journal article

Journal Article

Text

Landscape Ecology. 2025, 40, p. 125

Multi-temporal analysis of urban vegetation using deep learning and 3D reconstruction

Springer Science and Business Media LLC

English

Text

2025-07-01

https://doi.org/10.1007/s10980-025-02090-4

The version of record of this article, first published in Landscape Ecology, is available online at Publisher’s website: https://doi.org/10.1007/s10980-025-02090-4.

Context: Urban green spaces play a vital role in enhancing environmental quality and human well-being. However, traditional assessment methods, such as the green view index, primarily quantify green coverage while neglecting vegetation diversity, color richness, and seasonal dynamics, which are critical for urban livability. Objectives: This study develops a multi-temporal and multi-perspective analysis framework for urban green space visualization, introducing the Seasonal Species-Specific Plant View Index (S3PVI) to quantify plant coverage at the species level, capturing seasonal changes and visual diversity. Methods: The framework integrates computer vision, deep learning, and 3D reconstruction technologies, including structure from motion and 3D Gaussian splatting. To validate the S3PVI, case studies were conducted in Suita City, Japan, analyzing real-world seasonal vegetation patterns and testing the framework in a virtual park environment to assess its applicability in urban design. Results: The S3PVI effectively captured species-specific seasonal patterns, with cherry blossoms peaking at 45.61% visibility in spring and maples at 56.78% in autumn. Comparative analysis revealed distinctive vegetation strategies between streets, with Sanshikisaido showing higher seasonal amplitude but lower consistency than Nakayoshido. Virtual simulations confirmed that multi-species schemes optimally balanced seasonal impact with year-round visual stability. Conclusions: The S3PVI framework advances urban vegetation assessment by providing species-specific and seasonally dynamic visual data, supporting evidence-based urban planning for ecological sustainability and livability. Potential applications include brownfield redevelopment, virtual park planning, and urban design simulations.

This article is licensed under a Creative Commons Attribution 4.0 International License.

102258

Multi-temporal analysis of urban vegetation using deep learning and 3D reconstruction

Springer Science and Business Media LLC

English

2025-07-01

Landscape Ecology

40

125

https://doi.org/10.1007/s10980-025-02090-4

https://hdl.handle.net/11094/102258