Urban greenery is essential for environmental quality, visual comfort, and residents’ well-being, and it becomes especially critical in high-density residential compounds where outdoor space is limited. This study proposes a pedestrian-scale visibility framework that integrates solid 3D models (DEM, extruded buildings, water) with voxelized LiDAR point clouds to reconstruct fine-resolution outdoor scenes and to quantify visual perception indicators, including green view factor (GVF), sky view factor (SVF), and average green distance (AGD). A residential community in Nanjing is used as the case study. Line-of-sight sampling was performed on 223 viewpoints distributed across three empirically identified activity zones, and a resident questionnaire was conducted in parallel (279 valid responses). The results show that the visually open zone, characterized by relatively high SVF, moderate GVF, and larger vegetation setback (higher AGD), is also the zone most preferred by residents, whereas the zone with the highest GVF but strong enclosure is least preferred. This consistency between modeled indicators and survey responses confirms that excessive, close-range planting may reduce usability, while a balanced combination of greenery and openness better supports everyday outdoor activities. The proposed Point-Cloud-Based approach, therefore, provides a data-driven basis for planning, evaluating, and managing outdoor environments in dense urban residential areas, and ultimately reaching the purpose of more livable urban communities in the era of intelligent and sustainable cities.

Historic gardens, regarded as a significant genre of cultural heritage, encapsulate the enduring essence of bygone eras while concurrently transcending temporal boundaries to resonate with the present and future. These gardens provide us vitality and inspiration, holding a collective repository of human memory and serving as a testament to our shared heritage. However, like landscapes, gardens constantly change through natural processes and human interventions. How can we preserve these gardens, though changes are unavoidable? Spatial and visual characteristics are the gardens' essential characteristics, and point-cloud (LiDAR) technologies are powerful tools to reveal and analyze gardens’ spatial-visual relationships and characteristics. Therefore, this paper aims to present a point-cloud-based approach to identifying spatial-visual design principles and making them operational to protect and develop historic gardens. Additionally, several methods have been proposed in this research, including (a) a voxel-based method to transfer points into a solid model for GIS-based computation, (b) a novel method to analyze the field of view (FOV), and (c) a systemic framework to reveal historic gardens’ spatial-visual characteristics based on the voxelized model. Jichang Garden, a historic garden in Wuxi, China, known for its visual design and spatial arrangement, has been selected as a case study to showcase how to apply the methods proposed by this paper. The findings include the design principles for the water body, the arrangement for a route, and the planting strategies of the garden. The conservational strategies have been formed based on the findings, and the appliable potentials and limitations of the methods have also been discussed.

[Objective] Human perception of landscape and environment is primarily through visual senses, making visual landscape research a central theme in landscape architecture research. Conducting a visual analysis of the spatial characteristics of traditional Chinese gardens and exploring their visual features can provide valuable guidance for inheriting the essence of spatial design. In recent years, many scholars have employed quantitative analytical methods to identify the visual space of traditional gardens. However, few of these studies have used detailed models to analyze the spatial features of traditional garden spaces. Compared to traditional digital models, point cloud models collected through LiDAR (light detection and ranging) offer more detailed spatial information for visual landscape research. Therefore, this research aims to take Jichang Garden in Wuxi as an example to explore the possibilities and applications of in-depth visual and spatial analysis of traditional Chinese gardens using point cloud technology. This aim can be further broken into several components: 1) Establishing analytical methods and selecting analysis indicators; 2) applying these methods and indicators to analyze the visual space of Jichang Garden; 3) uncovering the characteristics and features of traditional Chinese gardens through the interpretation of analysis results. [Method] This research establishes a set of methods for quantifying the analysis of visual space using point cloud data, including three main steps. 1) Establishment of a model based on point cloud for calculating the analysis results of visual space. In detail, this research applies a voxel-based method to build multidimensional digital models for buildings, vegetation, and rockery, and build a digital elevation model (DEM) based on the point cloud data on the ground. Then, the aforesaid two kinds of models are integrated with water surface to finalize the modeling process. 2) Establishment of a visual analysis method (line of sight method) based on the three-dimensional digital model for obtaining visual space information. 3) Proposal of indicators for evaluating visual landscape utilizing the visual space information obtained, including “3D visibility”, “visual spread” and “feature ratio of visual field” indicators. A total of 11 viewpoints (viewpoints 1, 2, 3 ... 11) on the west side of Jinhuiyi Pond are analyzed using the above methods. [Results] This research’ s main findings consist of three parts. 1) 3D visibility calculation results. From the visibility analysis results of 11 viewpoints, it is evident that viewpoints 1, 5, 6, 7, and 10 have a relatively broad visual field, with some line of sight reaching a length of 100 meters. 2) Evaluation results of visual space indicators. The viewpoint with the highest 3D visibility is labeled as viewpoint 1, located at the Xianyuexie water viewing platform, while the one with the minimum 3D visibility is labeled as viewpoint 2. The viewpoint with the highest “visual spread” is viewpoint 10, situated in the plaza at the south of the Jiashutang building, while the one with the minimum “visual spread” is viewpoint 11. Except for viewpoint 1 (dominated by buildings) and viewpoint 10 (dominated by the ground), the highest “feature ratio of visual field” is occupied by vegetation. 3) Interpretation of the calculation results. […]