Objective With the rapid development of urban information models, 3D virtual scenes, and digital twin cities, the methods and standards for city information modeling have been widely adopted. Traditional spatial data storage and exchange methods are often based on two-dimensional drawings and documents, which cannot meet the current practice demands. Urban green spaces are a vital component of city infrastructure, providing various ecosystem services. To effectively digitalize the process of urban green space planning, design, and management, it is essential to establish spatial information models that can accurately reflect the characteristics of these spaces. Additionally, a monitoring and feedback mechanism based on digital twin models of green spaces should also be established. As a living organism, urban vegetation has unique branching structures and growth processes that differentiate it from conventional grey infrastructure. Therefore, traditional component-based information models cannot meet the modeling requirements of green spaces, entailing further investigation and enhancement.
Methods Green space information modeling includes unique characteristics in relational data construction, which will need specific configurations in terms of level of detail (LOD) division and model construction. Additionally, due to the growth and changing processes of plants, corresponding dynamic simulation algorithms need to be established. By summarizing and analyzing the green space-related components of existing digital information model standards both domestically and internationally, this research explores different expression methods suitable for green space information modeling by focusing on three aspects: model composition, data relationship, and analytical evaluation applications. To meet the practical needs of urban green space planning and management, this research conducts digital simulations of the geometric forms and attribute information of green space plants, and proposes directions for further improvement of green space information models, including the construction of modeling standards, the development of sharing platforms, and the research on generative algorithms.
Results The structures and characteristics of green space plants differ from those of other urban components, and their digital representation methods need to be explored based on the spatial morphological features of the vegetation in green spaces. Green space information models consist of two main components: spatial geometric models and attribute association models. Spatial geometric models are designed to express plants’ spatial geometric characteristics, including tree branching and leaf structures. Common representations of spatial geometric models include two-dimensional models, simple geometric models, triangular mesh models, voxel models, and generative models. Attribute association models are used to express the physiological and ecological characteristics of plants and the relationships between different indicators. This requires the expansion of existing model attributes to construct a corresponding attribute and association system. Moreover, the collection and application standards for vegetation attribute information remain inadequate. Regarding relational data construction, green spaces differ from other elements in urban settings, and their information models possess unique characteristics, entailing specific configurations in terms of LOD division and model construction. Still, due to the dynamic growth and change characteristics of plants, it is also essential to establish corresponding dynamic simulation algorithms. Current LOD models are typically designed for buildings and grey infrastructure, and their expression and description of plants tend to be simplistic and lack specificity, entailing further refinement and enhancement. Algorithms for constructing three-dimensional plant models include L-system algorithms and various neural networks, which can assist in generating 3D models. Simulations of plant growth processes encompass growth changes, seasonal variations, and environmental interactions. In terms of assessment and analysis, given the increasingly complex development trends of urban fabric, traditional two-dimensional assessment methods cannot meet the needs of complex green space evaluation. By establishing evaluation methods, green space information models can be applied to different practical needs: Calculation of three-dimensional green volume indicators, analysis of three-dimensional landscape patterns, and evaluation of three-dimensional spatial ecosystem services.
Conclusion Based on the requirements for green space information modeling, a corresponding construction framework can be established, including data acquisition, composition structure, detail level, spatial scale, sharing platform and analysis application of green space information models. Current urban green space information models require further development and refinement in research practices, which could focus on three approaches: The standardization of modeling process, the development of sharing platforms, and the application of generative algorithms. Regarding modeling standard construction, it is essential to determine data characteristics of green space information models at different scales and stages based on the project requirements of various green space planning, design, and management needs. This includes clarifying data collection and exchange methods for corresponding LOD, thereby achieving standardization and normalization throughout the entire process of green space information modeling. In terms of sharing platform development, it is necessary to establish a case study database of green space information models at different scales, as well as a database of plant components and a simulation analysis platform to support the research and practice of urban green space planning. Generative algorithms should also be implemented to reduce manual processing efforts and enhance the efficiency and accuracy of digital information modeling.
DownLoad: