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Buildings
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Advancing Urban Analytics and Sensing for Sustainable Cities
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Advancing Urban Analytics and Sensing for Sustainable Cities

A special issue of Buildings (ISSN 2075-5309).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4010

Special Issue Editors

Dr. Zhewei Liu

E-Mail Website
Guest Editor
Department of Geography, Geomatics and Environment, University of Toronto Mississauga 3359 Mississauga Road, Mississauga, ON, Canada
Interests: GIScience; urban resilience; human mobility; spatial data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Yu

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: urban informatics; seamless positioning and navigation; mobile mapping; geospatial data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Xu

E-Mail Website
Guest Editor
Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA
Interests: GIS; remote sensing; coastal mapping; geospatial big data; geospatial cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Hanlin Zhou

E-Mail Website
Guest Editor
Department of Geography, Sustainability, Community, and Urban Studies, University of Connecticut, Storrs, CT, USA
Interests: urban social sensing; GeoAI; deep learning; perceived built environment; human mobility; GIS; human behaviors; public health; environmental criminology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban environments are evolving rapidly, driven by technological advancements and growing societal challenges. This Special Issue aims to explore cutting-edge developments in urban analytics and sensing technologies, fostering innovative research that enhances our understanding of cities. We invite contributions from a wide range of disciplines, including, but not limited to, geospatial science, urban informatics, and smart city technologies.

Key topics of interest include leveraging geospatial data and sensing technologies for urban planning, the real-time monitoring of urban dynamics, and addressing pressing issues such as sustainability, social equity, and urban resilience. Submissions may focus on novel methods, such as machine learning, big data analytics, and sensor networks, to analyze urban phenomena. We also welcome case studies demonstrating practical applications in urban mobility, environmental monitoring, and public health.

This Special Issue provides a platform to showcase interdisciplinary research that integrates advanced sensing, data-driven insights, and actionable solutions to create smarter, more sustainable urban environments. By bridging theory and practice, we aim to inspire innovative approaches and cross-disciplinary collaborations for the future of cities.

Dr. Zhewei Liu
Dr. Yue Yu
Dr. Chao Xu
Dr. Hanlin Zhou
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

  • urban analytics
  • geospatial science
  • smart cities
  • urban sensing
  • sustainability

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

Published Papers (4 papers)

