detailed knowledge map of the field. The findings highlight that foundational technologies such as terrestrial laser scanning (TLS), photogrammetry, building information modeling (BIM), and heritage building information modeling (HBIM) have laid a solid technical foundation for accurate heritage documentation and semantic representation. At the same time, the integration of digital twins, the Internet of Things (IoT), artificial intelligence (AI), and immersive technologies has facilitated a shift from static documentation to dynamic perception, real-time analysis, and interactive engagement. The analysis identifies four major research domains: (1) 3D data acquisition and modeling techniques, (2) digital heritage documentation and information management, (3) virtual reconstruction and interactive visualization, and (4) digital transformation and cultural narrative integration. Based on these insights, this study proposes four key directions for future research: advancing intelligence and automation in 3D modeling workflows; enhancing cross-platform interoperability and semantic standardization; realizing the full lifecycle management of architectural heritage; and enhancing cultural narratives through digital expression. This study provides a systematic and in-depth understanding of the role of 3D visualization in architectural heritage conservation. It offers a solid theoretical foundation and strategic guidance for technological innovation, policy development, and interdisciplinary collaboration in the digital heritage field.

1. Introduction

Amid the ongoing digital transformation in the field of architectural heritage preservation, three-dimensional (3D) visualization technology has emerged as a vital tool for the documentation, analysis, and dissemination of historical heritage, attracting increasing scholarly attention [1,2,3]. As a key repository of cultural memory and historical significance, architectural heritage faces mounting challenges related to conservation and adaptive reuse. Traditional preservation methods often exhibit significant limitations in terms of recording precision, risk monitoring, and public engagement, rendering them insufficient to meet contemporary demands for efficiency, accuracy, and interactivity [4,5,6]. With the rise of the “digital heritage” paradigm, 3D visualization technologies have assumed an increasingly central role, offering capabilities for efficient documentation, immersive representation, and scalable dissemination [7,8,9].

By integrating spatial data acquisition, geometric modeling, semantic information representation, and interactive engagement, 3D visualization technologies not only enhance the quality of digital documentation and restoration processes but also create new opportunities for education, cultural tourism, and disaster risk management [10,11]. Particularly in the post-pandemic era, the rising demand for virtual exhibitions and remote interactions has further underscored the critical role of 3D visualization in advancing the digital transformation of built heritage [12].

Current research has predominantly concentrated on the targeted applications of 3D visualization technologies within the domain of architectural heritage conservation, with a particular emphasis on high-precision digital modeling and the virtual reconstruction of individual buildings or localized heritage sites. Certain studies have focused on the meticulous recording of model information and the digital restoration of detailed architectural components, thereby underscoring the pursuit of physical fidelity in heritage representations [13,14,15]. Others have extended the discourse by examining the role of digital technologies in enhancing conservation, repair, and restoration strategies, highlighting the potential of 3D visualization as a transformative tool in heritage interventions [16]. Despite these significant contributions at the technological application level, the extant literature exhibits a notable deficiency in systematically synthesizing the broader developmental trajectory of 3D visualization technologies, the evolution of the underlying knowledge structures, and the shifting thematic priorities within the field. To a considerable extent, this limitation constrains the integration of interdisciplinary innovations, impedes methodological advancement, and restricts the expansion of future research agendas. Addressing these gaps requires the implementation of more comprehensive, critically reflective, and forward-looking research approaches.

In response to this gap, the present study integrates scientometric analysis with systematic review methodologies to systematically map the intellectual structure and developmental trajectories of 3D visualization technologies in architectural heritage preservation. By employing CiteSpace version 6.2.R4 software, this research constructs a knowledge map and conducts a visual analytic study of the relevant literature indexed in the Web of Science Core Collection between 2005 and 2024. It aims to delineate key research pathways, trace technological evolution trends, and identify thematic areas and emerging hotspots. Furthermore, through systematic content analysis, this study refines the core concepts and thematic frameworks underpinning the field, offering a comprehensive reference for theoretical development, technological innovation, and future interdisciplinary research in architectural heritage conservation.

2. Research Design

2.1. Research Methodology

This study employs a mixed-methods research design, integrating scientometric analysis with a systematic review to comprehensively map the intellectual landscape of 3D visualization technologies in architectural heritage preservation. It aims to trace the field’s evolutionary trajectory and critically evaluate its major thematic developments at a macro level [17,18]. By combining quantitative and qualitative methodological frameworks, this study seeks to reduce interpretive bias and enhance the epistemological rigor and empirical validity of its findings.

2.1.1. Methodology for Scientometric Analysis

As a widely recognized quantitative research method, scientometric analysis was employed in this study using CiteSpace, a software tool developed by Professor Chaomei Chen. CiteSpace enables the rapid extraction of key nodes and critical information from extensive bibliometric datasets and facilitates the visual mapping of research topic evolution. It has been widely applied in knowledge structure detection, hotspot tracking, and the identification of disciplinary development pathways [19]. In this study, CiteSpace was utilized to construct a comprehensive knowledge map of 3D visualization research within the field of architectural heritage conservation, aiming to systematically uncover the knowledge structure and major research foci, as well as their evolutionary trajectories.

Table 1. Scientometric analysis techniques and their core analytical dimensions.

Analysis Method	Purpose and Analytical Focus
Annual Publication Trends	Track annual changes in the number of publications to reveal the temporal dynamics of research activities and developmental stages.
Journal Distribution	Identify core journals and major publishing platforms within the field and analyze disciplinary orientation and academic influence.
Highly Cited Literature	Identify foundational or highly influential works that underpin research in the field.
Institutional and National Collaboration Networks	Map the contributions of leading institutions and countries to 3D visualization research in architectural heritage.
Keyword Co-occurrence and Clustering Analysis	Analyze the frequency of keyword co-occurrence to identify research hotspots and knowledge subfields, as well as their interrelationships, thereby constructing a thematic knowledge structure of the field.
Emergent Term Analysis	Detect terms with sharply increasing citation frequencies within specific periods to identify emerging trends and conceptual shifts in digital heritage visualization.

summarizes the scientometric analysis techniques adopted in this study and their corresponding analytical dimensions, including annual publication trends, the highly cited literature, collaboration networks among research institutions and countries, keyword co-occurrence and clustering analyses, and the detection of emerging terms.

2.1.2. Methodology for the Systematic Review

As a qualitative research methodology, the systematic review emphasizes the systematicity, transparency, and replicability of the research process. By conducting a comprehensive and critical analysis of the representative literature, this approach enables the distillation of key technological trajectories, practical application paradigms, and underlying value systems embedded within 3D visualization technologies in the field of architectural heritage conservation. Systematic reviews are particularly effective in organizing thematic structures within interdisciplinary research contexts, as they establish coherent linkages among diverse research outputs and illuminate the internal logic and evolutionary pathways of knowledge development [20]. Building on the scientometric analyses, this study employed a systematic review methodology to further refine the keyword clusters generated by CiteSpace, aiming to identify thematically significant and representative research domains. To ensure analytical rigor and methodological consistency in terms of relevance, representativeness, and thematic depth, a set of standardized screening criteria was developed, as shown in

Table 2. Screening strategy and criteria.

