Due to the façade visibility, intuitive expression, and multi-view redundancy, oblique photogrammetry can provide optional data for large-scale urban LoD-2 reconstruction. However, the inherent noise in oblique photogrammetric point cloud resulting from the image-dense matching limits further model reconstruction applications. Thus, this paper proposes a novel method for the efficient reconstruction of LoD-2 building models guided by façade structures from an oblique photogrammetric point cloud. First, a building planar layout is constructed combined with footprint data and the vertical planes of the building based on spatial consistency constraints. The cells in the planar layout represent roof structures with a distinct altitude difference. Then, we introduce regularity constraints and a binary integer programming model to abstract the façade with the best-fitting monotonic regularized profiles. Combined with the planar layout and regularized profiles, a 2D building topology is constructed. Finally, the vertices of building roof facets can be derived from the 2D building topology, thus generating a LoD-2 building model. Experimental results using real datasets indicate that the proposed method can generate reliable reconstruction results compared with two state-of-the-art methods.

Shape segmentation in urban environments forms the foundation for tasks such as classification and reconstruction. Most artificial buildings with complex structures are composed of multiple simple geometric primitives. Based on this assumption, this paper proposes a divisive hierarchical clustering algorithm that uses shape classification and outliers reassignment to segment LiDAR point clouds in order to effectively identify the various shapes of structures that make up buildings. The proposed method adopts a coarse-to-fine strategy. Firstly, based on the geometric properties of different primitives in a Gaussian sphere space, coarse extraction is performed using Gaussian mapping and the DBSCAN algorithm to identify the primary structure of various shapes. Then, the error functions are constructed after parameterizing the recognized shapes. Finally, a minimum energy loss function is built by combining the error functions and binary integer programming (BIP) to redistribute the outlier points. Thereby, the accurate extraction of geometric primitives is achieved. Experimental evaluations on real point cloud datasets show that the indicators of precision, accuracy, and F1 score of our method are 0.98, 0.95, and 0.96 (point assignment) and 0.97, 0.95, and 0.95 (shape recognition), respectively. Compared with other state-of-the-art methods, the proposed method can efficiently segment planar and non-planar structures with higher quality from building point clouds.

Current assessments of urban green spaces (UGS) rely largely on two-dimensional (2D) indicators, which fail to capture the three-dimensional (3D) structure necessary for evaluating ecological functions and human exposure. Among these, the Normalized Difference Vegetation Index (NDVI) describes top-down canopy greenness from a nadir perspective, whereas the Green View Index (GVI) quantifies vegetation visibility at street level from a pedestrian perspective. Because the relationship between NDVI and GVI remains unclear, multi-indicator assessments become difficult to interpret, limiting their ability to jointly characterize urban greenery. To address these gaps, we develop a synergy framework that integrates remote sensing with street-view images. First, we aligned the observation scales through street-view depth estimation and converted NDVI into fractional vegetation cover (FVC) through nonlinear mapping to unify measurement units. Correlation experiments revealed that the consistency between GVI and FVC was weak across the city (R² = 0.27) but substantially stronger along arterial roads with continuous vegetation (R² = 0.61). On this basis, we design a Green Synergy Index (GSI) that combines FVC and GVI using fractional power-law adjustments and an interaction term to capture their joint effects. Robustness tests indicate that GSI effectively handles extreme or mismatched cases, differentiates greening patterns, and integrates complementary information from nadir and street views without numerical instability. Furthermore, we assess the consistency between GSI and land surface temperature (LST), showing that the proposed index improves explanatory power compared with FVC and GVI alone (by 5.6% and 8.8%, respectively). Application to the study area yields a mean GSI value of 0.44 on a 0–1 scale, with spatial variations closely associated with road geometry and functional zoning. This enables the identification of mismatched canopy and visibility segments and supports targeted, climate-sensitive green infrastructure planning.