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Buildings
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Data-Driven Intelligence for Sustainable Urban Renewal
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Data-Driven Intelligence for Sustainable Urban Renewal

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 2414

Special Issue Editors

Dr. Zhixing Li

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Guest Editor
School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310023, China
Interests: building indoor space design; building climate responsive design; multi-objective optimization; low carbon building design
Dr. Jing Dong

E-Mail Website
Guest Editor
School of Architecture and Fine Arts, Dalian University of Technology, Dalian 116024, China
Interests: green roof; urban renewal; urban adaptation planning
Special Issues, Collections and Topics in MDPI journals
Dr. Yukai Zou

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Interests: architectural design; building simulation; energy-efficient building; data-driven method; building retrofit
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiqiang Wang

E-Mail Website
Guest Editor
School of Architecture, Inner Mongol University of Technology, Hohhot 010051, China
Interests: regional architecture; architectural design and theory; digital design; AI-empowered urban design; low carbon architecture

Special Issue Information

Dear Colleagues,

Rapid urbanization, demographic shifts, and functional transitions have placed unprecedented pressure on many existing urban areas, which now face challenges such as aging infrastructure, inefficient land use, declining vitality, growing social disparities, and increasing environmental burdens. As a result, urban renewal has gradually shifted away from traditional “demolition–redevelopment” approaches toward more sustainable spatial restructuring, human-centered environment-making, and data-driven governance.

Meanwhile, advances in artificial intelligence (AI), sensing technologies, cloud computing, and spatial data infrastructures are profoundly transforming how cities identify problems, simulate interventions, and collaborate with communities to co-create future urban spaces. AI enables multi-source data fusion, automated spatial diagnostics, urban vitality prediction, generative design for urban form, and intelligent evaluation frameworks, supporting more precise, inclusive, and adaptive renewal strategies.

This Special Issue welcomes interdisciplinary research exploring how AI technologies—such as machine learning, computer vision, digital twins, generative design, and urban simulation—are reshaping the theories, methods, and practices of urban regeneration. We invite theoretical discussions, empirical analyses, technological innovations, and policy-oriented studies that investigate how AI can enhance the sustainability, resilience, and liveability of existing urban areas.

Areas of interest include, but are not limited to, the following topics:

  • AI-based diagnosis of urban spatial problems and decline patterns;
  • Machine learning for predicting urban vitality, mobility, and environmental performance;
  • Multi-source urban data fusion (e.g., street view imagery, IoT sensors, remote sensing, socio-economic data);
  • Digital twins and dynamic simulation for renewal strategies;
  • Applications of generative AI in block redesign, public space renewal, and streetscape transformation;
  • Intelligent evaluation tools for neighbourhood liveability, equity, and resilience;
  • Data-supported co-creation and participatory community planning;
  • AI-enabled conservation and adaptive reuse of historic or industrial districts;
  • Ethical, social, and governance issues in AI-mediated urban renewal.

By bringing together researchers, practitioners, technologists, designers, and policymakers, this Special Issue seeks to advance a systematic understanding of how artificial intelligence can empower urban regeneration, enhance spatial quality, promote social equity, and support sustainable and human-centered urban futures.

Dr. Zhixing Li
Dr. Jing Dong
Dr. Yukai Zou
Dr. Zhiqiang Wang
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-blind 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

  • artificial intelligence (AI)
  • urban regeneration
  • multi-source data fusion
  • digital twin
  • machine learning
  • generative design
  • sustainability and resilience

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Published Papers (5 papers)

