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Review

Digital Technologies in Urban Regeneration: A Systematic Literature Review from the Perspectives of Stakeholders, Scales, and Stages

by
Xiaer Xiahou
1,
Xingyuan Ding
1,
Peng Chen
2,
Yuchong Qian
3,* and
Hongyu Jin
4
1
School of Civil Engineering, Southeast University, Nanjing 211189, China
2
Southeast University Architectural Design & Research Institute Co., Ltd., Nanjing 210096, China
3
School of Architecture, Southeast University, Nanjing 210096, China
4
Department of Civil and Environmental Engineering, University of Auckland, Auckland 1010, New Zealand
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(14), 2455; https://doi.org/10.3390/buildings15142455
Submission received: 9 June 2025 / Revised: 6 July 2025 / Accepted: 9 July 2025 / Published: 12 July 2025
(This article belongs to the Special Issue Challenges in Implementing Emerging Technologies in the Building Construction Industry)
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Abstract

Urban regeneration, as a key strategy for promoting sustainable development of urban areas, requires innovative digital technologies to address increasingly complex urban challenges in its implementation. With the fast advancement of digital technologies such as artificial intelligence (AI), Internet of Things (IoT), and big data, these technologies have extensively penetrated various dimensions of urban regeneration, from planning and design to implementation and post-operation management, providing new possibilities for improving urban regeneration efficiency and quality. However, the existing literature lacks a systematic evaluation of technology application patterns across different project scales and phases, comprehensive analysis of stakeholder–technology interactions, and quantitative assessment of technology distribution throughout the urban regeneration lifecycle. This research gap limits the in-depth understanding of how digital technologies can better support urban regeneration practices. This study aims to identify and quantify digital technology application patterns across urban regeneration stages, scales, and stakeholder configurations through systematic analysis of 56 high-quality articles from the Scopus and Web of Science databases. Using a mixed-methods approach combining a systematic literature review, bibliometric analysis, and meta-analysis, we categorized seven major digital technology types and analyzed their distribution patterns. Key findings reveal distinct temporal patterns: GIS and BIM/CIM technologies dominate in the pre-urban regeneration (Pre-UR) stage (10% and 12% application proportions, respectively). GIS applications increase significantly to 14% in post-urban regeneration (Post-UR) stage, while AI technology remains underutilized across all phases (2% in Pre-UR, decreasing to 1% in Post-UR). Meta-analysis reveals scale-dependent technology adoption patterns, with different technologies showing varying effectiveness at building-level, district-level, and city-level implementations. Research challenges include stakeholder digital divides, scale-dependent adoption barriers, and phase-specific implementation gaps. This study constructs a multi-dimensional analytical framework for digital technology support in urban regeneration, providing quantitative evidence for optimizing technology selection strategies. The framework offers practical guidance for policymakers and practitioners in developing context-appropriate digital technology deployment strategies for urban regeneration projects.

1. Introduction

Urban regeneration (UR) activities encompass the comprehensive improvement, functional transformation, and demolition and reconstruction of existing buildings or areas that cannot meet the multi-dimensional development needs of contemporary cities in economic, political, social, cultural, and environmental–ecological aspects [1,2]. Furthermore, studies have also defined UR as a comprehensive and integrated vision and action aimed at addressing multi-dimensional urban issues, enhancing land value and improving environmental quality, resolving urban decay issues, and achieving predetermined objectives [3]. As a key strategy for promoting sustainable urban development, the successful implementation of UR requires innovative technical methods to address increasingly complex urban challenges.
With the advancement and continuous development of digital technologies, digital technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and machine learning (ML) have been extensively applied in urban-related construction [4,5]. The UR field is also gradually integrating digital technologies to improve regeneration efficiency and quality [6]. Specifically, digital technologies have penetrated various stages of UR: in the previous planning phase, providing precise data collection and analysis for target projects as well as efficient planning and design solutions [7], to assist decision-makers in making rational judgments; during the design process, providing the simulation effects of target objects to better optimize project schemes [8]; in the implementation process, providing comprehensive monitoring and management of UR projects throughout the entire process, to effectively avoid construction defects and potential safety issues [9]; and after the project is finished, improving residents’ quality of life through digital technology applications such as intelligent public transportation, security monitoring, real-time urban information, and cultural and educational facilities [10]. Moreover, government policy releases in some countries have also played a catalytic role in promoting the application of digital technologies in UR [11,12].
However, despite digital technologies bringing more possibilities to UR, the complexity and multi-dimensional characteristics of UR create challenges for the application of digital technologies due to the involvement of multiple stakeholders, complex socioeconomic factors, and multi-dimensional spatial and temporal scales [13,14]. Additionally, in some underdeveloped regions, the lack of corresponding financial support leads to slow research progress, preventing them from benefiting from digital technologies in the short term [15]. Although current research has demonstrated the application pathways of digital technologies in specific application scenarios, there is a lack of systematic evaluation of the practicality and compatibility of digital technologies in UR, as well as comprehensive analysis of their application effects and influencing factors. This research gap limits the in-depth understanding of how digital technologies can better support UR.
Therefore, based on this background, the research question addressed in this study is “how can digital technologies support urban regeneration?” To attempt to answer this question, this study employs the systematic literature review method to examine the application status, technical support, and existing deficiencies of digital technologies in major UR research fields. Through meta-analysis and bibliometric analysis, the performance of different digital technologies in various UR projects is summarized and synthesized, and the future development trends of digital technologies in UR are further explored. While existing systematic reviews have examined digital technologies in UR from various perspectives, this study distinguishes itself through several key innovations that address critical gaps in current knowledge systems. Unlike previous research that primarily focuses on categorical analysis of digital applications [16], this study introduces a comprehensive analytical framework that systematically explores the complex interrelationships between stakeholders, project scales, and project stages. Compared to smaller sample sizes in previous research, this study analyzes 56 high-quality articles, expanding the evidence base to enable more robust quantitative analysis of technology application patterns. Methodologically, this study transcends descriptive categorization by providing detailed percentage-based analysis that reveals the technology distribution in the Pre-UR stages (10–12%), during implementation, and Post-UR stages (14%), discovering previously unidentified temporal patterns in technology adoption. In comparison, while existing reviews propose general frameworks, this study constructs a multi-dimensional analytical system that captures the complete technological application system spanning from building-level to city-level implementations. This study aims to provide a systematic theoretical foundation for understanding the mechanisms of digital technologies’ role in UR and to provide reference for constructing an integrated application framework of digital technologies and UR.