Item	Description
Inclusion Criteria	The cluster topic is closely related to 3D visualization in architectural heritage. The cluster contains multiple high-frequency keywords with clear semantic coherence. The associated literature includes representative review papers or highly cited publications. The cluster can be categorized under a distinct technical pathway or research direction.
Exclusion Criteria	The cluster topic is overly fragmented or limited to a specific geographic location. The keywords are semantically dispersed or focused on marginal technical issues. The internal cohesion of the cluster is low, with insufficient support from the systematic literature. The relevance to architectural heritage is weak, or the technical application lacks generalizability.

, to guide the entire selection process.

To visually convey the analytical process, Figure 1 illustrates the thematic extraction workflow, outlining the sequential steps from cluster identification and selection to thematic abstraction and synthesis.

2.2. Data Sources and Data Collection Methodology

The data for this study were retrieved from the Web of Science Core Collection (WoS Core Collection), a leading database that indexes high-quality, peer-reviewed English-language journals worldwide and serves as a principal platform for international scientometric research and scholarly assessment. The specific datasets consulted include the Social Science Citation Index (SSCI), the Science Citation Index Expanded (SCIE), the Arts and Humanities Citation Index (AHCI), and the Emerging Sources Citation Index (ESCI) [21].

To ensure the breadth and depth of the literature retrieval, the keyword strategy was developed based on a preliminary literature review and terminological synthesis, focusing on two core dimensions: “3D visualization technology” and “architectural heritage conservation”. The search terms employed comprised “3D visualization”, “3D modeling”, “building information modeling”, “digital reconstruction”, “digital twin”, “BIM”, “photogrammetry”, “laser scanning”, “LiDAR”, “virtual reality”, “augmented reality”, “reality-based modeling”, and “3D scanning”, combined with domain-specific terms such as “architectural heritage”, “built heritage”, and “architectural conservation”.

To ensure the systematicity and representativeness of the dataset, the search was restricted to publications dated between 1 January 2005 and 31 December 2024, thereby encompassing two pivotal decades of rapid technological advancement in the field of architectural heritage conservation. The initial retrieval yielded 813 records. To enhance data quality, the dataset was further refined by limiting document types to journal articles and review articles (articles and review articles), excluding non-empirical works such as conference abstracts, editorials, and book reviews, and restricting the selection to English-language publications. Following this screening process, a final dataset comprising 484 valid records was retained for analysis.

2.3. Research Framework

This study aims to systematically elucidate the knowledge structure and evolutionary trajectories of 3D visualization technologies within the domain of architectural heritage conservation while establishing a coherent and methodologically rigorous research framework. The overarching approach integrates quantitative scientometric analysis with a qualitative systematic review, constituting a multi-tiered research process that encompasses data processing, knowledge map construction and interpretation, core concept refinement, thematic framework development, and the identification of future research directions. The detailed research workflow is illustrated in Figure 2.

In addition to emphasizing the quantitative precision of data analysis, the research framework prioritizes the semantic interpretation and theoretical modeling of 3D visualization technologies and their applications in design practice. The ultimate objective is to construct a comprehensive knowledge map of the field, systematically identify research gaps and emerging thematic directions, and provide a robust foundation for theoretical advancement and innovative practical applications in architectural heritage conservation.

3. Analysis Results and Discussion

3.1. Scientometric Analysis

This section employs scientometric analysis to quantitatively investigate the research corpus on 3D visualization technologies within the domain of architectural heritage conservation, utilizing CiteSpace software. Key indicators, such as annual publication trends, journal distribution patterns, highly cited works, institutional and international collaboration networks, keyword co-occurrence structures, and emergent term identification, are systematically synthesized to elucidate the underlying knowledge architecture and the evolutionary dynamics shaping the field.

3.1.1. Publication Trends over Time

Over the past two decades, scholarly interest in the application of 3D visualization technologies to architectural heritage conservation has exhibited a sustained and accelerating trajectory. As illustrated in Figure 3, the evolution of the field between 2005 and 2024 can be broadly categorized into three distinct phases.

During the initial exploratory phase (2005–2014), research activity remained nascent, with the annual number of publications fluctuating between 0 and 4. At this stage, technological immaturity, high equipment costs, and limited interdisciplinary collaboration restricted the application of 3D visualization technologies to preliminary validations and small-scale case studies, without the formation of a coherent research framework [22,23,24].

The subsequent phase of gradual growth (2015–2019) witnessed a steady increase in research output, with publications rising from 5 in 2015 to 21 in 2019. This expansion was driven by the maturation of enabling technologies such as laser scanning, building information modeling (BIM), virtual reality (VR), and augmented reality (AR), alongside increasing international advocacy for digital heritage preservation by organizations such as the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the International Council on Monuments and Sites (ICOMOS) [25,26]. During this period, the application of 3D visualization technologies expanded significantly in heritage documentation, virtual exhibitions, and public engagement initiatives.

Since the onset of the rapid development phase (2020–2024), research output has grown exponentially, with the number of publications soaring from 41 in 2020 to 115 in 2024. The global outbreak of the COVID-19 pandemic markedly heightened the demand for virtual access and remote interaction, accelerating the adoption of advanced technologies such as digital twins, intelligent modeling, and artificial intelligence (AI)-assisted reconstruction within architectural heritage conservation [27]. Concurrently, the emergence of technologies such as the Internet of Things (IoT) and semantic modeling has propelled 3D visualization from static documentation toward the development of intelligent, interactive, and predictive heritage management platforms [28].

Moreover, the dotted trendline in Figure 3 indicates a linear regression fit, revealing a persistent upward trend despite occasional short-term fluctuations. This trend underscores the increasing academic visibility and perceived value of 3D visualization technologies in heritage scholarship. However, a purely quantitative interpretation of this growth warrants caution. Scientometric indicators, while useful in identifying publication volume and temporal distribution, do not necessarily reflect the theoretical depth, methodological rigor, or practical impact of the research produced. Moreover, disparities in database coverage, language bias, and keyword selection may introduce distortions, potentially overrepresenting mainstream discourses while marginalizing alternative or non-English contributions.

3.1.2. Distribution of Journals

Table 3. Journal distribution (top 15 journals, 2005–2024).

