As urbanization accelerates, façade defects in existing residential buildings have become increasingly prominent, posing serious threats to structural safety and residents’ quality of life. In the high-density built environment of Shenzhen, traditional manual inspection methods exhibit low efficiency and high susceptibility to omission errors. This study proposes an integrated framework for façade defect detection that combines unmanned aerial vehicle (UAV)-based visible-light and thermal infrared imaging with deep learning algorithms and parametric three-dimensional (3D) visualization. Three representative residential communities constructed between 1988 and 2010 in Shenzhen were selected as case studies. The main findings are as follows: (1) the fusion of visible and thermal infrared images enables the synergistic identification of cracks and moisture intrusion defects; (2) shooting distance significantly affects mapping efficiency and accuracy—for low-rise buildings, 5–10 m close-range imaging ensures high mapping precision, whereas for high-rise structures, medium-range imaging at approximately 20–25 m achieves the optimal balance between detection efficiency, accuracy, and dual-defect recognition capability; (3) the developed Grasshopper-integrated mapping tool enables real-time 3D visualization and parametric analysis of defect information. The Knet-based model achieves an mIoU of 87.86% for crack detection and 79.05% for leakage detection. This UAV-based automated inspection framework is particularly suitable for densely populated urban districts and large-scale residential areas, providing an efficient technical solution for city-wide building safety management. This framework provides a solid foundation for the development of automated building maintenance systems and facilitates their integration into future smart city infrastructures.

Urban green infrastructure (UGI), a key component of nature-based solutions (NbSs), plays a vital role in enhancing urban resilience. Nonetheless, the absence of a thorough resilience evaluation for UGI has hindered the efficacy of its design and implementation. This article proposes an innovative urban environmental resilience index (ERI) framework designed to evaluate the lifecycle performance of UGI. First, a coupled environmental resilience evaluation system is proposed that encompasses indicators for the adaptation to acute disturbances and the mitigation of chronic pressures. Second, the inventive formulas for calculating the environmental resilience index are presented, which establish the weighting of indicators through Delphi-analytic hierarchy process (AHP) analysis, and the Storm Water Management Model (SWMM), GaBi, and i-Tree models are employed for the quantitative assessment. Third, four representative UGI scenarios in urban built-up areas have been selected for comparative analysis and in-depth discussion by calculating the resilience index. This research presents UGI solutions as adaptive measures for “Black Swan” events and “Gray Rhino” phenomena, offering significant case studies and methodological frameworks which will inform future endeavours in green and sustainable urban development.

The thermal behavior of roofs significantly impacts the indoor thermal environment. Rooftop mitigation strategies (RMS), as effective measures to reduce cooling load and improve indoor thermal comfort, have been extensively studied. However, the lack of comparative experimental RMS studies and the limitations of simulation software in accurately reproducing RMS thermal performance post-implementation highlight research gaps. This study utilized reduced-size models to investigate the thermal performance of RMSs - cool coating roof, photovoltaic (PV) roof, and PV cool coating roof - across summer, transition season, and winter, and further developed internal roof surface temperature prediction models through theoretical analysis and experimental data calibration. The results demonstrated that all RMSs outperformed a reference roof in reducing both interior and exterior temperatures, with cool coating roof exhibiting the best thermal performance in summer. The ranking of the internal thermal performance of RMS in summer from best to worst was as follows: cool coating roof, PV cool coating roof and PV roof. In transitional season and winter, PV roof had the lowest exterior surface temperature and PV coating roof had the lowest interior surface temperature. The maximum internal surface temperatures of the cool coating roof in summer were 1.6 °C and 1.5 °C lower than those of PV roof and PV cool coating roof, respectively. The combination of PV and cool coatings only provided better cooling effects in terms of interior surface temperature during the transitional season and winter. This study provides insights for decision-making regarding RMS in subtropical hot and humid regions.

Rapid urbanization has intensified public housing development and building density, posing significant challenges to residents’ well-being and urban sustainability. With the population of the Greater Bay Area on the rise, enhancing the spatial quality of public housing is now essential. The study proposed a quantitative framework to evaluate the relationship between the residential design elements and perceived density in high-density public housing neighborhoods. It employed a virtual reality perception experiment to analyze the relationship between significant spatial indicators and perceived density by investigating 16 high-density residential layout models in 3 configurations: Tower-Enclosed, Balanced Slab-Enclosed, and Staggered Slab-Enclosed. The results indicate that: (1) greater building height intensifies perceived density, leading to sensations of overcrowding and discomfort; (2) an increased sky ratio mitigates perceived density, fostering a more open and pleasant environment; (3) recessed residential facades enhance residents’ density perception; and (4) Staggered Slab-Enclosed Layout configurations receive the most favorable evaluations regarding perceived density. The authors attempt to go beyond current regulations to propose tailored solutions for Shenzhen’s high-density context, improving spatial efficiency and residential comfort in future public housing designs. The finding provides scientific evidence to support urban planners and policymakers in developing more resilient and sustainable high-density neighborhoods.

Green real estate is a new form of development that seeks to reduce the negative impact of real estate construction on the environment and society. It offers various forms of environmental and social utility and also bears higher construction costs. Residents’ satisfaction and willingness to pay (WTP) are therefore of great significance for the progress of green real estate. However, residents’ satisfaction with green residential areas and its correlation with WTP has been insufficiently researched. This study draws on face-to-face interviews conducted with 614 households in Qingtangwan (an exemplary green real estate project in Beijing) to extract information about residents’ satisfaction and WTP. With regard to satisfaction, it identifies five main components, specifically the operation and maintenance of residential areas, indoor and outdoor comfort, building quality, sustainable community attachment, and public facility accessibility (this last category had lower satisfaction). In terms of WTP, residents’ mean WTP was found to be CHY 204.23 per month, which is approximately USD 31.19. In addition to the bid value, the indoor and outdoor comfort, building quality, sense of community, and public facility accessibility were all found to be significantly related to residents’ WTP. Relevant policy recommendations for promoting this development mode included the establishment of public funds, the regular collection and disclosure of information about residents’ satisfaction, phased rent increases, and the enhancement of community cultural construction.

As renovating existing residential buildings shifts towards more detailed methodologies, conducting comprehensive diagnostic assessments before renovation is crucial for achieving successful outcomes. This research introduces an innovative large-scale diagnostic assessment method for existing residential buildings, addressing the inefficiencies, redundancies, and subjective biases present in traditional diagnostic processes through intelligent assistance modeling. The proposed method focuses on five key elements: construction year, exterior walls, windows, balconies, and shading devices, categorizing assessment levels into four grades (A, B, C, D) based on varying renovation intensities. Evaluation criteria are established for service life, thermal performance, degradation, and aesthetic quality. An intelligent assistance model, constructed using training datasets, enables rapid large-scale assessments, significantly reducing the evaluation time while maintaining an accuracy rate of over 95%. Empirical testing on residential buildings in Shenzhen confirmed the model’s effectiveness, demonstrating its superior accuracy and efficiency compared to traditional methods. A weighted analysis revealed that the impact of each factor on the building’s condition was as follows: exterior wall thickness (0.38), exterior finish deterioration (0.35), window deterioration (0.29), balcony deterioration (0.28), and construction year (0.23). Additionally, an interactive software application integrating the intelligent assessment model was developed. This study employed an interdisciplinary approach, combining machine learning and big data, offering a new perspective on building assessment and providing significant reference value for future residential renovation and urban planning.