2. Methodology

This study adopted a mixed research approach [17], as illustrated in Figure 1, which combines qualitative analysis, primarily based on a systematic literature review and meta-analysis [16], with quantitative analysis, primarily based on a bibliometric analysis [18], to further analyze and address the research questions. By employing this combined qualitative and quantitative research approach, not only can the limitations arising from using a single research method be reduced, but the research conclusions can also be made more comprehensive. Consequently, this mixed research approach helps reduce errors that may occur in results from using a single research method, also promotes in-depth understanding and mastery of knowledge and research development trends in related fields [19].

2.1. Systematic Literature Review

In the data collection step, this study employed the PRISMA methodology [20] to conduct comprehensive searches of two databases: the Scopus and Web of Science Core Collection databases. These two databases provide access to academic journals worldwide and are the most commonly used databases for literature search work [21]. This systematic literature review included the following review criteria: (1) whether it is relevant to the topic of UR; and (2) whether any digital technology is incorporated into the UR process. If the literature did not involve any aspect of UR or did not mention the use of digital technology, it was excluded from the research scope. The data collection and cleaning flowchart shown in Figure 2 provides a detailed explanation of the processing steps for the relevant literature, shown as the PRISMA flow diagram [20]. In this step, there were not any automation tools involved.
Identification: In this process, filters composed of keywords and Boolean operators from the Scopus and Web of Science Core Collection databases were employed [22]. English terms with high matching relevance to “urban regeneration” and “digital (intelligent) technology” were searched, including “urban regeneration”, “urban renewal”, “urban revitalization”, “urban redevelopment”, “urban transformation”, “urban upgrading”, “urban modernization”, “urban rehabilitation”, “urban renaissance”, and “city revitalization”, and “digital technology”, “intelligent technology”, and “smart technology”, as well as specific terms such as “BIM”, “GIS”, and “CIM”. Through the implementation of these selection criteria, 868 and 943 publications were identified from the two databases, respectively. The relationship diagram between publication volume and publication year (as shown in Figure 3) demonstrates a significant increase in publications since 2016. Based on this situation, this study decided to review publications published between 2016 and 2025, with the literature search date for this systematic review ending on 20 April 2025.
Screening: First, research indicates that approximately 90% of Web of Science indexed journals can also be found in Scopus [23]. To avoid duplication, repeated publications from both databases were excluded in this study. Second, the focus was placed on publications primarily in article format, followed by narrowing the date range to after 2016. Ultimately, 597 articles were included in the pending review list.
Eligibility: This stage primarily involved further eligibility confirmation of the literature through reading titles, abstracts, and keywords. After further checking, 93 articles demonstrated high relevance to this study.
Inclusion: This step required reading complete articles and reviewing the inclusion criteria standards. Through full-text reading, the content of each article was further confirmed for its relevance to the theme of digital technology applications in UR. Following this process, this study finally retained 56 articles that met the research theme for subsequent analysis.

2.2. Bibliometric Analysis

Bibliometric analysis explores the knowledge domains involved in this research through quantitative analytical approaches and identifies their developmental trends. In this study, the regional distribution patterns were analyzed, while also employing the VOSviewer (version 1.6.20) processing tool to conduct co-word analysis of high-frequency keywords corresponding to the literature publications [24].

2.3. Meta-Analysis Framework

This study also conducted a meta-analysis of the target literature publications, employing this statistical method to synthesize and analyze the research findings from the literature. Through systematic analysis of the literature, multiple sets of variables were identified, including the scale and size-level of UR projects, the different types of digital technologies involved, and the main stakeholders engaged in the projects. By conducting visualization analysis of the relationships among these variables, the results can be better presented and facilitate our summarization and understanding.

3. Data Analysis and Results

This study conducted a systematic literature review to classify and summarize the qualifying literature, providing a multi-perspective analysis of practical pathways for digital technology empowerment in UR. First, through structured classification of existing research publications, this study revealed key application domains and technological requirements of digital technologies in multi-dimensional UR scenarios. Second, employing bibliometric methods, the research analyzed the trends and evolution of hotspots from the temporal–spatial distribution and thematic clustering dimensions. Finally, utilizing meta-analysis methods, this study focused on examining the correlations among phased technology deployment strategies during project cycles, multi-stakeholder collaboration mechanisms, and technology adoption logic across different project scales. Through a systematic analytical framework to address the complexity of UR, this study aims to provide evidence for both theory and practice.

3.1. Systematic Review of DT in UR

Through a systematic literature review, this study ultimately compiled and summarized 56 published literature publications (shown as Table A1 in Appendix A). The consolidated literature was categorized into three major classes, Pre-UR, During-UR, and Post-UR, based on which stage of UR projects the digital technologies participated in. Furthermore, seven categories of digital technologies were identified and summarized (shown as Table 1): GIS technologies, BIM/CIM technologies, AI and Machine Learning, Visualization and Interaction, Remote Sensing and Surveying, Data Fusion and Analytics, and Cloud Computing and Platforms. These digital technologies function at different nodes, with some digital technologies covering the entire project lifecycle. The following analysis will use different project phase nodes as entry points to analyze and review the specific work scope of UR projects at each stage and the coverage of digital technology types.