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Research

24 pages, 5903 KB  
Article
A Dual-Height AI Framework for Proxy Assessment of Children’s Spatial Perception in a Large Cultural Complex
by Yingying Shen, Shuyan Zhu and Fei Zhang
Buildings 2026, 16(10), 2030; https://doi.org/10.3390/buildings16102030 - 21 May 2026
Abstract
Large-scale cultural complexes serve significant numbers of child users, yet existing spatial assessment approaches are predominantly developed from adult perspectives and rarely consider child-height environmental exposure conditions at children’s own eye level. To address this gap, this study introdus a novel dual-height proxy [...] Read more.
Large-scale cultural complexes serve significant numbers of child users, yet existing spatial assessment approaches are predominantly developed from adult perspectives and rarely consider child-height environmental exposure conditions at children’s own eye level. To address this gap, this study introdus a novel dual-height proxy assessment framework that integrates semantic segmentation with explainable machine learning, enabling scalable proxy-based spatial diagnosis without requiring direct child participation. This study proposes a proxy-based assessment framework combining dual-height street-view imagery (adult: 1.6 m; child: 1.2 m), semantic segmentation (DeepLabV3+ and PSPNet), GIS analysis, literature-informed proxy perceptual indices, and explainable machine learning (XGBoost with SHAP) applied across 480 sampling locations at the Longgang Cultural Centre, Shenzhen. The results reveal substantial differences in environmental exposure characteristics between adult-height and child-height viewpoints, with child-height imagery exhibiting 34% lower signage visibility and 30% higher spatial enclosure. Exploratory associations between environmental features and proxy perceptual indices yielded R2values ranging from 0.14 to 0.39, with walking distance, openness, and visual complexity emerging as the most influential variables within the proxy models. SHAP analysis identified non-linear relationships between environmental characteristics and proxy perception-related outcomes, and spatial mismatch mapping identified 120 locations warranting design attention. The study proposes a scalable and data-driven spatial proxy assessment framework to support child-friendly environmental screening and spatial diagnosis. The proposed proxy indices are grounded in developmental psychology literature and are not intended to substitute for children’s direct perceptual responses; rather, they are intended to characterise comparative child-height environmental exposure patterns within large-scale cultural environments. Validation using child-reported perception data, behavioural observation, participatory methods, and experimental wayfinding studies remains an important direction for future research. Full article
(This article belongs to the Special Issue Data-Driven Intelligence for Sustainable Urban Renewal)
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34 pages, 8365 KB  
Article
Multi-Dimensional Urban Waterfront Landscape Attributes and Recreational Vitality: Correlations and Strategies Based on the Beijing-Hangzhou Grand Canal
by Wei Dai, Ran Kang and Zixin Jiang
Buildings 2026, 16(9), 1774; https://doi.org/10.3390/buildings16091774 - 29 Apr 2026
Viewed by 318
Abstract
Recreational vitality is widely recognized as a core metric for assessing the quality of human settlements. Elucidating the relationship between recreational vitality and landscape characteristics is crucial for guiding the optimization and quality enhancement of urban waterfront spaces. This study takes the micro-scale [...] Read more.
Recreational vitality is widely recognized as a core metric for assessing the quality of human settlements. Elucidating the relationship between recreational vitality and landscape characteristics is crucial for guiding the optimization and quality enhancement of urban waterfront spaces. This study takes the micro-scale waterfront space of the Beijing–Hangzhou Grand Canal (Hangzhou section) as its research object, systematically analyzes the correlation between waterfront landscape attributes and recreational vitality, and formulates specific optimization strategies for enhancing recreational vitality. A total of 310 representative sampling sites was established. The study integrates machine learning-driven semantic image segmentation to achieve refined quantification of waterfront landscape metrics and employs anonymized mobile phone signaling data to dynamically characterize the spatiotemporal distribution of recreational vitality. Through correlation analysis and regression modeling, it quantifies the effect size and functional mechanisms of key landscape metrics on recreational vitality, and further proposes adaptive strategies for recreational vitality enhancement tailored to different urban functional zones. The key findings are as follows: (1) Recreational vitality is significantly higher on holidays than on workdays. High-vitality areas are concentrated in commercial functional zones, with an overall spatial gradient of “low in the east and high in the west, low in the north and high in the south”. (2) High-level Green View Factor (HGVF) shows a stable positive correlation with vitality, whereas the Sky View Factor (SVF) and the Enclosure Interface View Factor (EIVF) correlate negatively. (3) The influence of landscape metrics is strongly moderated by functional zone type: in residential functional zones, HGVF has strong explanatory power; in commercial functional zones, it shows complex nonlinearity; in ecological conservation zones, its explanatory power is generally weaker. (4) Tailored enhancement strategies are proposed for each functional zone. This study clarifies the link between core waterfront landscape attributes and micro-scale recreational vitality, and provides a scientific basis for evidence-based design and sustainable enhancement of urban waterfront spaces. Full article
(This article belongs to the Special Issue Data-Driven Intelligence for Sustainable Urban Renewal)
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27 pages, 8367 KB  
Article
The Influence of Spatial Characteristics on Crowd Behaviors: A Behavioral Proxy Approach for Street Quality Assessment
by Ke Xiang, Zhuoyue Liang, Yiyu Ouyang, Shuyin Xiang and Elena Lucchi
Buildings 2026, 16(8), 1584; https://doi.org/10.3390/buildings16081584 - 17 Apr 2026
Viewed by 372
Abstract
This study examines the street spaces of Shamian Island in Guangzhou and addresses the long-standing urban design challenge of quantifying subjective perception. Drawing on environmental psychology, it introduces “behavioral representation” as a proxy variable for perception. By synthesizing international street design guidelines, the [...] Read more.
