Visual perception of the urban landscape in a city is complex and dynamic, and it is largely influenced by human vision and the dynamic spatial layout of the attractions. In return, landscape visibility not only affects how people interact with the environment but also promotes regional values and urban resilience. The development of visibility has evolved, and the digital landscape visibility analysis method allows urban researchers to redefine visible space and better quantify human perceptions and observations of the landscape space. In this paper, we first reviewed and compared the theoretical results and measurement tools for spatial visual perception and compared the value of the analytical methods and tools for landscape visualization in multiple dimensions on the principal of urban planning (e.g., complex environment, computational scalability, and interactive intervention between computation and built environment). We found that most of the research was examined in a static environment using simple viewpoints, which can hardly explain the actual complexity and dynamic superposition of the landscape perceptual effect in an urban environment. Thus, those methods cannot effectively solve actual urban planning issues. Aiming at this demand, we proposed a workflow optimization and developed a responsive cross-scale and multilandscape object 3D visibility analysis method, forming our analysis model for testing on the study case. By combining the multilandscape batch scanning method with a refined voxel model, it can be adapted for large-scale complex dynamic urban visual problems. As a result, we obtained accurate spatial visibility calculations that can be conducted across scales from the macro to micro, with large external mountain landscapes and small internal open spaces. Our verified approach not only has a good performance in the analysis of complex visibility problems (e.g., we defined the two most influential spatial variables to maintain good street-based landscape visibility) but also the high efficiency of spatial interventions (e.g., where the four recommended interventions were the most valuable), realizing the improvement of intelligent landscape evaluations and interventions for urban spatial quality and resilience.

Much of the research on climate change has focused on carbon reduction in cities or countries. However, more attention needs to be paid to how to achieve carbon neutrality in the urban design and planning stage, and the lack of quantitative analysis of carbon related to urban space makes it difficult to locate urban space and provide direct guidance for urban planning and design. This study proposed three optimization paths to achieve carbon neutrality in multi-scale urban building clusters. Firstly, we reconstructed the quantitative calculation system of urban building communities with the goal of carbon neutrality; secondly, we screened the carbon source reduction and carbon sink interventions that are suitable for multi-scale urban building communities; finally, we constructed a carbon emission and carbon sink calculation system of planning and design schemes based on the layout of relevant elements of planning and design schemes with a grid cell of 100 × 100 m. In practice, there was a gap of about 115,000 tons of CO₂ from the carbon-neutral target and 26% of carbon emission was distributed in the Xiajiabian Station TOD. In this study, nine types of carbon reduction measures were adopted to achieve carbon neutrality in the region, among which the highest carbon reduction was achieved by biomass energy measures, accounting for 29% of the total carbon reduction of 33,745.27 T. The objective of this study is to accurately and quantitatively assess the carbon targets of urban spaces at different scales and adopt effective measures to achieve carbon neutrality.

In the field of urban design, current research has shifted towards resident preference perception and computer-aided design methods that rely on deep learning techniques. In this study, we aimed to provide a quantitative design method for urban space design that could take into account the preferences of different populations. Through empirical research, we collected real urban space and population data, which we then quantified using advanced intelligent recognition tools based on deep learning techniques. Our ensuing analysis illuminated the intricate interplay between constituent elements of urban spaces and the structural and emotional changes of residents. By taking into account the specific driving relationships between each element and residents, we proposed a new evaluation methodology for constructing an intelligent design evaluation model for urban spaces. This intelligent design evaluation model was subsequently used to evaluate the urban space both pre- and post-design. The standard deviation of the difference results demonstrated that the design option (SD value = 0.103) and the desired option for Space 1 were lower than the current option (SD value = 0.129) and the expected scheme. Our findings provide quantitative configuration strategies and program evaluation for urban space design, thus helping designers to design urban spaces that are more popular with residents.