3.1.1. Pre-UR Stage

The preparation stage of UR projects typically encompasses the identification of diverse projects, planning and design, determination of regeneration objectives, formulation of regeneration strategies, generation of regeneration decisions, and comprehensive assessment of target areas prior to project initiation. Particularly for projects such as urban villages, Geographic Information Systems (GISs) serve as the fundamental core technology, integrated with Remote Sensing Imagery (RSI) and Street View Imagery (SVI) technologies, enabling project planners to accurately identify target areas requiring regeneration and conduct corresponding regeneration potential assessments. This integrated technological approach has progressively evolved into a positioning tool for UR, while simultaneously providing data support for government formulation of regeneration strategies (such as demolition scope and renovation priorities) [25,26,27,28,29]. Three-dimensional modeling technology and Information and Communication Technology (ICT) introduced on the foundation of GIS applications can more efficiently provide users with immersive project participation experiences and interactive design opportunities [30,31,32,33], thereby providing helpful assistance for planning decisions. Through the integration of Public Participation Geographic Information Systems (PPGISs) with Multi-Criteria Decision Analysis (MCDA), the proposed Spatial Decision Support System (SDSS) provides a tool for promoting the participation of property owners, investors, and urban managers in UR projects [34]. AI tools combined with machine learning analyze historical data and predict economic effects following spatial regeneration [35]. Additionally, machine learning and deep learning, primarily based on Random Forest models, further enhance data analysis efficiency during the planning phase, providing robust scientific evidence for land spatial utilization optimization and climate resilience planning through the quantification of factors such as building morphology and environmental indices [27,36,37,38,39,40]. Building Information Modeling (BIM) and City Information Modeling (CIM) provide multi-source information integration capabilities and full project lifecycle management capacity. Simulation software such as OpenFOAM simulate environmental conditions and evaluate the environmental impact and safety of regeneration schemes [41,42,43], providing design frameworks for land renewal projects [44]; these technologies collaborate with digital twin technology in the auxiliary calculation of carbon emissions and historical heritage protection work [35,45,46]. These digital technologies provide substantial assistance for the preliminary planning and design stage of UR projects, facilitating the development of the subsequent stages.

3.1.2. During-UR Stage

Following the completion of the preparation stage, UR projects proceed with subsequent production and construction work according to different design schemes and plans, encompassing demolition, reconstruction, continuous monitoring, and protection of target buildings. During the interim implementation and monitoring activities of UR projects, the extensive integration of digital technologies provides dynamic feedback and precise regulatory mechanisms for project governance. Unlike the preparation stage, oriented toward identification and decision support, the interim stage emphasizes real-time optimization of construction execution, environmental monitoring, and construction scheduling. During implementation, the deep coupling of BIM with three-dimensional technology and Virtual Reality (VR) technology significantly enhances construction efficiency and worker collaboration capabilities, effectively mitigating spatial conflicts and rework risks during construction processes, and achieving shortened construction periods while ensuring construction quality [41,47,48]. In real-time environmental monitoring and construction scheduling, IoT technology and intelligent energy consumption monitoring systems play crucial roles. For instance, sensor networks constructed in Seoul UR projects monitor air quality, noise, and vibration indicators, uploading data to cloud platforms in real-time. When PM2.5 concentrations exceed threshold values, the system automatically triggers feedback and adjusts processes through work suspension orders to improve environmental quality [30]; similarly, energy consumption monitoring systems used in Guangzhou old city renovation processes achieve multi-source data sharing through ICT technology integration, optimizing construction sequences and improving project return on investment [49]. Additionally, the treatment of secondary products during UR project construction includes using GISs for construction waste management [50]. This also involves evaluating impacts on residents’ temporary living environments during UR project implementation and effectively fine-tuning project progress in real-time [51].

3.1.3. Post-UR Stage

When UR projects approach completion or conclude, various follow-up activities continue, including but not limited to collecting opinions and suggestions from stakeholders, conducting long-term continuous monitoring activities for data collection to evaluate project operational effectiveness, and helping subsequent UR policy formulation.
Among these activities, digital technologies are employed to analyze UR effects: GISs are utilized to analyze spatial environments in Post-UR areas, constructing geographic databases through multi-source data integration and revealing through analysis that UR can help mitigate urban heat island effects [52]; similarly, based on GIS technology combined with GPS visualization tools, pedestrian flow monitoring is conducted for completed regeneration projects to evaluate post-project performance [53]; additionally, GISs are employed for comparative analysis of Italian and Portuguese historical urban districts before and after regeneration to quantify the effects (such as housing price increases and tourist volume growth in Porto post-regeneration) and verify the achievement of planning objectives [54]. These studies show that digital technologies in the Post-UR phase focus on quantification of environmental indicators and social responses, achieving sustained attention to UR projects following completion and validating the potential of digital technologies in the Post-UR stage.
In summary, Figure 4 illustrates the distribution of digital technology applications across three phases of UR, revealing that certain digital technologies are present throughout the entire lifecycle of UR projects, while others exhibit distinct distributional patterns. BIM/CIM (12%) and GIS (10%) technologies demonstrate substantial application proportions in the Pre-UR stage, highlighting their core roles in preliminary investigation, assessment, and planning design [26]. The relatively low application proportion of AI and machine learning (2%) technologies in this phase indicates certain application gaps for intelligent technologies during the Pre-UR stage. During the implementation (During-UR stage), digital technology applications are relatively evenly distributed, with data fusion and analytics (8%) technologies better meeting the demands for integrating multi-source heterogeneous data in UR activities [53,55]. The increased application proportion of intelligent technologies indicates the emerging value of these technologies in construction management and quality monitoring [41,50,56]. The application proportions of BIM/CIM (4%) and GIS (9%) technologies show slight decreases compared to the previous phase, reflecting the primary importance of their functions in the design and preliminary planning stages. The distribution of digital technology applications in the Post-UR stage exhibits distinct management- and maintenance-oriented characteristics. Compared to the previous phase, GIS technology applications significantly increase to 14%, closely related to the spatial management and operational maintenance requirements in the later stages of UR. Cloud computing and platform technologies reach an application proportion of 4%, demonstrating the importance of digital platforms in long-term operational management. However, both BIM/CIM and AI technologies decrease to 1% application proportions, revealing the relative limitations of these technologies in post-operational phases. Remote sensing and surveying technologies maintain relatively stable application proportions (2–4%) across all three stages, reflecting the continuous value of these technologies in comprehensive UR monitoring. Visualization and interaction technologies exhibit a pattern of an initial decline followed by an increase, reflecting their dual functions in preliminary presentation and post-phase management. Data fusion and analytics technologies appear only in the During-UR and Post-UR stages, with application proportions of 8% and 5%, respectively, indicating that these technologies primarily serve the multi-element data integration and decision support requirements during the implementation and operational phases. This quantitative analysis enhances understanding of the research questions and provides clearer insights into the roles and distribution patterns of digital technologies across different UR project stages.