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Research

24 pages, 7680 KB  
Article
Sensing Vegetation Resistance and Recovery Along Urban–Rural Gradients
by Kexin Liu, Nuo Li, Lifang Zhang, Hui Gan, Zhewei Liu, Hao Teng, Xiaomu Wang, Yulong Zeng and Jingxue Xie
Buildings 2026, 16(7), 1308; https://doi.org/10.3390/buildings16071308 - 26 Mar 2026
Viewed by 505
Abstract
Understanding vegetation-mediated mitigation of urban heat islands (UHI) is essential for sustainable urban adaptation strategies. Although vegetation responses to extreme heat events have been widely explored using satellite remote sensing and statistical methods, evidence remains limited regarding how these responses vary along urban–rural [...] Read more.
Understanding vegetation-mediated mitigation of urban heat islands (UHI) is essential for sustainable urban adaptation strategies. Although vegetation responses to extreme heat events have been widely explored using satellite remote sensing and statistical methods, evidence remains limited regarding how these responses vary along urban–rural gradients, particularly in terms of resistance and recovery dynamics. This study focuses on the North Tianshan Slope Urban Agglomeration (TNSUA) in Xinjiang, China. Based on Enhanced Vegetation Index (EVI) data from 2000 to 2022, an urban–rural gradient was delineated using impervious surface fraction. Vegetation resistance and recovery during extreme heat events were quantified to reveal spatiotemporal response patterns. Generalized additive models (GAMs) and Random Forest (RF) models were applied to identify key driving factors and to evaluate their relative importance across multiple spatial scales. The results indicate that rural land cover along the gradient provides a strong cooling effect, particularly in areas with an urban development intensity (UDI) of 70–85%. Vegetation responses show pronounced seasonal differences, with urban vegetation generally exhibiting lower resistance and recovery than rural vegetation. At the county scale, local UHI intensity is the dominant driver of vegetation responses, whereas at the pixel scale, precipitation and vapor pressure deficit (VPD) play the most critical roles. Overall, this study improves the understanding of vegetation responses to extreme heat events in arid regions and provides scientific support for nature-based urban heat adaptation strategies. Full article
(This article belongs to the Special Issue Advancing Urban Analytics and Sensing for Sustainable Cities)
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43 pages, 43591 KB  
Article
Research on the Formation Mechanism of Spontaneous Living Spaces and Their Impact on Community Vitality
by Xiyue Guan, Wei Shang, Fukang Chen and Wei Liu
Buildings 2026, 16(2), 352; https://doi.org/10.3390/buildings16020352 - 14 Jan 2026
Viewed by 584
Abstract
Spontaneous living spaces are public activity venues within cities that emerge through residents’ autonomous creation and informal planning. Although these spaces may appear disorganized, they serve vital functions: fostering social interaction, enhancing community vitality, improving spatial adaptability, and increasing life satisfaction. However, research [...] Read more.
Spontaneous living spaces are public activity venues within cities that emerge through residents’ autonomous creation and informal planning. Although these spaces may appear disorganized, they serve vital functions: fostering social interaction, enhancing community vitality, improving spatial adaptability, and increasing life satisfaction. However, research on the formation mechanisms, structural logic, resident satisfaction, and the impact of spontaneous living spaces on community vitality is limited, and there is a lack of robust research methodologies. This study aims to explore the formation mechanisms of spontaneous living spaces within historic cultural districts and their influence on community vitality. Using Wuhan’s Tanhualin National Historic and Cultural District as a case study, this research innovatively combines the Mask R-CNN deep learning model with a Random Forest regression model. The Mask R-CNN model was employed to accurately identify and perform pixel-level segmentation of 1249 spontaneous living spaces. Combined with questionnaire surveys and the Random Forest model, this study reveals non-linear relationships between key factors such as community vitality, resident satisfaction with various types of spontaneous living spaces, and crowd density. The findings show that spontaneous living spaces effectively address residents’ unmet needs for emotional connection and dynamic lifestyles—needs often overlooked by official residential planning. This research provides a reliable technical framework and quantitative decision support for regulating the formation of spontaneous living spaces, thereby enhancing residents’ quality of life and urban vitality while preserving historical character. Full article
(This article belongs to the Special Issue Advancing Urban Analytics and Sensing for Sustainable Cities)
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21 pages, 4758 KB  
Article
Explaining and Reducing Urban Heat Islands Through Machine Learning: Evidence from New York City
by Shengyao Liao and Zhewei Liu
Buildings 2026, 16(1), 186; https://doi.org/10.3390/buildings16010186 - 1 Jan 2026
Cited by 1 | Viewed by 1131
Abstract
Urban heat islands (UHIs) have intensified in rapidly urbanizing regions like New York, exacerbating thermal discomfort, public health risks, and energy consumption. While previous research has highlighted various environmental and socioeconomic contributors, most existing studies lack interpretable, fine-scale models capable of quantifying the [...] Read more.
Urban heat islands (UHIs) have intensified in rapidly urbanizing regions like New York, exacerbating thermal discomfort, public health risks, and energy consumption. While previous research has highlighted various environmental and socioeconomic contributors, most existing studies lack interpretable, fine-scale models capable of quantifying the effects of specific drivers—limiting their utility for targeted planning. To address this challenge, we develop an interpretable machine learning framework using Random Forest and XGBOOST to predict land surface temperature across 1800+ census tracts in the New York metropolitan area, incorporating vegetation indices, water proximity, urban morphology, and socioeconomic factors. Both models performed strongly (mean R2 ≈ 0.90), with vegetation coverage and water proximity emerging as the most influential cooling factors, while built form features played supporting roles. Socioeconomic vulnerability indicators showed weak correlations with temperature, suggesting a relatively equitable thermal landscape. Optimization simulations further revealed that increasing vegetation to a threshold level could lower average surface temperatures by up to 6.38 °C, with additional but smaller gains achievable through adjustments to water access and urban form. These findings provide evidence-based guidance for climate-adaptive urban design and green infrastructure planning. More broadly, the study illustrates the potential of explainable machine learning to support data-driven environmental interventions in complex urban systems. Full article
(This article belongs to the Special Issue Advancing Urban Analytics and Sensing for Sustainable Cities)
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22 pages, 6925 KB  
Article
Adaptive Urban Heat Mitigation Through Ensemble Learning: Socio-Spatial Modeling and Intervention Analysis
by Wanyun Ling and Liyang Chu
Buildings 2025, 15(21), 3820; https://doi.org/10.3390/buildings15213820 - 23 Oct 2025
Viewed by 1188
Abstract
Urban Heat Islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks for socially vulnerable populations. While the role of urban environmental features in shaping UHI patterns is well recognized, their differential impacts on diverse social groups remain underexplored—limiting the development of [...] Read more.
Urban Heat Islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks for socially vulnerable populations. While the role of urban environmental features in shaping UHI patterns is well recognized, their differential impacts on diverse social groups remain underexplored—limiting the development of equitable, context-sensitive mitigation strategies. To address this challenge, we employ an interpretable ensemble machine learning framework to quantify how vegetation, water proximity, and built form influence UHI exposure across social strata and simulate the outcomes of alternative urban interventions. Drawing on data from 1660Dissemination Areas in Vancouver, we model UHI across seasonal and diurnal contexts, integrating environmental variables with socio-demographic indicators to evaluate both thermal and equity outcomes. Our ensemble AutoML framework demonstrates strong predictive accuracy across these contexts (R2 up to 0.79), providing reliable estimates of UHI dynamics. Results reveal that increasing vegetation cover consistently delivers the strongest cooling benefits (up to 2.95 °C) while advancing social equity, though fairness improvements become consistent only when vegetation intensity exceeds 1.3 times the baseline level. Water-related features yield additional cooling of approximately 1.15–1.5 °C, whereas built-form interventions yield trade-offs between cooling efficacy and fairness. Notably, modest reductions in building coverage or road density can meaningfully enhance distributional justice with limited thermal compromise. These findings underscore the importance of tailoring mitigation strategies not only for climatic impact but also for social equity. Our study offers a scalable analytical approach for designing just and effective urban climate adaptations, advancing both environmental sustainability and inclusive urban resilience in the face of intensifying heat risks. Full article
(This article belongs to the Special Issue Advancing Urban Analytics and Sensing for Sustainable Cities)
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