Calibrated general-purpose smartphone augmented reality for urban street-tree structural data acquisition

This is the authors' abstract. We don't add key points for this paper.

Urban street trees are key to urban climate regulation, carbon storage and habitat provision, yet many cities still lack fine-scale, up-to-date field data on their structure and site conditions. Conventional tape and laser measurements are accurate but slow, labour-intensive and often impractical along narrow, obstacle-rich sidewalks. This study develops and tests a smartphone-based augmented-reality (AR) framework for measuring three ecologically critical geometric parameters of street trees—diameter at breast height (DBH), planting-pit area and tree spacing. Using general-purpose AR ruler apps on three consumer smartphones, we implemented a standardized field protocol and a generic geometric-plus-statistical calibration workflow that converts basal rectangular measurements into calibrated DBH. Field validation along a 1.5-km street corridor in Guangzhou, China (125 trees) shows that, after calibration, mean absolute percentage errors are 6–8% for DBH and below 3% for planting-pit area and spacing, satisfying Class B precision under the Chinese Regulations on Main Technical Specifications for Forestry Professional Surveys . AR workflows reduced survey time by 35–40% and required only one operator. The framework thus provides an operationally efficient, low-cost and transferable pathway to generate standardized structural data for urban-forest inventories and ecological-informatics applications. • Evaluates general-purpose smartphone AR apps for DBH, planting-pit area and tree spacing in dense urban streetscapes. • Develops an open geometric–statistical calibration workflow converting basal AR rectangles into calibrated DBH under forestry precision standards. • Using 125 street trees, shows calibrated AR achieves ≈6–8% DBH error and < 3% planar error (Class B). • Demonstrates 35–40% reductions in field time and single-operator capability compared with tape-based measurements. • Positions calibrated smartphone AR as a scalable, low-cost source of structural inputs for urban ecological informatics.