3.2. Results of Bibliometric Analysis

The bibliometric analysis conducted in this study employs regional distribution and co-word analysis as analytical entry points. The research focuses on the distribution of the literature by year and country, further discussing the development trends of research directions related to digital technology applications in UR. Additionally, this study utilizes co-word analysis to examine the terminology used in the research and their relationships, employing the VOSviewer (version 1.6.20) application to analyze the filtered database information.

3.2.1. Spatial and Temporal Distribution of Studies

Based on the analysis of the literature, the global publication volume regarding digital technology research themes in UR exhibits distinct geographical distribution characteristics (as shown in Figure 5). Asian countries and regions occupy an absolute main position in terms of publication volume, with approximately 42 published papers, reflecting high attention in research and an active interest in this field. In this region, besides China, countries such as South Korea, India, and Iran also demonstrate considerable research output, primarily conducting research on regional urban development planning and sustainable development, as well as the construction of UR participation platforms [31,34,37], all of which demonstrate the overall strength of the Asian region in this research field. The research distribution in Europe is relatively dispersed but stable, with Italy and Spain being the primary contributors in the region, mainly focusing on evaluation references for the reconstruction of historical sites in UR activities and the identification and regeneration of old buildings [54,57]. Countries including the UK, Germany, Portugal, and the Czech Republic also present research contributions, reflecting European researchers’ sustained attention to this field. This distribution pattern indicates that European research forces are relatively balanced, with various countries participating in related research to different degrees. The participation of North America and Oceania is relatively low, while the African region has only a few countries such as Egypt reporting relevant research. This distribution pattern may reflect differences in attention levels, research resource investment, and relevant policy orientations across different regions regarding this research theme, while also suggesting that this research field may be closely related to specific needs or challenges in the Asian region.

3.2.2. Keyword Co-Occurrence Analysis

Based on keyword co-occurrence analysis using VOSviewer, digital technologies in UR research exhibit a hierarchical conceptual framework and distinct evolutionary trends. From the comprehensive analysis of the network structure in Figure 6 and density distribution in Figure 7, “urban renewal”, “urban regeneration”, and “GIS” constitute the most prominent central nodes, with their high frequency and strong connection density establishing the foundational status of these concepts in existing research. Particularly, GIS serves as a core digital infrastructure, playing a vital role in spatial analysis and data integration for UR decision-making. Notably, cutting-edge technological concepts such as “digital twins”, “machine learning”, and “artificial intelligence”, while positioned at the network periphery, form tight associations with traditional planning terminologies like “urban planning” and “site selection analysis”, highlighting the penetration trend of data-driven decision-making paradigms and confirming the methodological transformation of urban studies toward computational paradigms.
The co-occurrence network presents two major interconnected research clusters: the technological innovation cluster, centered on BIM, GIS, and machine learning, emphasizes technological breakthroughs and application expansion of digital tools; and the theoretical framework cluster connects fundamental concepts such as “urban development”, “sustainability”, and “stakeholders”, focusing on the social dimensions and governance challenges of urban transformation. This dual orientation both advances innovative applications of technological tools such as digital twin simulation and maintains deep attention to the social attributes of UR, reflecting the interdisciplinary integration characteristics of this research field. The presence of VOSviewer nodes further validates the rigor of the bibliometric methods, aligning with academic advocacy for transparent research trajectory visualization [58]. The multi-scale spatial distribution of keywords validates the complete technological application system of digital technologies from BIM applications at the building level to GIS integration at the city level, and then to comprehensive decision support at the city level. This scale integration capability constitutes an important driving force for digital technologies to promote the intelligent transformation of UR.

3.3. Meta-Analysis

To further analyze the results, this study additionally selected the different stakeholders involved in UR and the project size-level as classification criteria, which provides a more intuitive and clear observation of the application distribution of different digital technologies across different variables, as shown in Figure 8. Furthermore, through analyzing the relationships among these three factors, it can be concluded that UR operates as a multi-scale process, with each level requiring different technological approaches and stakeholder participation strategies.

3.3.1. Project Scale and Digital Technology Adoption

This study categorized UR-related projects according to project size-level into hierarchical divisions: building-level, referring to UR activities targeting individual buildings or street neighborhoods; district-level, encompassing UR activities covering communities, industrial areas, and urban block-sized areas; and city-level, with this scale taking the city as the main body and extending coverage to surrounding areas, typically involving large-scale or multi-regional collaborative projects.
Through analyzing the left half of Figure 8, differential application distributions of digital technologies across different project levels can be observed. GIS and BIM/CIM show high participation rates across all levels, indicating the foundational status of such technologies in UR projects. Visualization and interactive technologies in micro-level projects can further promote public participation and stakeholder communication; AI and machine learning applications in district and city levels are primarily distributed for large-scale data analysis and strategic decision-making prediction and simulation; and remote sensing and monitoring technologies at the city-level primarily support assessment and continuous monitoring capabilities for UR projects, especially for urban entities.

