Sustainable development can only be achieved with an innovative improvement from the way we currently analyze, design, build and manage our urban spaces. Current digital analysis and design methods for cities, such as visibility analysis, deeply rely on mapping and modeling techniques. However, most methods fall short of depicting the real visual landscape in the urban realm and this could bring a significant error in visibility calculations which may lead to an improper decision for urban spaces. The technical development of light detection and ranging(LiDAR) technology introduces new approaches for urban study. LiDAR utilizes point clouds including thousands or even millions of georeferenced points, and thus can support 3-D digital representation of urban landscape with detailed information and high resolution. Besides the superiority in representing urban landscape, LiDAR point clouds also has a clear advantage in quantitative analysis and provides better visibility than traditional models. In this paper, we first introduced a novel approach to map visibility in the urban built environment involving vegetation data directly using airborne LiDAR point clouds. This approach calculates neighborhood statistics for occlusion detection. Then we presented 2 case with different scenarios showing how our approach can be used to obtain a precise visibility in an urban area in the Netherlands. At last, we discussed how point clouds based visibility models can be further explored and can better assist urban design.

China has experienced a continuous population increase in urban areas over the last few decades with limited land for construction, which has prompted upward growth in the urban environment. Rapid urbanization has encroached on mountain landscapes and deteriorated the visual landscape in different parts of China. This study aims to investigate and analyze the proper balance between visual landscape protection of urban mountains and vertical urban development using a visibility-based method. An interactive and quantitative method was developed in this research using multiple digital 2D and 3D platforms based on the specification of prohibited spaces for constructive expansion in building height control. A metropolitan area near Mufu Mountain in Nanjing, China, was selected as a case study to implement the proposed method and simulate multiple vertical urban development scenarios. According to the comparison of different scenarios, there is a better building height layout to simultaneously satisfy the requirement of sustaining the Mufu mountain’s visibility and the construction capacity proposed by the documented plan. Two polynomial models were generated to quantitatively investigate the relationship between the protection of mountain landscapes and vertical urban development and served as a reference basis for urban planners to formulate the construction volume control strategy around Mufu Mountain. The proposed method in this study can help planners and urban managers to seek an appropriate approach to control building heights and achieve visual sustainability.

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.

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.