Individual Street Tree Detection and Vitality Assessment Using GeoAI and Multi-Source Imagery

Abstract

Highlights: What are the main findings? A GeoAI-based framework was developed to delineate individual street tree crowns from high-resolution aerial imagery and to derive a remote sensing-based vitality proxy by integrating NDVI, NDRE, and NDMI at the individual tree level. The analysis revealed spatially varying vitality patterns across urban environments. Trees along major road corridors generally showed lower spectral vitality signals, while those near parks, riverfront walkways, and recently developed residential areas tended to exhibit higher values. NDMI captured moisture-related variation that was not fully reflected by chlorophyll-related indices. What are the implication of the main findings? The proposed framework provides a scalable and repeatable approach for screening and monitoring street tree vitality over large urban areas, complementing traditional field-based inspections rather than replacing them. By highlighting areas with clusters of low-vitality tree signals, the method offers a practical decision-support tool for prioritizing field diagnosis and informing data-driven urban green-infrastructure management within smart-city planning contexts. Urban street trees provide essential environmental and social benefits, yet their vitality is often challenged by adverse urban conditions such as traffic emissions, impervious surfaces, and limited soil moisture. Conventional street tree management relies heavily on labor-intensive field inspections, making large-scale and repeatable assessment difficult. To address this limitation, this study proposes a GeoAI-based framework that integrates high-resolution aerial imagery, multispectral satellite data, and deep learning–based semantic segmentation to automatically delineate individual street trees and derive a remote sensing-based vitality proxy. Street trees are detected from orthorectified aerial imagery using semantic segmentation models, and vegetation indices—including NDVI, NDRE, and NDMI—are extracted from multispectral satellite imagery. An area-weighted object–pixel matching strategy is applied to associate spectral indicators with individual crowns across multi-resolution datasets. A composite vitality proxy is then constructed by integrating chlorophyll- and moisture-related indices. The results reveal spatial variability in spectral vitality signals across different urban environments. Trees along major road corridors tended to exhibit lower chlorophyll- and moisture-related indicators, while those near parks, riverfront walkways, and recently developed residential areas generally showed higher values. NDMI provided complementary insights into moisture-related stress that were not fully reflected by chlorophyll-based indices. Although the proposed vitality proxy is not a substitute for field-based diagnosis, the overall framework offers a scalable approach for citywide screening and prioritization of potentially stressed trees, supporting data-informed urban green infrastructure management within smart-city planning contexts. © 2026 by the authors.