3.3.2. Stakeholder Involvement and Digital Strategies

UR projects involve extensive economic, environmental, cultural, political, and social impacts [11]. Therefore, to achieve objectives at each project stage, various UR activities also include different stakeholders [2]. Through reviewing different stakeholders appearing in the literature, this study categorized them into eight groups, with their descriptions and details as shown in Table 2: residents, government agencies, planners/policy makers, developers/business entities, technical professionals/expert teams, researchers/academia, non-governmental organizations, and enterprises/industries. After grouping, this study analyzed different stakeholders and the digital technologies they use or are involved with, calculating frequency statistics, resulting in the right side of Figure 8, which shows the connections between different stakeholders and digital technologies. Thicker lines represent a higher frequency of use/contact/involvement between the two points. Observation reveals that GIS technology has the broadest scope of involvement, touching almost all stakeholders, with particularly close connections to residents, government personnel, and planners/designers, which fully shows GISs’ characteristics for collecting, storing, managing, computing, analyzing, displaying, and describing geographic information [59]. For BIM/CIM technology, visualization technology, and remote sensing mapping technologies, due to the operational foundation required by these technologies, smaller associations with residents can be observed from the figure. Relative to government workers and technical professionals, these technologies serve as powerful tools for promoting UR decision-making and feasibility assessment [44]. Compared to traditional digital technologies, the emergence of AI and machine learning technologies, data fusion analysis, and big data cloud platforms has added options for residents’ participation in UR processes, facilitating better communication and connection among different stakeholders, which is conducive to UR projects developing toward public preferences.

4. Discussion

4.1. Challenges in Applying Digital Technologies to Support UR

This literature review provides a progressively clearer understanding of how digital technologies support UR through analysis of their positioning and application across three dimensions: stakeholders, project scale, and project stage. Several challenges have also emerged, which will be analyzed through these three dimensions.

4.1.1. Stakeholder-Related Challenges

UR projects involve coordination among diverse stakeholders, each facing significant challenges in digital technology application. The analysis results show that while GIS technology reaches almost all stakeholders, varying technical thresholds lead to unequal participation levels. Residents, as important participants in UR activities, face evident technical barriers in BIM/CIM and remote sensing surveying applications, creating a divide that limits the depth and breadth of public participation. Although government agencies, planning designers, and technical professionals possess strong technical application capabilities, obstacles remain in cross-departmental coordination and data sharing. Enterprises and developers focus more on the economic benefits of technology application, while research institutions and non-governmental organizations emphasize the social impacts of technology. These goal differences further exacerbate the complexity of coordination and communication among stakeholders.

4.1.2. Scale-Dependent Technology Adoption Challenges

Differences between the size-level of UR projects present multi-level challenges for digital technology application. The scale of the project significantly affects the digital technologies’ applicability and efficacy, creating distinct implementation requirements across different project dimensions. In building-level micro-projects, technology application is relatively simple but requires high precision, with visualization and interactive technologies needing to balance technical complexity with user-friendliness. In community and district-level meso-projects, data integration and multi-system coordination become primary challenges, requiring balance between technical standardization and project specificity. In city-level macro-projects, large-scale data processing and cross-regional coordination demand higher technical infrastructure requirements while involving data security and privacy protection issues across different administrative regions. Additionally, differences in investment return cycles across various project scales affect the adoption decisions for advanced digital technologies.

4.1.3. Stage-Specific Implementation Challenges

Digital technology application in different UR stages shows marked imbalance, creating stage-specific implementation challenges. There are significant differences in how technology is utilized before, during, and after UR, reflecting the varying technical requirements and stakeholder needs across project phases. In the Pre-UR stage, while GIS and BIM/CIM technologies are widely applied, AI and machine learning technology application rates are only 2%, indicating application gaps in intelligent technology for early-stage decision support. In the During-UR stage, contradictions exist between the high demand for data fusion and analysis technologies (8%) and insufficient existing technology integration capabilities, with technical requirements for real-time monitoring and dynamic control challenging system stability and response speed. In the Post-UR stage, both BIM/CIM and AI technology application rates drop to 1%, showing difficulties in value transformation for these high-initial-investment technologies in later operational maintenance, affecting the full lifecycle benefits of technology application. Meanwhile, rapid changes in technical requirements across different phases make it difficult for existing technologies to adapt to dynamic adjustment requirements.

4.2. Research Gaps and Future Direction

4.2.1. Lack of Systematic Management Framework

Existing research lacks systematic management frameworks for digital technology application in UR. Although various digital technologies are applied across different stages, scales, and stakeholder levels, unified technical integration standards and coordination mechanisms are absent. Future research should construct digital technology management models covering complete project cycles, including technical selection decision frameworks, unified data standard specifications, cross-stage technical integration mechanisms, and multi-stakeholder collaborative platforms. Such systematic frameworks should support optimal configuration of technology combinations, achieve effective coordination among different digital technologies, and establish dynamic assessment mechanisms for technology application effectiveness.

4.2.2. Insufficient Integration of Advanced AI Technologies

The application of high-level technologies such as AI and machine learning in UR remains at preliminary stages, particularly with excessively low application rates in the Pre-UR stage, failing to fully leverage their advantages in complex decision support and predictive analysis. Notably, only 1% to 2% of AI technologies are actually utilized in UR at any project level, representing a significant underutilization of these advanced capabilities. Future research directions should focus on deep application of AI technologies throughout the UR lifecycle, including developing machine learning-based algorithms for regeneration demand identification, constructing intelligent decision support systems for UR projects, establishing AI-driven project risk warning mechanisms, and exploring deep learning potential in UR effect prediction and long-term impact assessment. Simultaneously, research is needed on integration pathways between AI technologies and traditional digital technologies to form intelligent digital technology application ecosystems.

