Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it is essential to extract high-precision two-dimensional (2D) and three-dimensional (3D) Urban Morphological Parameters (UMPs) and integrate socio-economic data for UFZ classification. In this study, we conducted UFZ classification using airborne LiDAR point clouds, aerial images, and point-of-interest (POI) data. Initially, we fused LiDAR and image data to obtain high-precision land cover distributions, building height models, and canopy height models, which served as accurate data sources for extracting 2D and 3D UMPs. Subsequently, we segmented city blocks based on road network data and extracted 2D UMPs, 3D UMPs, and POI Kernel Density Features (KDFs) for each city block. We designed six classification experiments based on features from single and multiple data sources. K-Nearest Neighbors (KNNs), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to classify UFZs. Furthermore, to address the potential data redundancy stemming from numerous input features, we implemented a feature optimization experiment. The results indicate that the experiment, which combined POI KDFs and 2D and 3D UMPs, achieved the highest classification accuracy. Three classifiers consistently exhibited superior performance, manifesting a substantial improvement in the best Overall Accuracy (OA) that ranged between 8.31% and 17.1% when compared to experiments that relied on single data sources. Among these, XGBoost outperformed the others with an OA of 84.56% and a kappa coefficient of 0.82. By conducting feature optimization on all 107 input features, the classification accuracy of all three classifiers exceeded 80%. Specifically, the OA for KNN improved by 10.46%. XGBoost maintained its leading performance, achieving an OA of 86.22% and a kappa coefficient of 0.84. An analysis of the variable importance proportion of 24 optimized features revealed the following order: 2D UMPs (46.46%) > 3D UMPs (32.51%) > POI KDFs (21.04%). This suggests that 2D UMPs contributed the most to classification, while a ranking of feature importance positions 3D UMPs in the lead, followed by 2D UMPs and POI KDFs. This highlights the critical role of 3D UMPs in classification, but it also emphasizes that the socio-economic information reflected by POI KDFs was essential for UFZ classification. Our research outcomes provide valuable insights for the rational planning and development of various UFZs in medium-sized cities, contributing to the overall functionality and quality of life for residents.

Understanding the influence of urban morphology on Land Surface Temperature (LST) is essential for urban planning, development, and mitigating the urban heat island effect. Leveraging high-resolution remote sensing data, this study systematically extracted 64 2D urban morphological parameters (UMPs) and 28 3D UMPs, along with their corresponding summer and winter LST data, at both the grid level (using a 30 m × 30 m grid as the minimum unit) and the block level (using an urban block as the minimum unit). The 2D UMPs were derived from landscape indices of land cover, while the 3D UMPs included 3D building-related UMPs (BUMPs) and tree-related UMPs (TUMPs). Ultimately, multiple statistical methods were employed to investigate the complex mechanisms through which these 2D and 3D UMPs influence LST across summer and winter. This study showed the following results: (1) Most 2D and 3D UMPs significantly correlated with LST in both seasons at the grid/block levels, with stronger correlations at block level. (2) Stepwise regression revealed that combining 2D and 3D UMPs enhanced LST explanation, achieving R² = 70.9% (summer) and 65.7% (winter) for the entire area, with consistent results in built-up zones. (3) Relative importance analysis identified 35 (summer) and 28 (winter) influential features, which were ranked as follows: 2D UMPs > 3D BUMPs > 3D TUMPs. This highlights 2D UMPs’ dominance while confirming 3D UMPs’ significance. These findings emphasize the need for integrated 2D and 3D urban design, considering both planar layouts and vertical configurations of buildings/vegetation. This study provides practical guidance for thermal environment mitigation and sustainable urban development through optimized spatial planning.

With rapid urbanization and industrialization, PM_2.5 pollution exerts a significant negative impact on the urban eco-environment and on residents’ health. Previous studies have demonstrated that cities in China are characterized by urban particulate matter island (UPI) phenomena, i.e., higher PM_2.5 concentrations are observed in urban areas than in rural settings. How, though, nature and socioeconomic environments interact to influence UPI intensities is a question that still awaits a general explanation. To fill this knowledge gap, this study investigates spatiotemporal variations in UPI effects with respect to different climatic settings and city sizes in 240 cities in China from 2000 to 2015 using remotely sensed data and explores the effective mechanism of human–environmental factors on UPI dynamics based upon the Geographically Weighted Regression (GWR) model. In particular, a conceptual framework that considers natural environments, technology, population, and economics is proposed to explore the factors influencing UPIs. The results show (1) that about 70% of the cities in China selected exhibited UPI effects from 2000 to 2015. In addition, UPI intensities and the number of UPI-related cities decreased over time. It is noteworthy that PM_2.5 pollution shifted from urban to rural areas. (2) Elevation was the most efficient driving factor of UPI variations, followed by precipitation, population density, NDVI, per capita GDP, and PM_2.5 emission per unit GDP. (3) Climatic backgrounds and city sizes influenced the compositions and performance of dominant factors regarding UPI phenomena. This study provides valuable a reference for PM_2.5 pollution mitigation in cities experiencing global climate change and rapid urbanization and thus can help sustainable urban developments.