Underground pipe network is a critical city infrastructure, which plays an important role in smart city management. As the detailed three-dimensional (3D) scene of underground pipe networks is difficult to construct, and massive numbers of pipe points and segments are difficult to manage, a 3D pipe network modeling and organization method is explored in this study. First, the modeling parameters were parsed from the pipe network survey data. Then, the 3D pipe segment and point models were built based on parametric modeling algorithms. Finally, a heterogeneous data structure for the 3D pipe network was established through loose quadtree data organization. The proposed data structure was suitable for 3D Tiles, which was adopted by Cesium (a web-based 3D virtual globe); hence, a multitude of pipe networks can be viewed in the browser. The proposed method was validated by generating and organizing a large-scale 3D pipe network scene of Beijing. The experimental results indicate that the 3D pipe network models formed by this method can satisfy the visual effect and render the efficiency required for smart urban management.

Introduction Previous research usually focused on high-frequency crash clusters (surrounded by high-frequency crashes), which overlooked outlier locations where high-frequency crashes were surrounded by low-frequency crashes. Neglecting spatiotemporal outliers might overlook critical factors for safety improvements. Methods Using pedestrian-vehicle crash data in North Carolina from 2007 to 2019, this study proposes an enhanced spatiotemporal analysis framework (combined with Approximate Nearest Neighbour and the Global Moran I index) to distinguish spatiotemporal crash outliers from aggregated/dispersed patterns. Random parameters ordered logit models with heterogeneity in means are used to model ordered injury severities, identify significant factors and explore in-depth heterogeneity across observations. Results Likelihood ratio test results indicate significant instability exists in factors across four spatiotemporal patterns: high-low outlier, low-high outlier, high-high cluster and low-low cluster. Also, spatiotemporal variations and shifts in the proportion of crashes that suffer more severe injuries are founded on the marginal effects of several factors. Specific countermeasures and policy guidance are suggested under different patterns. In high-low outliers, drunk drivers increase the probability of more severe crash outcomes in public vehicular areas from 16.3% to 92.8%. Meanwhile, in low-high outliers, 64.8% of the crashes with speed limits between 45 and 75 mph would result in more severe outcomes. By increasing the mean, older pedestrians, drunk pedestrians and urban areas increase this proportion to 91.6%, 87.2% and 78.2%, respectively. Conclusion These findings underscore the importance of strengthening alcohol tests and setting safety crossing facilities for older pedestrians in identified high-speed urban areas. All these propose a calling for spatiotemporal outlier investigation in future crash analysis and prevention.

A high-fidelity 3D urban building model requires large quantities of detailed textures, which can be non-tiled or tiled ones. The fast loading and rendering of these models remain challenges in web-based large-scale 3D city visualization. The traditional texture atlas methods compress all the textures of a model into one atlas, which needs extra blank space, and the size of the atlas is uncontrollable. This paper introduces a size-adaptive texture atlas method that can pack all the textures of a model without losing accuracy and increasing extra storage space. Our method includes two major steps: texture atlas generation and texture atlas remapping. First, all the textures of a model are classified into non-tiled and tiled ones. The maximum supported size of the texture is acquired from the graphics hardware card, and all the textures are packed into one or more atlases. Then, the texture atlases are remapped onto the geometric meshes. For the triangle with the original non-tiled texture, new texture coordinates in the texture atlases can be calculated directly. However, as for the triangle with the original tiled texture, it is clipped into many unit triangles to apply texture mapping. Although the method increases the mesh vertex number, the increased geometric vertices have much less impact on the rendering efficiency compared with the method of increasing the texture space. The experiment results show that our method can significantly improve building model rendering efficiency for large-scale 3D city visualization.

Accurate building extraction (BE) from multimodal remote sensing imagery is critical to urban management and sustainable development. Although the integration of optical and synthetic aperture radar (SAR) data can provide complementary information, large-scale deployment is difficult due to blurring object boundaries, severe modality disparities, and expensive computing costs. To overcome these concerns, we proposed SSDFNet, a state space driven multimodal fusion framework for the Optical-SAR BE task. By involving VMamba into the backbone, SSDFNet captures long-term contextual dependencies with linear computing complexity, enabling efficient global modeling. To improve extraction quality, we proposed the Semantic Detail Fusion Module (SDFM), which injects high-level semantic cues into high-resolution features for finer structural delineation; the Dual Stream Fusion Module (DSFM), which adaptively aligns heterogeneous optical and SAR representations; and the Frequency Domain Enhancement Fusion Module (FDEFM), which improves edge sharpness and topological consistency through frequency decomposition and recombination. Extensive experiments on the DFC23 Track 2, MSAW, and INRIA datasets demonstrate the superiority of SSDFNet, achieving IoU scores of 85.31%, 80.70%, and 84.38%, respectively. In addition, we construct a 0.5 m resolution Optical-SAR rooftop dataset covering Singapore, on which SSDFNet attains an IoU of 80.41%. Furthermore, we quantitatively evaluate the effectiveness of the extracted building rooftops in a downstream solar photovoltaic (PV) potential estimation task by integrating rooftops with height information. The results demonstrate that improved geometric accuracy of building extraction directly leads to more reliable PV potential estimation, highlighting the practical significance of the proposed method for renewable energy planning.