Introduction: This study delves into the spatial preferences of children for play spaces within high-density urban block environments, specifically targeting the area of Baihua Second Road in Shenzhen, China.Methods: Recognizing the critical role of play in children’s development, and the unique challenges posed by dense urban settings, this research employs multiclass logistic regression models and negative binomial regression models to construct a detailed mathematical analysis of neighborhood spatial elements and children’s play space preferences. Data was meticulously gathered through both objective observations of 14 different types of spaces within the block, and subjective assessments via children’s responses to a series of environment photos, capturing the essence of over 3,000 child participants’ interactions and choices.Results: Key findings reveal a pronounced preference among children for soft facility features and visually appealing spatial experiences, suggesting a nuanced understanding of play space needs beyond traditional playground designs. Notably, the study identifies that while cartoon-style designs in play facilities might increase moderate attractiveness, ordinary designs hold broader appeal, indicating a preference for diversity in play space aesthetics. These insights offer profound implications for urban planners and designers, advocating for a child-centered approach in the creation of urban play environments that prioritize aesthetic diversity, and the integration of natural elements.Conclusion: Moreover, the study situates Baihua Second Road as a paradigmatic case, illustrating the methodology and analytical framework applied in addressing the complex interplay between children’s play preferences and urban spatial configurations. By incorporating a comprehensive data-driven analysis, this research contributes significantly to the discourse on child-friendly urban design, offering valuable strategies for cultivating inclusive and engaging urban play spaces for children amidst the constraints of high-density city living.

For ultra-high-resolution (UHR) image semantic segmentation, striking a balance between computational efficiency and storage space is a crucial research direction. This paper proposes a Feature Fusion Network (EFFNet) to improve UHR image semantic segmentation performance. EFFNet designs a score map that can be embedded into the network for training purposes, enabling the selection of the most valuable features to reduce storage consumption, accelerate speed, and enhance accuracy. In the fusion stage, we improve upon previous redundant multiple feature fusion methods by utilizing a transformer structure for one-time fusion. Additionally, our combination of the transformer structure and multibranch structure allows it to be employed for feature fusion, significantly improving accuracy while ensuring calculations remain within an acceptable range. We evaluated EFFNet on the ISPRS two-dimensional semantic labeling Vaihingen and Potsdam datasets, demonstrating that its architecture offers an exceptionally effective solution with outstanding semantic segmentation precision and optimized inference speed. EFFNet substantially enhances critical performance metrics such as Intersection over Union (IoU), overall accuracy, and F1-score, highlighting its superiority as an architectural innovation in ultra-high-resolution remote sensing image semantic segmentation.

As urbanization intensifies, high-density communities have become a dominant urban form, making Child-Friendly Community (CFC) development crucial for sustainable urban growth. However, transforming these communities poses challenges, particularly regarding residents’ risk perceptions—an area largely overlooked in existing research. To address this gap, this study introduces “Risk Tolerance (RT)” as a key variable and constructs a multidimensional model of Child-Friendly Community Transformation Risk Tolerance (CFCTRT) to examine its structure and influencing factors. Based on survey data from residents in high-density communities in China’s first- and second-tier cities, Structural Equation Modeling (SEM) is used for empirical analysis. The results show that residents exhibit lower tolerance toward changes in safety, economy, and daily life, but higher tolerance in resource and aesthetic dimensions. Expectations for CFCs and satisfaction with current communities both positively influence CFCTRT, with satisfaction also mediating the relationship between expectations and tolerance. These findings provide a novel perspective on residents’ psychological responses to CFC transformations and offer empirical support for more inclusive and adaptive urban planning strategies.