Voxelized Point Cloud and Solid 3D Model Integration to Assess Visual Exposure in Yueya Lake Park, Nanjing

Guanting Zhang (), Dongxu Yang and Shi Cheng
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Guanting Zhang: College of Architecture, Nanjing Tech University, Nanjing 211816, China
Dongxu Yang: College of Architecture, Nanjing Tech University, Nanjing 211816, China
Shi Cheng: School of Architecture, Southeast University, Nanjing 210096, China

Land, 2025, vol. 14, issue 10, 1-21

Abstract: Natural elements such as vegetation, water bodies, and sky, together with artificial elements including buildings and paved surfaces, constitute the core of urban visual environments. Their perception at the pedestrian level not only influences city image but also contributes to residents’ well-being and spatial experience. This study develops a hybrid 3D visibility assessment framework that integrates a city-scale LOD1 solid model with high-resolution mobile LiDAR point clouds to quantify five visual exposure indicators. The case study area is Yueya Lake Park in Nanjing, where a voxel-based line-of-sight sampling approach simulated eye-level visibility at 1.6 m along the southern lakeside promenade. Sixteen viewpoints were selected at 50 m intervals to capture spatial variations in visual exposure. Comparative analysis between the solid model (excluding vegetation) and the hybrid model (including vegetation) revealed that vegetation significantly reshaped the pedestrian visual field by reducing the dominance of sky and buildings, enhancing near-field greenery, and reframing water views. Artificial elements such as buildings and ground showed decreased exposure in the hybrid model, reflecting vegetation’s masking effect. The calculation efficiency remains a limitation in this study. Overall, the study demonstrates that integrating natural and artificial elements provides a more realistic and nuanced assessment of pedestrian visual perception, offering valuable support for sustainable landscape planning, canopy management, and the equitable design of urban public spaces.

Keywords: visual exposure indicators; mobile LiDAR; urban landscapes; visual analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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