Multimodal Large-Model-Driven Method for Urban 3D Green-Space Calculation and Low-Carbon Green-Space Management

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Against the background of global carbon neutrality goals and urban ecological transformation, traditional two-dimensional green-space indicators cannot fully reflect spatial–ecological benefits, and conventional 3D green-space calculation and landscape maintenance methods suffer from low accuracy and low efficiency. This paper proposes a theoretical technical framework combining multimodal remote sensing data, LiDAR point clouds and domain large language models for urban green-space scenarios. Firstly, a multimodal feature fusion strategy is constructed to adapt to complex urban environments, and a hierarchical calculation workflow for individual trees and regional 3D green-space is designed. Secondly, a full-life-cycle carbon accounting system for landscape engineering is established, which clarifies the accounting boundaries and quantitative methods of carbon sources and carbon sinks. Thirdly, a human–computer collaborative decision framework is proposed to realise intelligent recommendations for low-carbon maintenance schemes. This study focuses on theoretical framework construction and method exploration, and verifies the technical feasibility through simulation analysis. Different from existing independent research on green-space calculation or carbon accounting, this work integrates the two tasks and innovates a multimodal model application mode for landscape industry. This study focuses on theoretical framework construction and methodological exploration; feasibility assessment is only carried out via simulation deduction, rather than real-world field validation.