4.2.3. Cross-Scale Integration and Adaptive Technology Deployment

Future research should focus on cross-scale technology integration and adaptive technology deployment strategies. This includes establishing technology selection models that adapt to different project size-level and phase characteristics, developing digital platform architectures supporting multi-size-level coordination, and constructing technology configuration optimization algorithms based on project characteristics. Moreover, it is frequently necessary to modify digital technologies to fit specific urban contexts encompassing legal, cultural, and technical considerations, yet this critical adaptation process is not well examined in current research. The contextual adaptation challenge extends beyond the project scale to include regulatory compliance requirements, local cultural practices, community expectations, and existing technological infrastructure capabilities that vary significantly across different urban environments. Additionally, in-depth research is needed on social impacts and equity issues of digital technology application to ensure technological progress benefits all stakeholders and promotes more inclusive and sustainable UR practices. Future studies should systematically investigate how digital technologies can be effectively customized for diverse urban contexts while maintaining their core functionality and interoperability across different legal frameworks, cultural settings, and technical environments. These findings indicate that effective UR requires a systematic approach considering both technological capabilities, project scale requirements, and stakeholder participation mechanisms. The multi-dimensional relationship network revealed by this analysis demonstrates the inherent complexity of contemporary UR projects and highlights the necessity of establishing a comprehensive planning framework in future research that can adapt to diverse technology applications, multi-size-level coordination requirements, and diversified stakeholder participation patterns. This comprehensive perspective provides a foundation for developing more effective UR governance strategies that can fully utilize digital technologies while ensuring inclusive stakeholder participation across all project scales.

5. Conclusions

This systematic review comprehensively elucidates how digital technologies support UR processes through a multi-dimensional analytical framework. Through a systematic literature review, bibliometric analysis, and meta-analysis methods, this study conducted in-depth analysis of 56 high-quality papers, revealing the complex ecosystem of digital technology applications in UR. The research found that digital technologies serve as key driving factors throughout the UR lifecycle, showing distinct application pattern differences across different phases. GIS and BIM/CIM technologies construct the infrastructure for UR decision-making, while emerging technologies such as AI and machine learning are gradually penetrating this field. Multi-scale analysis indicates that technology adoption strategies must be carefully tailored according to project characteristics, with building-level micro-projects emphasizing user-friendly visualization technologies, district-level meso-projects requiring robust data integration capabilities, and city-level macro-projects demanding comprehensive analytical frameworks. Stakeholder analysis highlights the importance of addressing digital divides and ensuring inclusive participation, with technical professionals and government agencies showing high technology engagement levels while residents still face significant barriers to meaningful participation.
However, digital technology application in UR still faces numerous challenges, including the absence of systematic management frameworks, insufficient integration of advanced AI technologies, and limited cross-scale coordination mechanisms. Future research should focus on developing comprehensive management frameworks that can coordinate different project scales and stakeholder groups while exploring how emerging technologies can more effectively integrate into UR processes. Critically, it must be recognized that digital technologies are merely tools and means for UR, not ends in themselves. While promoting digital technology application, human-centered concepts must be maintained, organically combining technology application with community needs, cultural preservation, ecological environment, and economic development to achieve truly sustainable UR. The research results provide a foundation for formulating more effective UR governance strategies that can fully utilize digital technologies while ensuring inclusive stakeholder participation across all project scales, offering a reference for advancing this field toward more comprehensive, equitable, and technology-enhanced UR practices.

Author Contributions

Conceptualization, X.X., X.D. and Y.Q.; methodology, X.X. validation, H.J. and P.C.; formal analysis, X.D.; investigation, X.D. and P.C.; resources, X.X., X.D. and Y.Q.; data curation, X.D. and Y.Q.; writing—original draft preparation, X.D.; writing—review and editing, X.D. and X.X.; visualization, X.D.; supervision, X.X. and Y.Q.; project administration, X.X.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China under grant number 72101054; the Fundamental Research Funds for the Central Universities under grant number 2242023R40040; and the China Postdoctoral Science Foundation under grant number 2025M771607.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

Author Peng Chen was employed by Southeast University Architectural Design & Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A

