Objective The core objective of Building Information Modeling (BIM) development is interoperable information sharing. However, existing BIM data schemas are mainly oriented toward building assets and are not sufficiently adapted to the professional characteristics and engineering requirements of landscape architecture, especially in the representation of plant elements in China’s landscape engineering. To remedy the absence of a domain-ready data schema in the landscape field, this study takes plants—the core ecological elements of landscape systems—as its object, and develops an application framework from two tightly coupled dimensions: 1) the construction and platform internalization of a standardized plant dataset, and 2) the practical use of the Industry Foundation Classes (IFC) to achieve cross-platform, semantics-preserving exchange of plant objects for plant objects in landscape engineering. Beyond addressing a modeling gap, the objective is to articulate clear, computable semantics for living vegetation that capture species identity, spatial morphology, ecological habit, and lifecycle management needs in ways existing building-focused schemas do not. The study aims to clarify the scope and granularity of plant information required for design coordination and long-term stewardship, reduce ambiguity in terminology and units across stakeholders, and establish a consistent basis for ecological asset representation within broader urban digital ecosystems toward digital twins and city information models.

Methods Plant instances were first digitally defined through literature review, project investigation, and analysis of landscape engineering workflows. A standardized plant data checklist was established, consisting of 2 categories, 7 subcategories, and 25 core attribute fields. These fields cover identification, geometric dimensions, ecological habits, ecological functions, procurement information, construction information, and operation and maintenance information. The dataset was embedded into the Revit software platform so that field identifiers, value types and units, and value-domain constraints could be enforced at the point of modeling, enabling direct authoring and downstream validation. On this basis, a user-defined attribute mapping method was adopted to carry plant semantics in IFC without altering existing entity boundaries. The procedure comprised four steps: attribute curation, mapping-rule authoring, configuration-based export, and post-export verification. In practice, Revit parameters were aligned one-to-one with IFC properties and quantities via an external configuration file that records, for each field, the target IFC property name and value type (with units) together with its source parameter. This design ensures structural conversion of plant parameters into IFC while maintaining semantic fidelity across tools. Consistency checks were conducted before and after export to verify presence, type, unit correctness, value-domain validity, and equivalence between authoring and exchange layers.

Results The proposed method was applied to the Qianwan Park Phase I landscape project in Minhang District, Shanghai, with a project area of approximately 100,000 m². The application enabled the extension and export of plant attributes from the native Revit model to IFC, while retaining the selected plant semantic information. With the standardized dataset internalized, the platform directly drove modeling and subsequent checking, and the exported IFC files provided structured storage of the 25 key plant attributes for representative plant species. In the viewer-based inspection workflow, attribute panels for representative species displayed identification information such as Chinese name, botanical name, family, and genus; geometric information such as height, crown spread, DBH, rootball depth, and rootball diameter; ecological information such as climate adaptability, soil requirements, and light requirements; and management information such as procurement items, planting date, and maintenance notes. Cross-species spot checks found no missing items, misplacements, or unit inconsistencies, as evidenced by semantic equivalence among the family parameter panels, the IFC property sets, and the viewer displays. Beyond technical fidelity, the normalized fields enabled immediate downstream use: species lists could be compiled programmatically; maintenance guidance could be filtered and scheduled from O&M attributes; and carbon-related estimates could use the geometric and ecological parameters as inputs with minimal manual preprocessing. The end-to-end validation artifacts further supported auditability and reproducible deliveries across design−construction−operations handoffs.

Conclusion The findings enhance the digital expression of plant objects and strengthen the standardized, computable attributes of landscape ecological assets, thereby providing technical support for ecological, data-driven management of landscape projects. Concretely, the study demonstrates that a dictionary-led standardized dataset (2 classes, 7 subcategories, 25 fields) can be internalized in mainstream BIM authoring to unify semantics at source; and that a configuration-based IFC application method—maintaining current schema entities while conveying rich semantics through property/quantity groupings—can deliver cross-platform, traceable and verifiable plant information without modifying the IFC entity landscape. In a real-world park project with 35 tree species, the approach produced structured datasets that remained semantically equivalent across authoring, exchange, and viewing environments, and proved directly useful for species registers, construction communication, O&M planning, and carbon-awareness workflows. Strategically, this positions plants as first-class ecological assets within Landscape Information Modeling (LIM), enabling lossless information exchange across the lifecycle and facilitating integration with digital twins and city information modeling ecosystems. Future work will extend the dictionary and mapping pattern to additional landscape elements—terrain, pavements, water bodies, small structures, and site furnishings—under a unified, compatible data architecture; establish case repositories and sharing platforms to consolidate best practices; and explore generative algorithms to reduce manual authoring while better representing plant growth dynamics and seasonal change.