Deforestation, a major contributor to climate change, poses detrimental
consequences such as agricultural sector disruption, global warming, flash
floods, and landslides. Conventional approaches to urban street tree inventory
suffer from inaccuracies and necessitate specialised equipment. To overcome
these challenges, this paper proposes an innovative method that leverages deep
learning techniques and mobile phone imaging for urban street tree inventory.
Our approach utilises a pair of images captured by smartphone cameras to
accurately segment tree trunks and compute the diameter at breast height (DBH).
Compared to traditional methods, our approach exhibits several advantages,
including superior accuracy, reduced dependency on specialised equipment, and
applicability in hard-to-reach areas. We evaluated our method on a
comprehensive dataset of 400 trees and achieved a DBH estimation accuracy with
an error rate of less than 2.5%. Our method holds significant potential for
substantially improving forest management practices. By enhancing the accuracy
and efficiency of tree inventory, our model empowers urban management to
mitigate the adverse effects of deforestation and climate change.

提出利用深度学习技术和手机影像进行城市街道树木清查的创新方法，通过智能手机摄像头捕捉的图像来准确分割树干并计算胸径，具有优越的准确度、对专门设备的依赖较少以及适用于难以到达的地区等多项优点。在包括 400 棵树的广泛数据集上进行评估，得到了胸径估计误差小于 2.5% 的准确度，该方法有望显著提升森林管理实践，并通过提高树木清查的准确性和效率，为城市管理应对森林砍伐和气候变化的不良影响提供支持。