Table A1. Comprehensive information of reviewed studies.
Table A1. Comprehensive information of reviewed studies.
Author and YearStageScaleStakeholderDigital TechnologyRegion
Wu & Leng, 2025 [43]Pre-URMicro-level
Macro-level		Residents
Planners/Policy Makers
Government Agencies		BIM/CIMChina
Wang et al., 2025 [28]Pre-URMeso-levelResidents
Planners/Policy Makers		Visualization and InteractionChina
Sun et al., 2025 [27] Pre-URMeso-levelPlanners/Policy MakersGIS
AI and Machine Learning		China
Manna et al., 2025 [37]Pre-URMeso-levelResidents
Planners/Policy Makers		AI and Machine LearningIndia
Li et al., 2025 [60]Pre-URMacro-levelGovernment Agencies
Planners/Policy Makers		GISChina
Chen et al., 2025 [61]Pre-UR
During-UR		Meso-levelGovernment AgenciesBIM/CIM
Visualization and Interaction
Cloud Computing and Platforms		China
Zhou et al., 2024 [40]Pre-UR
During-UR		Macro-levelResidents
Government Agencies
Planners/Policy Makers		AI and Machine LearningChina
Zhao et al., 2024 [33]Pre-URMacro-levelResidents
Government Agencies
Developers/Business Entities		BIM/CIMChina
Zhang et al., 2024 [46]Pre-URMeso-levelPlanners/Policy Makers
Technical Professionals/Expert Teams		BIM/CIMChina
Yilmaz & Alkan, 2024 [62]Pre-URMacro-levelResidents
Government Agencies
Researchers/Academia		AI and Machine Learning
Cloud Computing and Platforms		Turkey
Ye et al., 2024 [63]Pre-URMacro-levelGovernment Agencies
Planners/Policy Makers		AI and Machine LearningChina
Teklemariam, 2024 [64]During-URMacro-levelResidents
Government Agencies
Non-Governmental Organizations		GISAfrica
Mutani et al., 2024 [52]Post-URMacro-levelResidents
Planners/Policy Makers
Researchers/Academia		GIS
AI and Machine Learning		Italy
Lin & Song, 2024 [65]Pre-URMicro-levelPlanners/Policy MakersVisualization and Interaction
AI and Machine Learning		China
Lin et al., 2024 [55]During-URMacro-levelPlanners/Policy MakersGIS
Remote Sensing and Surveying
AI and Machine Learning		China
Hu et al., 2024 [25]Pre-URMeso-levelResidents
Planners/Policy Makers
Government Agencies		Visualization and Interaction
Remote Sensing and Surveying		China
Chen et al., 2024 [66]Post-URMicro-levelResidents
Planners/Policy Makers
Government Agencies		GIS
Data Fusion and Analytics
Cloud Computing and Platforms		China
Chao et al., 2024 [51]During-URMeso-levelResidentsGIS
Visualization and Interaction
AI and Machine Learning		China
Carruthers & Wei, 2024 [36]Pre-URMacro-levelPlanners/Policy MakersAI and Machine LearningUnited State
Yu et al., 2023 [38]Pre-URMicro-levelPlanners/Policy Makers
Professionals/Expert Teams		AI and Machine Learning
Visualization and Interaction		China
Xueqiang et al., 2023 [67]Pre-URMacro-levelPlanners/Policy Makers
Government Agencies		GISChina
Tian et al., 2023 [68]Pre-URMacro-levelResidents
Government Agencies		Cloud Computing and PlatformsChina
Shi et al., 2023 [26]Pre-URMeso-levelPlanners/Policy MakersGIS
Remote Sensing and Surveying
AI and Machine Learning		China
Kim et al., 2023 [69]During-URMeso-levelResidentsCloud Computing and PlatformsSouth Korea
Faraji et al., 2023 [44]Pre-URMacro-levelPlanners/Policy MakersBIM/CIMIran
Duan et al., 2023 [70]Pre-URMicro-levelResidentsGISChina
Dong et al., 2023 [71]Pre-UR
Post-UR		Macro-levelEnterprises/IndustriesGISChina
Chen et al., 2023 [72]Pre-URMeso-levelResidents
Government Agencies		GIS
Visualization and Interaction		China
Allan et al., 2023 [35]Pre-URMacro-levelResidents
Planners/Policy Makers
Government Agencies		GIS
BIM/CIM
Visualization and Interaction.
AI and Machine Learning		Australia
Akl et al., 2023 [45]Pre-URMicro-levelPlanners/Policy Makers
Government Agencies		GIS
BIM/CIM
Visualization and Interaction		Egypt
Zhang et al., 2022 [39]Pre-URMeso-levelPlanners/Policy Makers
Government Agencies		GIS
Visualization and Interaction
AI and Machine Learning		China
Zhang & Lee, 2022 [73]Pre-URMeso-levelPlanners/Policy MakersGIS
Data Fusion and Analytics
Cloud Computing and Platforms		China
Sütçüoğlu & Önaç, 2022 [74]Pre-URMacro-levelResidents
Planners/Policy Makers		GIS
Data Fusion and Analytics		Turkey
Seve et al., 2022 [75]Pre-URMeso-levelResidentsData Fusion and Analytics
Cloud Computing and Platforms		Spain
Sampaio et al., 2022 [48]During-URMicro-levelTechnical Professionals/Expert TeamsBIM/CIM
Visualization and Interaction		Portugal
Shih et al., 2021 [76]Pre-UR
During-UR
Post-UR		Meso-level
Macro-level		Residents
Planners/Policy Makers
Non-Governmental Organizations		GIS
Data Fusion and Analytics
China
Praharaj, 2021 [31]Pre-UR
Post-UR		Macro-levelPlanners/Policy Makers
Government Agencies		Data Fusion and AnalyticsIndia
Porat & Shach-Pinsly, 2021 [77]Pre-URMacro-levelPlanners/Policy Makers
Government Agencies		GIS
Data Fusion and Analytics		Israel
Tiboni et al., 2020 [54]Post-URMacro-levelResidents
Government Agencies		GISItaly
Portugal
Kim et al., 2020 [30]Pre-UR
During-UR
Post-UR		Macro-levelResidents
Government Agencies
Researchers/Academia		GIS
BIM/CIM
Data Fusion and Analytics		South Korea
Kang et al., 2020 [78]Pre-URMacro-levelResidents
Government Agencies		GISSouth Korea
Dogan et al., 2020 [79]Pre-URMeso-levelResidents
Government Agencies		GIS
AI and Machine Learning		Turkey
Boulanger et al., 2020 [53]Pre-UR
During-UR
Post-UR		Micro-levelResidents
Government Agencies
Researchers/Academia
Professionals/Expert Teams		GIS
Data Fusion and Analytics
Visualization and Interaction		Italy
Zhang et al., 2019 [80]Pre-URMicro-levelResidents
Planners/Policy Makers
Professionals/Expert Teams		Visualization and InteractionChina
Xu et al., 2019 [81]Pre-URMeso-levelResidents
Planners/Policy Makers
Developers/Business Entities		GIS
Remote Sensing and Surveying		China
Wang & Fukuda, 2019 [55]Pre-URMacro-levelResidents
Government Agencies
Researchers/Academia		GIS
BIM/CIM		Japan
Wang et al., 2019 [49]Pre-UR
During-UR
Post-UR		Meso-levelResidents
Government Agencies
Developers/Business Entities		GIS
Data Fusion and Analytics		China
Ruiz-Pérez et al., 2019 [57]Pre-UR
During-UR
Post-UR		Micro-levelResidents
Government Agencies
Developers/Business Entities		GIS
Data Fusion and Analytics		Spain
Omidipoor et al., 2019 [34]Pre-UR
During-UR
Post-UR		Macro-levelDevelopers/Business EntitiesGIS
Data Fusion and Analytics		Iran
Dowsett & Harty, 2019 [41]Pre-UR
During-UR		Micro-levelDevelopers/Business Entities
Professionals/Expert Teams		BIM/CIMUK
Ding et al., 2019 [47]Pre-UR
During-UR		Micro-levelResidents
Professionals/Expert Teams		BIM/CIM
Visualization and Interaction		China
Faltejsek et al., 2018 [42]Pre-URMeso-levelGovernment Agencies
Developers/Business Entities		BIM/CIM
Visualization and Interaction		Czech Republic
Abarca-Alvarez et al., 2018 [82]Pre-URMacro-levelResidents
Planners/Policy Makers
Government Agencies
Developers/Business Entities		GIS
Data Fusion and Analytics		Spain
Xu et al., 2017 [29]Pre-URMicro-levelResidents
Enterprises/Industries		GIS
Remote Sensing and Surveying		China
Tsai, 2016 [83]Pre-URMacro-levelResidents
Planners/Policy Makers		GIS
Visualization and Interaction		China
Trubka & Glackin, 2016 [32]Pre-URMicro-levelResidentsBIM/CIM
Visualization and Interaction		Australia