No.	Journal	Year	Frequency	Centrality
1	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences	2014	300	0.02
2	Journal of Cultural Heritage	2009	274	0.01
3	Automation in Construction	2006	247	0.37
4	Remote Sensing (Basel)	2015	209	0.05
5	Sustainability (Basel)	2018	169	0.01
6	International Journal of Architectural Heritage	2017	165	0
7	Applied Sciences (Basel)	2019	159	0
8	Sensors (Basel)	2015	141	0.1
9	ISPRS International Journal of Geo-Information	2019	138	0
10	ISPRS Journal of Photogrammetry and Remote Sensing	2006	131	0.2
11	Heritage (Basel)	2020	116	0.01
12	Buildings (Basel)	2019	112	0.01
13	Structural Survey	2018	109	0
14	Applied Geomatics	2015	100	0.02
15	Heritage Science	2019	96	0.01

presents the publication frequencies and first appearance years of the 15 most influential journals that contributed to research on 3D visualization technologies in architectural heritage conservation between 2005 and 2024. The inclusion of the publication year offers additional insight into the temporal dimension of the field’s development, helping to contextualize the emergence and sustained influence of key journals [29,30]. The findings reveal that these journals span a wide range of disciplines, including cartography and remote sensing, architecture and engineering, cultural heritage studies, and sustainable development, thereby emphasizing the highly interdisciplinary nature of the field. Notably, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences and the ISPRS Journal of Photogrammetry and Remote Sensing emerge as particularly prominent publication venues. Their high frequency underscores the foundational role of surveying, mapping, and remote sensing technologies in advancing 3D visualization applications, particularly in spatial data acquisition, 3D modeling, and the digital documentation of architectural heritage.

Furthermore, the substantial publication outputs in heritage-focused journals, such as the Journal of Cultural Heritage and the International Journal of Architectural Heritage, demonstrate the academic community’s strong commitment to applying 3D visualization technologies for authenticity preservation, historical value reconstruction, and theoretical innovation in cultural heritage studies. Such contributions have not only enriched the theoretical discourse but have also fostered interdisciplinary integration between digital technologies and heritage research.

In addition, the presence of architecture and engineering journals, including Automation in Construction and Buildings (Basel), reflects the expanding application of 3D visualization technologies in domains such as building information modeling (BIM), intelligent maintenance, and heritage risk management. Particularly, Automation in Construction exhibits a centrality value of 0.37, which is significantly higher than that of other journals, indicating its pivotal role in bridging research communities in architectural engineering, smart technologies, and heritage conservation.

Despite these strengths, the journal distribution also reveals critical structural imbalances. The dominance of a few high-frequency journals contrasts with the relatively low output from others that, despite high centrality scores, contribute to fewer publications. This indicates a potential overreliance on a narrow subset of publication platforms, which may restrict epistemological diversity and limit the breadth of scholarly engagement. Moreover, many heritage-specific journals exhibit low or negligible centrality, suggesting a marginal position in the broader knowledge network and limited influence in fostering cross-disciplinary integration.

The geographic and linguistic concentration of core journals, largely published by European and North American institutions with English as the principal language, also merits reflection. This concentration raises concerns regarding linguistic inclusivity, regional representation, and the potential marginalization of heritage perspectives, knowledge systems, and epistemologies originating from the Global South.

In conclusion, the current journal landscape reflects both the maturity and structural asymmetries of the research ecosystem. While it illustrates growing disciplinary convergence and methodological sophistication, it also underscores the need to expand the range of publication platforms, promote the visibility of underrepresented voices, and advance a more inclusive and equitable knowledge architecture for 3D visualization in architectural heritage conservation.

3.1.3. Highly Cited Papers

The identification of highly cited publications provides critical insights into the foundational knowledge structure and evolving research frontiers of 3D visualization technologies in architectural heritage conservation.

Table 4. Top 15 most cited papers (TGCS *).

No.	Paper Title	Year of Publication	TGCS
1	Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark	2020	290
2	BIM for heritage science: a review	2018	153
3	Review of built heritage modelling: Integration of HBIM and other information techniques	2020	149
4	Terrestrial laser scanning and limit analysis of masonry arch bridges	2011	130
5	Combined Use of Terrestrial Laser Scanning and IR Thermography Applied to a Historical Building	2015	91
6	Integrating radar and laser-based remote sensing techniques for monitoring structural deformation of archaeological monuments	2013	89
7	From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage	2020	85
8	Digital Twin: Research Framework to Support Preventive Conservation Policies	2020	83
9	Protocol to Manage Heritage-Building Interventions Using Heritage Building Information Modelling (HBIM)	2018	82
10	BIM semantic-enrichment for built heritage representation	2019	81
11	An Efficient Pipeline to Obtain 3D Model for HBIM and Structural Analysis Purposes from 3D Point Clouds	2020	80
12	A Parametric Scan-to-FEM Framework for the Digital Twin Generation of Historic Masonry Structures	2021	79
13	HBIM and Virtual Tools: A New Chance to Preserve Architectural Heritage	2018	78
14	From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning	2021	77
15	3D GIS for cultural heritage restoration: A ‘white box’ workflow	2016	73

* Note: TGCS = total global citation score.

lists the 15 most highly cited papers in this domain (ranked by total global citations), retrieved from the Web of Science Core Collection between 2005 and 2024.

A temporal analysis of publication trends reveals a notable concentration of high-impact studies between 2018 and 2021, during which 11 out of the 15 most cited articles, accounting for over 70 percent, were published. Remarkably, the year 2020 alone contributed four of these top-ranking works. This clustering not only suggests an intensified surge of academic interest but also underscores the relatively recent consolidation of the field’s intellectual landscape. While this proliferation of influential publications within a short time span may signal a period of rapid innovation and methodological advancement, it simultaneously prompts critical reflection on the long-term stability and empirical validation of prevailing research frameworks. The recency of these contributions implies that many have yet to undergo rigorous longitudinal assessment, raising important questions about their theoretical robustness, practical scalability, and enduring relevance across diverse cultural heritage contexts.

An analysis of the titles and citation metrics reveals that terrestrial laser scanning and heritage building information modeling (HBIM) have emerged as core research themes. The HBIM framework, in particular, has been extensively employed to integrate heterogeneous data sources, facilitating the documentation, management, and intervention planning of heritage assets. As a result, while HBIM has proven effective in consolidating heterogeneous datasets for heritage documentation, analytical evaluation, and intervention planning, its current applications are often hindered by persistent challenges related to system interoperability, data scalability, and the meaningful integration of intangible cultural heritage values [31].

In addition, emerging research directions, such as digital twin technologies and semantic enrichment approaches, are attracting increasing scholarly attention [32,33]. These developments signal a paradigmatic shift toward intelligent, data-driven models of heritage conservation, emphasizing the potential of advanced technologies to enable predictive maintenance and proactive intervention strategies. Although these developments exhibit considerable conceptual sophistication and have gained increasing citation recognition, their practical implementation and empirical validation remain significantly underexplored. There is a notable lack of critical scholarship assessing their real-world impact on conservation practices, particularly beyond the confines of proof-of-concept prototypes and controlled pilot studies.