This study examines the street spaces of Shamian Island in Guangzhou and addresses the long-standing urban design challenge of quantifying subjective perception. Drawing on environmental psychology, it introduces “behavioral representation” as a proxy variable for perception. By synthesizing international street design guidelines, the study establishes a street-characteristic indicator system covering spatial scale, interface, facilities, and landscape. Multiple linear regression (MLR) models are then applied to analyze in depth how spatial elements influence five types of behavior, including lingering, passing through, and consumption. The results show that walkway width is the core driving factor across all behavior types, while artistic landscape installations exert the most significant effect on long-duration stays. In addition, different spatial elements exhibit distinct mechanisms in shaping various behaviors. The study constructs a “space–perception–behavior” cognitive framework, providing an evidence-based tool and a methodological reference for evaluating subjective perception in urban design. Full article
(This article belongs to the Special Issue Data-Driven Intelligence for Sustainable Urban Renewal)
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36 pages, 16246 KB  
Article
A Compliance-Driven Generative Framework for Zhejiang-Style Rural Facades
by Chengzong Wu, Liping He, Shishu Tong, Jun Zhao and Yun Wu
Buildings 2026, 16(8), 1544; https://doi.org/10.3390/buildings16081544 - 14 Apr 2026
Viewed by 438
Abstract
Under the background of the Rural Revitalization Strategy, Zhejiang Province is promoting “Zhejiang-style Vernacular Dwellings” as a crucial measure to enhance the rural living environment and architectural appearance. However, traditional stylistic control tools, such as standardized rural housing design atlases, exhibit limitations including [...] Read more.
Under the background of the Rural Revitalization Strategy, Zhejiang Province is promoting “Zhejiang-style Vernacular Dwellings” as a crucial measure to enhance the rural living environment and architectural appearance. However, traditional stylistic control tools, such as standardized rural housing design atlases, exhibit limitations including weak responsiveness to villagers’ individualized needs and high professional thresholds. Consequently, they struggle to address the bottlenecks in grassroots governance efficiency caused by massive and personalized housing demands. Meanwhile, when applied to architectural design, general generative AI technologies often suffer from “structural hallucinations” and the weakening of regional characteristics due to a lack of physical tectonic constraints. Oriented towards the governance requirements of the Zhejiang Provincial Rural Housing Design Guidelines, this study proposes a compliance evaluation-driven “Contour-Semantic-Image” hierarchical generative control framework. This aims to construct a visual scheme generation and pre-screening workflow that deeply adapts to the logic of rural governance. At the data level, this research aggregates multi-source materials, including official standardized atlases, government stylistic guidelines, and real-world photographs. Through expert screening and standardized processing of 596 schemes, a dataset of 333 high-quality, finely annotated structured samples is constructed. Furthermore, a human-guided, machine-segmented workflow assisted by Segment Anything Model 2 (SAM 2) is employed to establish a semantic label system comprising 4 major categories and 13 subcategories of components, thereby achieving the structural deconstruction of architectural prior knowledge. At the generation level, a two-stage model is trained based on Stable Diffusion and ControlNet: Stage I utilizes contour conditions and “layout prompts” to generate semantic label maps, aiming to strengthen component topology and layout consistency; Stage II employs the semantic label maps and “style prompts” as conditions to generate photorealistic facade images. By utilizing explicit semantic constraints to guide the model from pixel synthesis to logical generation, it achieves the controllable rendering of stylistic details and material expressions. At the evaluation level, an automated verification system featuring “clause translation–metric calculation–comprehensive scoring” is proposed. It conducts scoring, re-ranking, and diagnostic feedback on the generated variants across three dimensions: Design Rationality (Q), General Compliance (G), and Jiangnan water-town Regional Characteristics (P-J), forming a closed-loop “Generation-Evaluation-Feedback” workflow. Overall, this framework provides a “visualizable, evaluable, and explainable” pathway for scheme generation and pre-screening in the digital governance of rural architectural appearance. Full article
(This article belongs to the Special Issue Data-Driven Intelligence for Sustainable Urban Renewal)
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49 pages, 21402 KB  
Article
CorbuAI: A Multimodal Artificial Intelligence-Based Architectural Design (AIAD) Framework for Computer-Generated Residential Building Design
by Yafei Zhao, Ziyi Ying, Wanqing Zhao, Pengpeng Zhang, Rong Xia, Xuepeng Shi, Yanfei Ning, Mengdan Zhang, Xiaoju Li and Yanjun Su
Buildings 2026, 16(3), 668; https://doi.org/10.3390/buildings16030668 - 5 Feb 2026
Cited by 1 | Viewed by 859
Abstract
Integrating artificial intelligence (AI) into residential architectural design faces challenges due to fragmented workflows and the lack of localized datasets. This study proposes the CorbuAI framework, hypothesizing that a multimodal AI system integrating Pix2pix-GAN and Stable Diffusion (SD) can streamline the transition from [...] Read more.
Integrating artificial intelligence (AI) into residential architectural design faces challenges due to fragmented workflows and the lack of localized datasets. This study proposes the CorbuAI framework, hypothesizing that a multimodal AI system integrating Pix2pix-GAN and Stable Diffusion (SD) can streamline the transition from floor plan generation to elevation and interior design within a specific regional context. We developed a custom dataset featuring 2335 manually refined Chinese residential floor plans and 1570 elevation images. The methodology employs a specialized U-Net V2.0 generator for functional layout synthesis and an SD-based model for stylistic transfer and elevation rendering. Evaluation was conducted through both subjective professional scoring and objective metrics, including the Perceptual Hash Algorithm (pHash). Results demonstrate that CorbuAI achieves high accuracy in spatial allocation (scoring 0.88/1.0) and high structural consistency in elevation generation (mean pHash similarity of 0.82). The framework significantly reduces design iteration time while maintaining professional aesthetic standards. This research provides a scalable AI-driven methodology for automated residential design, bridging the gap between schematic layouts and visual representation in the Chinese architectural context. Full article
(This article belongs to the Special Issue Data-Driven Intelligence for Sustainable Urban Renewal)
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