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Figure 1. Mixed review methodology flowchart.
Figure 1. Mixed review methodology flowchart.
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Figure 2. Flowchart for data collection and cleaning.
Figure 2. Flowchart for data collection and cleaning.
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Figure 3. Distribution of publication years in the literature (Red arrows mark a sharp surge in output volume beginning in 2016).
Figure 3. Distribution of publication years in the literature (Red arrows mark a sharp surge in output volume beginning in 2016).
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Figure 4. Distribution of digital technologies in different stages.
Figure 4. Distribution of digital technologies in different stages.
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Figure 5. Distribution of publications based on region.
Figure 5. Distribution of publications based on region.
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Figure 6. Keyword co-occurrence networks.
Figure 6. Keyword co-occurrence networks.
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Figure 7. Keyword co-occurrence density.
Figure 7. Keyword co-occurrence density.
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Figure 8. Sankey diagram (scale–digital technology–stakeholder).
Figure 8. Sankey diagram (scale–digital technology–stakeholder).
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Table 1. Frequency of digital technology applications by category in the literature.
Table 1. Frequency of digital technology applications by category in the literature.
CategoriesNamesFrequency
GIS
Technologies		Spatial Analysis, ArcGIS, POI Analysis, Geodatabase, Network Analysis28
BIM/CIM TechnologiesBuilding Information Modeling, City Information Modeling, Revit, 3D Modeling17
AI and Machine LearningMachine Learning, Random Forest, Deep Learning, GANs, Predictive Modeling16
Visualization and InteractionVR/AR, Digital Twin, Simulation (CFD/OpenFOAM), Spatial Syntax13
Remote Sensing and SurveyingRemote Sensing (RS), LiDAR, Point Cloud, Satellite Imagery, Street View Imagery10
Data Fusion and AnalyticsBig Data Analytics, IoT Sensors, Social Media Mining, MCDA (AHP/TOPSIS)9
Cloud Computing and PlatformsCloud Computing, Mobile Apps, Web Platforms, Google Earth Engine (GEE)5
Table 2. Distribution of stakeholders engaged in UR projects by category.
Table 2. Distribution of stakeholders engaged in UR projects by category.
CategoriesStakeholderFrequency
ResidentsUrban residents, citizens, community residents, the public, local residents, community members, students, the general public, tourists, etc.30
Government AgenciesLocal government, government departments, planning departments, management and planning departments, relevant government departments, municipal government, cultural heritage management departments, etc.33
Planners/Policy MakersPlanners, urban planners, policy makers, urban decision makers, formulators, urban managers, etc.18
Developers/Business EntitiesReal estate developers, design and development companies, property owners, investors, managers, real estate stakeholders, etc.15
Technical Professionals/Expert TeamsTechnical personnel, industrial planning engineers, design teams, designers, urban builders and designers, technical partners, technical suppliers, construction workers, heritage experts, etc.14
Researchers/AcademiaScientific researchers, academic researchers, academic teams, urban research scholars, etc.6
Non-Governmental OrganizationsNon-governmental organizations, communities, etc.3
Enterprises/IndustriesIndustrial enterprises, factories, etc.2
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MDPI and ACS Style

Xiahou, X.; Ding, X.; Chen, P.; Qian, Y.; Jin, H. Digital Technologies in Urban Regeneration: A Systematic Literature Review from the Perspectives of Stakeholders, Scales, and Stages. Buildings 2025, 15, 2455. https://doi.org/10.3390/buildings15142455

AMA Style

Xiahou X, Ding X, Chen P, Qian Y, Jin H. Digital Technologies in Urban Regeneration: A Systematic Literature Review from the Perspectives of Stakeholders, Scales, and Stages. Buildings. 2025; 15(14):2455. https://doi.org/10.3390/buildings15142455

Chicago/Turabian Style

Xiahou, Xiaer, Xingyuan Ding, Peng Chen, Yuchong Qian, and Hongyu Jin. 2025. "Digital Technologies in Urban Regeneration: A Systematic Literature Review from the Perspectives of Stakeholders, Scales, and Stages" Buildings 15, no. 14: 2455. https://doi.org/10.3390/buildings15142455

APA Style

Xiahou, X., Ding, X., Chen, P., Qian, Y., & Jin, H. (2025). Digital Technologies in Urban Regeneration: A Systematic Literature Review from the Perspectives of Stakeholders, Scales, and Stages. Buildings, 15(14), 2455. https://doi.org/10.3390/buildings15142455

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