Overall, the evolution of research on 3D visualization technologies in architectural heritage conservation demonstrates clear progression from early, technology-specific experiments to the establishment of a comprehensive interdisciplinary framework that encompasses 3D data acquisition, semantic modeling, digital simulation, and intelligent heritage management, thus reflecting an accelerating trajectory toward integrated intelligent system development.

3.1.4. Collaboration Network Analysis

The collaboration network analysis provides critical insights into the key institutional distributions and geographic synergy patterns that characterize research on 3D visualization technologies in architectural heritage conservation.

Institutional Collaboration Networks

Figure 4 depicts the institutional collaboration network spanning the period from 2005 to 2024. Structurally, the network displays medium-density clustering, which is characterized by several closely interconnected research groups, alongside a number of relatively isolated or weakly connected nodes. Each node represents a research institution, with the node size indicating the volume of associated publications and the connecting lines representing collaborative relationships. Node colors correspond to the average publication year, with warmer tones denoting more recent research activity.

The analysis reveals that the Universitat Politècnica de València holds a central and influential position within the network, distinguished by its substantial node size and extensive collaborative ties, underscoring its pivotal role in advancing research on 3D visualization and architectural heritage conservation. Other prominent institutions include the Polytechnic University of Turin, the University of Sevilla, the Consiglio Nazionale delle Ricerche (CNR), and the Polytechnic University of Milan, all of which demonstrate strong academic output and robust inter-institutional collaborations. Furthermore, Chinese institutions, notably Southeast University and the Hubei University of Technology, have emerged as significant contributors, reflecting the increasing research activity and rising international visibility of Chinese scholars in this domain. Additionally, institutions such as the Universidad Politécnica de Madrid, Sapienza University of Rome, and the Centre National de la Recherche Scientifique (CNRS) function as critical “bridges”, facilitating interdisciplinary and international collaborations and promoting the dynamic exchange and integration of knowledge across diverse research clusters.

Nevertheless, the network reveals pronounced structural imbalances. European institutions demonstrate densely interconnected clusters, whereas collaborations involving institutions from the Global South remain limited and peripheral. This geographic disparity, along with the insufficient integration of emerging research centers outside of Europe, raises critical concerns about the inclusivity and representational equity of the global academic ecosystem. Moreover, several institutions that specialize in heritage research occupy marginal positions within the network, despite their thematic relevance, indicating a limited capacity to influence inter-institutional knowledge exchange. These findings underscore the urgent need for intentional strategies aimed at fostering globally inclusive research partnerships and integrating underrepresented epistemologies and regional heritage perspectives into the expanding discourse on three-dimensional visualization in conservation practice.

Country Collaboration Networks

Figure 5 depicts Spain, Italy, China, and the United Kingdom as the principal contributors to research on 3D visualization technologies within the field of architectural heritage conservation. Among these, Spain and Italy demonstrate the largest node sizes, signifying their leadership in both research productivity and international collaboration. Notably, the relatively large node size and warmer coloration associated with China highlight the significant expansion of Chinese research outputs over the past decade, underscoring its emergence as a major force within the field.

The overall network structure reveals a moderately interconnected system of global collaboration, with European countries occupying a central position. Spain and Italy, in particular, act as pivotal hubs, actively fostering a broad range of international partnerships. Furthermore, intra-European collaboration appears particularly robust, reflecting a high degree of regional cohesion supported by shared policy frameworks and collective initiatives under the European Union’s heritage conservation programs [34]. This collaborative model not only facilitates resource sharing and technological exchange but also accelerates the integration of transnational research efforts, thereby advancing the global agenda for heritage conservation.

Relatively small node sizes and limited link densities indicate a persistent underrepresentation of non-Western countries in the global collaborative network of architectural heritage research. This structural imbalance reflects the marginalization of diverse cultural perspectives and the continued dominance of Eurocentric academic frameworks in shaping knowledge production. In addition, several countries with rich architectural heritage traditions occupy only peripheral positions in international research networks, raising significant concerns about inclusiveness, representational equity, and epistemic fairness within the field.

3.1.5. Identification of Core Themes and Frontier Trends

This study systematically delineates the evolution of research themes and frontier trends in 3D visualization technologies within the domain of architectural heritage conservation from 2005 to 2024. The analysis is grounded in the examination of the keyword co-occurrence network, the visualization of time zones, and the detection of strong emergent terms.

Keyword Co-Occurrence Analysis

Several core research themes are identified through the keyword co-occurrence network depicted in Figure 6. High-frequency keywords, such as cultural heritage, architectural heritage, documentation, laser scanning, and photogrammetry, constitute the foundational knowledge base of the field, with a primary focus on the digital documentation, preservation, and dissemination of cultural assets. Simultaneously, technology-oriented keywords such as BIM, HBIM, digital twin, and point cloud appear prominently, emphasizing the pivotal role of building information modeling (BIM), 3D data acquisition, and intelligent model development in contemporary heritage conservation practices.

Moreover, the recent emergence of keywords such as virtual reality, semantic segmentation, and machine learning signifies a discernible shift in research trajectories toward immersive experience design and intelligent data interpretation. This trend highlights a broader developmental trajectory in which 3D visualization technologies are progressively transitioning from static representational tools to dynamic platforms that enable interactive engagement and cognitive augmentation in heritage research.

Despite substantial advancements in technological applications within the field, the keyword co-occurrence network reveals several enduring limitations. The strong concentration of research around technical terminology and digital workflows reflects an ongoing tendency toward technocentrism, which may obscure the socio-cultural, political, and ethical dimensions that are essential to the interpretation of cultural heritage. Of particular concern is the marked absence of keywords related to community engagement, indigenous knowledge systems, and critical heritage theory. This omission suggests that the field remains largely shaped by positivist methodologies and problem-oriented approaches, limiting the potential for more inclusive, context-sensitive, and theoretically grounded research frameworks.

Time Zone Visualization Analysis

The time zone visualization presented in Figure 7 delineates the emergence and evolutionary trajectories of key research themes over time, providing a longitudinal depiction of the development of 3D visualization technologies in architectural heritage conservation between 2005 and 2024. Each horizontal line represents a major research cluster, with node positions indicating periods of keyword activity, node sizes reflecting the frequency of occurrence, and node colors corresponding to the average year of publication.

The cluster analysis identifies several prominent research themes. Cluster #0, “Digital Twin”, emerges as the largest and most recent research cluster, underscoring the pivotal role of dynamic digital modeling in the real-time monitoring, predictive maintenance, and intelligent management of heritage sites [35]. Although differing in terminology, Cluster #0, “Digital Twin”, and Cluster #2, “Digital Twins”, both converge on the application of digital twin technologies in architectural heritage conservation. Cluster #1, “Laser Scanning”, represents an early and sustained research focus, highlighting the fundamental importance of terrestrial laser scanning (TLS) in the acquisition and documentation of high-fidelity 3D data [36]. Cluster #3, “Cultural Heritage”, concentrates on the preservation and public dissemination of cultural assets. Cluster #4, “Artificial Intelligence”, reflects the accelerated integration of intelligent technologies, such as machine learning, deep learning, and semantic segmentation, into heritage conservation practices [37]. Other notable clusters include Cluster #6, “Structural Health”, which focuses on risk assessment and structural stability evaluations of heritage sites, and Cluster #9, “3D Reconstruction”, which addresses the generation of accurate and detailed 3D models from heterogeneous and complex data sources. Collectively, these clusters illustrate the expanding technological landscape of the field and its increasing interdisciplinary integration across engineering, computer science, and heritage studies.

