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Urban Heritage and Spatial Regeneration in the Age of Intelligence
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Urban Heritage and Spatial Regeneration in the Age of Intelligence

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Architectural Design, Urban Science, and Real Estate".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 786

Editors

Prof. Dr. Weizhen Chen

E-Mail Website
Guest Editor
College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Interests: urban design; landscape urbanism; historic urban landscape
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Cheng

E-Mail Website
Guest Editor
College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Interests: local culture; cultural landscape heritage; artificial intelligence; landscape perception; heritage tourism
Special Issues, Collections and Topics in MDPI journals
Dr. Yinying Tao

E-Mail Website
Guest Editor
College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Interests: urban cultural heritage; historic districts; artificial intelligence; spatial perception
Dr. Pan He

E-Mail Website
Guest Editor
1. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
2. Tongji Architectural Design (Group) Co., Ltd. Shanghai, Shanghai 200092, China
Interests: urban regeneration; green gentrification; landscape design

Special Issue Information

Dear Colleagues,

Against the backdrop of rapid advancements in digital technology and artificial intelligence, the intersection of urban physical space and social logic has reached unprecedented complexity. Urban heritage conservation and regeneration have evolved beyond physical restoration, becoming a systemic transformation involving digital modeling, behavioral analysis, interest negotiation, and social justice. A key challenge in global sustainable urban development is how to leverage cutting-edge technologies to enhance governance efficiency while preserving historical identity and ensuring spatial rights for diverse social groups.

"Intelligent digitalization" offers a novel perspective for understanding the complexities of urban systems. By integrating big data, artificial intelligence, digital twins, and non-invasive neural monitoring, we can more accurately analyze the evolution of urban heritage and simulate the social impacts of urban renewal. This shift from "technological rationality" to "intelligent empowerment" not only provides scientific support for the revitalization of historical spaces but also offers data-driven decision tools to address gentrification and achieve spatial justice.

This Special Issue focuses on the synergy between "technology–human–space," emphasizing methodological innovations across disciplines to tackle practical challenges in urban regeneration. We examine not only how digital tools can optimize landscape design and heritage protection, but also how these technologies capture spatial behavioral decisions and psychological responses to foster more inclusive, equitable, and emotionally resonant urban places.

This Special Issue aims to establish a cutting-edge academic platform that brings together global scholars to share innovative research in intelligent heritage, computational urban design, behavioral geography, spatial justice, and environmental psychology, exploring new pathways for urban landscape transformation in the digital age.

This Special Issue welcomes (but is not limited to) the following topics:

  • Applications of AI and digital technology in heritage conservation;
  • Data-driven urban regeneration methodologies;
  • Heritage tourism and virtual/augmented reality;
  • Neuro-urbanism, environmental psychology, and spatial behavior studies;
  • Policy and governance innovations for sustainable urban regeneration;
  • Regional technological practices in a globalized context.

Prof. Dr. Weizhen Chen
Dr. Yue Cheng
Dr. Yinying Tao
Dr. Pan He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban heritage
  • urban regeneration
  • artificial intelligence
  • spatial justice
  • behavioral spatial decision-making
  • gentrification
  • landscape planning and design
  • digital twin
  • social equity
  • environmental psychology
  • heritage tourism

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 26698 KB  
Article
Evaluating Urban Street Space Quality Using Multi-Source Data and Fully Convolutional Neural Networks: A Case Study of Xi’an’s Historic Urban Area
by Na Liu, Xiaowei Zheng and Jun Ma
Buildings 2026, 16(13), 2574; https://doi.org/10.3390/buildings16132574 - 27 Jun 2026
Viewed by 198
Abstract
Street space quality in historic cities has become a central concern in heritage conservation and urban renewal. Nevertheless, existing evaluation frameworks often overlook the historical dimension and insufficiently address the interaction effects among influencing factors. Taking the historic urban area of Xi’an as [...] Read more.
Street space quality in historic cities has become a central concern in heritage conservation and urban renewal. Nevertheless, existing evaluation frameworks often overlook the historical dimension and insufficiently address the interaction effects among influencing factors. Taking the historic urban area of Xi’an as a case study, this study constructed a comprehensive assessment framework for street space quality comprising five dimensions: accessibility, comfort, convenience, safety, and historicity. A total of 15 indicators were quantified for 404 street segments across four street typologies—commercial, residential, historic, and mixed-use—using multi-source data and Baidu Street View image analysis based on a fully convolutional neural network. The GeoDetector model was then applied to identify key influencing factors and explore their interaction effects. The results reveal that comfort and convenience are the dominant dimensions affecting street space quality. Among all indicators, street interface permeability and facility density show the strongest explanatory power. Furthermore, all pairs of influencing factors exhibit either bi-factor enhancement or nonlinear enhancement, highlighting the synergistic effects of multiple variables in shaping street quality. Based on these findings, this study proposes differentiated renewal strategies for the four street types and offers a transferable methodological framework for data-driven assessment and targeted intervention in the renewal of historic urban streets. Full article
(This article belongs to the Special Issue Urban Heritage and Spatial Regeneration in the Age of Intelligence)
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36 pages, 27595 KB  
Article
Green Gentrification and Resident Support in Shanghai’s Regenerating Waterfront
by Pan He, Yue Cheng and Weizhen Chen
Buildings 2026, 16(13), 2480; https://doi.org/10.3390/buildings16132480 - 23 Jun 2026
Viewed by 208
Abstract
Post-industrial waterfront regeneration can improve environmental quality and public space, but it may also produce green gentrification and unequal access to regeneration benefits. To support socially responsive planning evaluation, this study examines how green gentrification is spatially manifested and how residents perceive and [...] Read more.
Post-industrial waterfront regeneration can improve environmental quality and public space, but it may also produce green gentrification and unequal access to regeneration benefits. To support socially responsive planning evaluation, this study examines how green gentrification is spatially manifested and how residents perceive and support waterfront green space development in Shanghai’s Yangpu Riverside. A sequential mixed-methods design combines census, housing price, and green space data from 2000 to 2020 with 317 resident questionnaires. The study identifies socio-spatial changes associated with green gentrification, cross-culturally adapts and validates the Gentrification Worldview Instrument (GWI), and examines the associations among gentrification worldviews, place attachment, and support for green space development. Results show no statistically significant relative acceleration in housing price growth in near-waterfront neighborhoods during the regeneration period, but reveal an expanding housing price premium, educational upgrading, and population decline. These patterns are consistent with a spatially differentiated tendency toward green gentrification embedded in the broader state-led waterfront regeneration process, rather than demonstrating an independent effect of greenbelt construction. The Chinese-adapted GWI retains the three dimensions of neighborhood preservation, development support, and social integration. Among surveyed residents, development support and place identity are positively associated with support for waterfront green space development, whereas neighborhood preservation is negatively associated with support. The results further indicate a statistical mediation pattern in which place identity forms a significant indirect association between development support and support for green space development. The findings provide an evidence-based framework for evaluating inclusive waterfront regeneration and suggest that planning and design should integrate green space accessibility, local memory, residents’ perceptions, and social equity. Full article
(This article belongs to the Special Issue Urban Heritage and Spatial Regeneration in the Age of Intelligence)
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