Establishing a citywide street tree inventory with street view images and computer vision techniques

Trees in urban areas have diverse ecological, social, and health benefits. The establishment of up-to-date and accurate street-tree inventories that list the species and locations of individual street trees is critical to urban tree management and tree-planting campaigns. However, street-tree inventories are incomplete or lacking altogether in most cities. This is partly because conventional field assessment is laborious or expensive. In this study, we developed and validated a novel and cost-effective method to establish a city-wide tree inventory based on computer vision and freely available street view images (SVIs). Tree information such as species, height, crown diameter, and geographical coordinates at the individual tree level can be assessed. Based on an object detection model, we adopted a species-based loss function to address the challenges of long-tailed class distribution of species, which is caused by imbalance among sample size of different tree species and can lead to poor performance of the model. Compared with other research in urban tree species recognition, the modified model shows a higher accuracy. In order to calculate quantitative features of street trees, we employed a deep learning algorithm, which is pretrained on stereo dataset and validated on Google Street View images, to estimate the depth of each pixel in SVIs. Furthermore, as a demonstration, we established the citywide tree inventory and conducted tree diversity analysis for Jinan, China. Compared with new developed area, the old town has more street trees and more diverse tree species which can improve biodiversity and walkability. We also found that plane trees, which can cause allergic reactions, are dominant in northern new developed urban area.