The temporal evolution of research within the field can be broadly divided into three phases. In the initial phase, research predominantly focused on data acquisition and modeling techniques, centering on the digital documentation and visual reconstruction of built heritage. During the intermediate phase, scholarly attention shifted toward cultural heritage digitization, virtual reality applications, and the development of HBIM frameworks, emphasizing the integration of multi-source data and the enhancement of public-facing visualization and dissemination strategies. In the most recent phase, research has increasingly gravitated toward emergent themes, such as digital twins, artificial intelligence, and semantic modeling, signaling a transition toward more intelligent, automated, and predictive approaches to heritage conservation. This phase prioritizes dynamic monitoring, intelligent analysis, and proactive maintenance applications.

Nonetheless, this research trajectory reveals several underlying epistemological limitations. Although the strong reliance on engineering and computational methodologies has significantly contributed to technological advancement, it also promotes a technocentric orientation that may obscure the socio-cultural, ethical, and political dimensions that are essential to cultural heritage. This narrowing of focus risks constraining the development of more pluralistic, critically reflective, and humanistically grounded approaches within the field of heritage conservation.

Overall, the evolution of research keywords reflects a paradigmatic shift in the application of 3D visualization technologies in architectural heritage conservation, transitioning from early stages characterized by static recording to an advanced phase emphasizing intelligent management and dynamic decision support.

Analysis of Emergent Terms

As depicted in Figure 8, the analysis of emergent terms delineates the evolutionary trajectory of research hotspots in the application of 3D visualization technologies to architectural heritage conservation between 2005 and 2024. In the formative phase of the field, the keyword digital photogrammetry (burst intensity = 2.43) showed significant activity between 2011 and 2015, indicating an early research emphasis on photogrammetric techniques for heritage documentation. At that time, terrestrial laser scanning (TLS) had not yet achieved widespread adoption, and photogrammetry was regarded as a cost-effective and flexible alternative for spatial data acquisition [38]. Between 2017 and 2020, research directions diversified significantly. The keyword Unmanned Aerial Vehicles (UAV) (burst intensity = 2.62) emerged prominently, reflecting the increasing utilization of UAV platforms for the 3D modeling of large-scale or inaccessible heritage sites. Simultaneously, documentation (burst intensity = 3.96) and reconstruction (burst intensity = 3.17) became highly active, underscoring the growing emphasis on both high-precision recording and 3D reconstruction to support heritage conservation and dissemination. In addition, a series of keywords associated with laser scanning technology became prominent during this period. These include laser (active from 2018 to 2021), point cloud (active from 2018 to 2020), and information (active from 2019 to 2020), indicating the rapid proliferation of laser scanning applications and an increasing focus on structuring and managing point cloud data. Since 2020, research trajectories have increasingly converged toward integration and intelligence. Keywords such as generation (active from 2020 to 2021), point cloud data (active from 2020 to 2022), and heritage buildings (active from 2021 to 2022) reflect the growing scholarly attention to constructing high-fidelity 3D models of historic structures. Notably, HBIM (burst intensity = 1.90) has emerged as a central research focus, establishing itself as a specialized information modeling framework tailored to heritage conservation. Furthermore, the consistent emergence of keywords such as preservation (active from 2021 to 2024), quality (active from 2022 to 2024), and system (active from 2022 to 2024) indicates a strategic shift toward systematization, standardization, and quality assurance in digital heritage practices. In particular, the sustained prominence of quality and system highlights a paradigmatic transition in research priorities, moving from an initial focus on basic digitization toward a more mature emphasis on precision, interoperability, and the long-term sustainability of digital heritage models.

A comprehensive analysis of emergent keywords further clarifies the forefront trajectories shaping contemporary research. Terms such as HBIM, digital twin, heritage buildings, quality, and system have prominently emerged between 2020 and 2024, reflecting a rapid evolution in architectural heritage conservation toward more intelligent, efficient, and systematically structured approaches. Digital twin technologies and intelligent modeling have established themselves as critical technological pathways, catalyzing innovation by enabling dynamic monitoring, predictive maintenance, and enhanced decision-making in heritage site management. At the same time, there is an increasing emphasis on high-quality data management and the development of systematic operational frameworks, underscoring the field’s growing focus on standardization, interoperability, and long-term sustainability. Furthermore, the integration of AI-driven automated processing, immersive technologies such as virtual reality (VR) and augmented reality (AR), and predictive maintenance mechanisms into conservation practices signals a transition toward a more integrated and cognitively augmented paradigm. Collectively, these advancements indicate that the field is entering a new phase characterized by intelligent systems, enhanced interconnectivity, and forward-looking heritage management strategies.

3.1.6. Overview of Scientometric Mapping Results

The results of the scientometric mapping analysis reveal that research on 3D visualization technologies in architectural heritage preservation has demonstrated sustained growth, with particularly notable expansion over the past decade. Journal distributions are primarily concentrated in the disciplines of surveying and mapping, architecture, and cultural heritage preservation and sustainable development, reflecting the inherently interdisciplinary character of the field. Highly cited literature predominantly focuses on advancements in data acquisition methodologies, information modeling frameworks, and intelligent application pathways, illustrating the dual-driven momentum of technological evolution and application diversification.

Institutional collaboration network analysis indicates that leading European research institutions dominate the global research landscape. These institutions serve as critical hubs for technological innovation, standardization, and international academic cooperation, significantly promoting knowledge dissemination and methodological advancement. Simultaneously, research productivity and international collaboration among Asian universities and research institutes, particularly in China, are steadily increasing, reflecting the rising academic influence of Asia in the domain of digital heritage preservation. At the national level, the collaboration network demonstrates the formation of stable and cohesive partnerships among European countries, while Asian countries are exhibiting a rapidly accelerating trajectory in global knowledge diffusion and technological application, thereby facilitating broader international exchange and collaborative innovation.

The analysis of keyword co-occurrence and emergence further highlights that foundational technologies such as terrestrial laser scanning, photogrammetry, building information modeling (BIM), and heritage building information modeling (HBIM) have established the core technological infrastructure underpinning this field. Meanwhile, emerging technologies such as digital twins, virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) are advancing the research frontier, shifting the thematic focus from static architectural documentation toward dynamic management, intelligent monitoring, and immersive interactive engagement. This evolutionary trajectory signifies a progressive deepening of the field’s knowledge architecture, with theoretical frameworks and practical applications increasingly moving toward greater integration, intelligence, and interactivity.

Although significant progress has been made in technological innovation, interdisciplinary integration, and international collaboration, and while scientometric mapping has effectively revealed the developmental trajectory and knowledge structure of the field, several critical issues warrant closer examination. One major concern is the persistent emphasis on technical aspects such as data acquisition and modeling, which reflects a predominantly technocentric research orientation. In contrast, essential non-technical dimensions, including socio-cultural context, the interpretation of heritage values, and public engagement, remain insufficiently addressed. This imbalance suggests a lack of humanistic depth in the prevailing research paradigm. In addition, despite the growing geographical diversity of collaborative networks, regional disparities remain evident. Research visibility and academic influence from developing regions continue to be limited, contributing to ongoing inequalities in global knowledge production. Furthermore, although emerging technologies such as digital twins and artificial intelligence have expanded methodological boundaries, their integration with theoretical inquiry and critical practice remains underdeveloped.

3.2. Systematic Review

While scientometric analysis offers considerable value in elucidating the intellectual structure and evolutionary trajectory of the field, the breadth and complexity of its outputs call for a more refined, content-driven investigation. Accordingly, this study builds upon the results of scientometric mapping and keyword clustering by incorporating a systematic review methodology to critically examine the literature associated with high-frequency clusters. Through a comparative evaluation of the 15 keyword clusters generated by CiteSpace, a multidimensional analysis is conducted across three core dimensions: semantic proximity, thematic salience, and citation impact. This analytical approach facilitates the identification of four highly representative cluster modules, from which four overarching research themes are derived, as presented in

Table 5. Representative clusters and their corresponding core research themes.

No.	Cluster-ID & Keywords	Core Theme
1	#1 laser scanning, #8 level of detail (LOD), #9 3D reconstruction, #12 image analysis	3D Data Acquisition and Modeling Techniques
2	#0 digital twin, #2 digital twins, #4 artificial intelligence, #6 structural health	Digital Heritage Documentation and Information Management
3	#7 heritage building, #10 historic buildings, #11 virtual reconstruction	Virtual Reconstruction and Interactive Visualization
4	#3 cultural heritage, #14 adobe constructions	Digital Transformation and Cultural Narrative Integration

.

3.2.1. Three-Dimensional Data Acquisition and Modeling Technologies

Three-dimensional (3D) data acquisition and modeling represent the technical foundation of architectural heritage visualization, as illustrated in Figure 9. This process significantly enhances the accuracy and efficiency of spatial documentation while providing essential data support for subsequent semantic modeling and digital heritage management.

In terms of data acquisition, techniques such as photogrammetry, unmanned aerial vehicle (UAV) imaging, and close-range photography offer lightweight and flexible solutions suitable for documenting small- to medium-scale heritage structures and complex terrains [39,40,41]. In comparison, active sensing technologies such as Light Detection and Ranging (LiDAR) and terrestrial laser scanning (TLS) generate high-density, high-precision point clouds, making them well-suited for high-fidelity modeling of expansive sites and detailed façade geometries [42,43]. However, traditional image-based approaches often require manual operations and offer limited accuracy, while laser scanning, despite its precision, involves high costs and complex data processing [44,45,46]. Recent technological advancements have enabled hybrid workflows such as Structure from Motion (SfM) and UAV-based photogrammetry, which support scalable and cost-effective data collection strategies [3,47,48]. For instance, Yakar and Dogan (2019) integrated SfM with UAV imaging to reconstruct multiple historical buildings, significantly improving spatial coverage and acquisition efficiency [49].

LiDAR-generated point clouds typically require preprocessing steps such as noise filtering, coordinate registration, and density optimization. These data are then combined with multisensor sources, including RGB imagery, infrared, and thermal imaging, forming a comprehensive dataset for detailed geometric modeling and semantic interpretation [50,51].

The modeling process has evolved from early-stage point cloud triangulation to the adoption of building information modeling (BIM) platforms [52,53]. While BIM facilitates the parametric modeling of regular architectural components, it has a limited capacity to represent the irregular forms and material diversity of historical structures [54]. Heritage building information modeling (HBIM) addresses this limitation by incorporating archival data, component evolution histories, and conservation records, thereby combining geometric modeling with semantic representation. This enhances the interpretability of models and supports applications such as digital archiving, heritage communication, and conservation management [55].

To further improve automation and intelligence in modeling workflows, artificial intelligence (AI) technologies are being integrated into 3D modeling systems. AI is increasingly used for component recognition, semantic classification, texture generation, and automatic annotation [56,57]. These applications enhance modeling efficiency and enable the adaptive recognition of complex architectural morphologies, offering promising support for semantic enrichment and intelligent HBIM updates [58]. Future research should prioritize the development of AI-driven algorithms for morphological detection and semantic annotation to enable integrated and intelligent modeling across the full 3D heritage documentation pipeline.

3.2.2. Digital Heritage Documentation and Information Management

Digital recording and information management have become central to contemporary research in architectural heritage conservation [59,60]. While traditional methods such as architectural drawings, photographic documentation, and manual archives were essential in the early phases of preservation, they increasingly fall short in addressing modern demands for structural monitoring, temporal evolution tracking, and broad public engagement [61].

To overcome these limitations, recent studies have proposed models for integrating multi-source heterogeneous data. These frameworks aim to unify laser-scanned point clouds, orthophotos, GIS datasets, and historical records within a single spatial database, thereby reducing information fragmentation and spatiotemporal disconnects [62,63]. This integrative approach not only enhances the structural coherence and completeness of heritage archives but also enables multi-scale analysis from city-wide urban heritage to individual architectural components through BIM and Geographic Information System (GIS) interoperability. However, practical implementation is hindered by discrepancies in data accuracy, formatting, and semantic consistency across sources, resulting in complex integration processes and inefficiencies.

The application of 3D modeling technologies has created new possibilities for lifecycle-based heritage management, including long-term monitoring, maintenance, and conservation [64]. Originally developed for design and performance analysis in contemporary architecture, building information modeling (BIM) has evolved into heritage building information modeling (HBIM) for use in historical contexts [65]. HBIM extends conventional modeling capabilities by integrating metadata on component evolution, repair history, and cultural semantics. It supports cross-temporal data tracking, layered semantic annotation, and the creation of dynamic, knowledge-rich digital archives [66,67,68,69]. However, current applications of HBIM often lack standardized semantic frameworks and shared ontologies, limiting its scalability and broader adoption in collaborative knowledge platforms.

There is an urgent need to re-examine the structural challenges of HBIM from a system-wide perspective [70,71]. Inconsistent data formats, divergent semantic labeling conventions, and fragmented version control mechanisms continue to impede cross-platform interoperability. Additionally, many heritage information systems lack robust strategies for data sustainability, making them vulnerable to issues such as metadata degradation and failed format migration. Addressing these challenges requires the development of unified semantic ontologies and standardized protocols for cross-platform data exchange, which are essential for ensuring long-term digital preservation and reuse.

In this context, emerging technologies are reshaping the landscape of digital heritage management. Digital twins, the Internet of Things (IoT), and artificial intelligence (AI) are evolving from experimental applications to integrated systems that offer real-time monitoring, behavioral simulation, and intelligent decision-making support [72,73,74]. For example, the integration of HBIM and digital twin technology in the modeling of the Panayoa-Camiotis Church has provided critical data to support monitoring, conservation, and restoration efforts [75].

3.2.3. Virtual Reconstruction and Interactive Visualization

Virtual reconstruction has emerged as one of the most prominent applications of three-dimensional (3D) visualization in architectural heritage conservation. Its function has evolved from the simple replication of physical structures to the development of integrated platforms that combine cultural interpretation, narrative engagement, and public interaction [2]. The primary goal of such approaches is no longer limited to digitally reproducing heritage environments, but rather to revitalize historical memory and cultural context through digital technologies. This approach breathes new life into heritage sites that are inaccessible, damaged, or insufficiently documented [76]. Currently, the prevailing reconstruction methodology relies on 3D modeling based on informed assumptions drawn from diverse sources, including architectural drawings, archaeological reports, photographic archives, and expert knowledge. These models are typically deployed within virtual reality (VR) platforms to provide immersive experiences. However, when documentation is limited or interpretations vary, the process may fall into the trap of speculative reconstruction. In such cases, the model may be visually convincing but disconnected from historical reality, thereby raising concerns about the balance between authenticity and narrative creativity [77].

With the integration of augmented reality (AR) and mixed reality (MR) technologies, virtual reconstruction has advanced from passive viewing to active spatial engagement. By overlaying information on a site’s original appearance, restoration history, and significant events within the real-world environment, users can engage in meaningful spatial comparison and cultural exploration [78,79,80]. These mixed reality solutions have been widely implemented in historic precincts, museums, and interpretive systems, particularly where physical access is constrained or where conservation policies necessitate non-intrusive alternatives.

Despite these innovations, several challenges remain [81,82]. For example, AR systems are sensitive to environmental conditions such as lighting and network interference, while MR systems demand advanced hardware and high modeling precision, which may restrict their use in small- to medium-sized projects. Additionally, scholarly debate persists on whether reconstruction strategies should emphasize historical fidelity or a user-centered experience, with no universally accepted evaluation framework currently available.

Recent research indicates a shift in focus from visual display to immersive experience and narrative interaction [83,84]. Technologies such as guided storytelling, haptic interfaces, and spatial audio are being employed to enhance users’ sense of presence and historical empathy. Some experimental projects utilize first-person narration, allowing users to engage with historical scenes from the perspective of specific individuals. This participatory model deepens emotional involvement and interpretive depth, although it may also risk oversimplifying narratives or distorting cultural context, especially in cross-cultural communication.

Moreover, interactive visualization systems not only enhance the visibility of heritage assets but also increase their interpretability and emotional resonance [85,86]. Some projects simulate aging, graffiti, and repair marks within virtual models to convey temporal depth and stimulate reflection on the lived history of the site. Nevertheless, many virtual reconstructions remain focused on visual fidelity and technical achievement, with the deeper integration of cultural narrative and semantic context still in an early and exploratory stage.

3.2.4. Digital Transformation and Cultural Narrative

As three-dimensional (3D) visualization technologies evolve from static representational tools into dynamic cultural mediators, the digital preservation of architectural heritage is entering a new phase focused on narrative engagement and contextual immersion [87,88]. This transition reflects a broader shift in heritage conservation from replicating physical structures to constructing cultural meanings. It encompasses innovations in dissemination strategies, as well as transformations in cognitive frameworks, value systems, and epistemological practices [89]. Within this context, digital narratives have emerged as a sophisticated application of 3D visualization, emphasizing the fusion of narrative structure, emotional logic, and interactive design [90]. Rather than supplementing information, it constitutes an experience-oriented process of meaning-making. By constructing timelines, embedding character perspectives, and recreating historical trajectories, digital narratives reposition the audience from passive viewers to active participants, thereby enhancing immersion and cultural identity. For instance, virtual reality (VR) projects that reconstruct historical scenes through multimodal interfaces—such as audio narration, layered imagery, and tactile feedback—allow users to experience historical conflicts, everyday life, and ritual practices within immersive settings, stimulating emotional resonance and memory formation.

However, the expansion of technical capabilities has also raised concerns about balancing cross-cultural communication with the preservation of authenticity [91]. In multilingual and multicultural contexts, maintaining the integrity of local values while ensuring accessibility for broader audiences remains a persistent challenge. Especially in heritage contexts marked by strong ethnic or regional specificity, narrative simplification may lead to cultural misinterpretation, contextual distortion, or semantic alienation. Furthermore, digital narratives often rely on historical imagination for reconstruction, making it critical to delineate the boundary between informed inference and fictional construction. While the incorporation of fictional characters or events can enhance user engagement, it also risks blurring historical accuracy and diminishing the educational function [92,93].

On the methodological level, digital transformation is not only reshaping the pathways of heritage communication but also redefining the research paradigm. The focus has shifted from documentation based on physical information to interactive models centered on cultural semantics and participatory mechanisms [94]. As such, 3D visualization now serves as a medium for interpreting and conveying the cultural significance of heritage rather than merely reproducing its physical form [76]. Emerging approaches advocate participatory narrative frameworks that engage community members, Indigenous populations, and historians in the co-construction of heritage narratives. Despite their technical complexity, these multivocal and inclusive models reflect greater cultural respect and representational diversity [95].

In conclusion, digital narratives represent an advanced form of 3D visualization that signals a fundamental shift in architectural heritage practices. It moves beyond static digital archiving to support the creation of meaningful cultural experiences. This approach not only expands the technological dimensions of visualization but also transforms the interpretive and participatory logic of heritage communication, serving as a vital link between technology and cultural value, between history and memory, and between knowledge and the public.

4. Future Research Orientations

Drawing upon a comprehensive review of scientometric mappings and high-frequency keyword clusters related to 3D visualization technologies in architectural heritage conservation between 2005 and 2024, several pivotal research trajectories have emerged.

4.1. Advancing Intelligence and Automation in 3D Modeling Workflows

As the volume of three-dimensional data related to architectural heritage continues to expand, enhancing modeling efficiency while maintaining high geometric accuracy has become a pressing research concern. To address this issue, future workflows must incorporate artificial intelligence (AI), deep learning, and morphological recognition algorithms to overcome persistent challenges in component parameterization, semantic classification, and adaptive modeling [96,97]. Despite the growing maturity of photogrammetry, laser scanning, and heritage building information modeling (HBIM) technologies, the modeling process remains heavily reliant on manual input, limiting its scalability for large and complex heritage sites.

Recent studies have demonstrated that AI-based methods, including machine learning and semantic segmentation, not only automate object recognition and component classification but also offer intelligent decision support for damage assessment and structural diagnostics [61,98]. Furthermore, the integration of immersive technologies, such as virtual reality, augmented reality, and mixed reality, offers new possibilities for enhancing user engagement and facilitating public interaction with digital heritage [99,100,101]. However, most existing systems remain limited to static model visualization and lack deep contextual integration or responsive interactivity.

Consequently, future 3D modeling systems must prioritize both intelligent automation and the comprehensive integration of data processing, semantic construction, and immersive experience design. Such integration is essential to advance the collaborative and intelligent development of architectural heritage conservation across the entire digital workflow.

4.2. Enhancing Cross-Platform Interoperability and Semantic Standardization

Although 3D visualization technologies have significantly improved the precision and comprehensiveness of digital heritage documentation [102], existing heritage information systems are frequently characterized by fragmented data structures, inconsistent semantic models, and limited interoperability across platforms [103,104]. These limitations not only hinder the integration of heterogeneous systems, such as heritage building information modeling, Geographic Information Systems, Internet of Things applications, and immersive environments, but also impede sustainable data management, cross-disciplinary collaboration, and long-term archival preservation.

While some platforms have achieved preliminary standardization in terms of component coding, metadata structuring, and attribute annotation, the lack of a global and scalable semantic interoperability framework remains a major obstacle. Semantic disjunctions and isolated data silos still prevail, significantly restricting reuse, multi-platform analysis, and continuous project development.

To overcome these barriers, future research should focus on the development of a unified metadata architecture and multilingual semantic tagging protocols to ensure consistency across cultural and linguistic contexts. Additionally, standardized cross-platform APIs should be established to support semantic mapping and structured data exchange among BIM, GIS, and IoT systems. Equally important is the construction of a domain-specific semantic ontology capable of capturing material heterogeneity, historical transformations, and cultural narratives inherent in architectural heritage. Together, these advancements will promote richer semantic expressiveness, enhance logical reasoning capacity, and ensure the long-term interoperability and sustainability of digital heritage ecosystems.

4.3. Realizing the Full Lifecycle Management of Architectural Heritage

The digitalization of architectural heritage should extend beyond static recording and visualization to encompass a comprehensive and intelligent management system that covers the entire lifecycle of heritage assets. As conservation paradigms shift from passive preservation to proactive maintenance, the integration of emerging technologies such as digital twins and the Internet of Things has become essential to facilitating this transformation [105,106].

Currently, most 3D modeling research remains focused on spatial restoration and visual representation at discrete time points, which limits its capacity to support long-term use, environmental adaptability, and dynamic maintenance. This limitation is particularly evident in scenarios that involve disaster prediction, aging assessment, and emergency response, in which traditional platforms often lack real-time monitoring and intelligent decision-making functionalities [55].

Future research should therefore aim to systematically incorporate 3D visualization technologies across all phases of the heritage lifecycle, including initial documentation, condition monitoring, risk evaluation, restoration planning, ongoing maintenance, and adaptive reuse. Emphasis should be placed on enhancing dynamic data updating, artificial intelligence-assisted diagnostics, and traceable management frameworks. Moreover, the integration of sensor-based data with machine learning algorithms offers promising potential for detecting microstructural changes and forecasting systemic deterioration, ultimately improving the accuracy, responsiveness, and sustainability of conservation efforts.

4.4. Enhancing Cultural Narratives Through Digital Expression

As 3D visualization technologies gain broader adoption in architectural heritage conservation, their role is expanding beyond precise physical replication to serve as vehicles for cultural narration and memory revitalization [107]. While much of the current literature remains focused on geometric modeling and visual fidelity, it often overlooks the embedded local knowledge, social significance, and emotional connections that constitute the essence of heritage value [108,109]. Cultural heritage derives its meaning not merely from its physical attributes but from the historical experiences, collective memories, and symbolic identities it embodies [110].

Therefore, future research must move beyond technical frameworks and adopt interdisciplinary methodologies that integrate insights from cultural studies, anthropology, and narrative theory to develop more socially attuned and culturally expressive modes of digital representation. Approaches such as virtual character simulation, contextual storytelling, semantic enrichment, and multisensory interaction can transform spatial reconstructions into immersive cultural narratives. These methods foster deeper cognitive, emotional, and social engagement by enabling users to actively participate in heritage interpretation. This evolution signifies a broader transition from technology-centric preservation to culture-oriented communication and supports the development of a more participatory, experiential, and inclusive digital heritage environment.

5. Conclusions

This study presents a comprehensive review of the developmental trajectory and research hotspots of 3D visualization technologies in architectural heritage conservation from 2005 to 2024. By integrating scientometric analysis using CiteSpace with systematic review methods, this research identifies four major thematic domains: 3D data acquisition and modeling techniques, digital heritage documentation and information management, virtual reconstruction and interactive visualization, and digital transformation and cultural narrative integration. These themes collectively illustrate the evolution of 3D visualization from a purely technical tool to a medium for cultural expression and value communication.

The results highlight the foundational role of technologies such as terrestrial laser scanning (TLS), photogrammetry, building information modeling (BIM), and heritage building information modeling (HBIM) in supporting high-precision documentation and semantic enrichment of heritage assets. At the same time, the integration of emerging technologies such as digital twins, the Internet of Things (IoT), artificial intelligence (AI), and immersive media has significantly advanced the field toward dynamic perception, intelligent analysis, and interactive communication. This has transformed the conservation paradigm from static reproduction to adaptive engagement.

However, certain limitations of this study should be acknowledged. First, the dataset is primarily sourced from the Web of Science Core Collection and limited to English-language publications. As a result, significant region-specific studies, particularly those published in Chinese or indexed in other databases, may have been excluded. This limitation may affect the completeness and representativeness of the findings. Second, although the study provides a macro-level analysis of research trends and knowledge structures, it lacks in-depth comparative case studies and empirical assessments of practical applications. These aspects are essential for validating the real-world impact of visualization technologies in heritage conservation.

Based on these findings, this study proposes several future research directions. These include advancing intelligence and automation in 3D modeling workflows, enhancing cross-platform interoperability and semantic standardization, realizing the full lifecycle management of architectural heritage, and enhancing cultural narratives through digital expression. Advancing technological, systemic, and cultural dimensions in a coordinated manner will support the development of a more holistic and integrated framework for heritage conservation. This study not only contributes to a clearer understanding of the field’s evolution but also lays a conceptual foundation for future research that connects technological innovation with cultural significance. Ultimately, it aims to foster a new paradigm of digital heritage conservation that is intelligent, inclusive, and culturally informed.