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.

翻译：森林砍伐是导致气候变化的主要因素之一，会带来农业部门中断、全球变暖、山洪暴发和滑坡等破坏性后果。传统的城市街道树木清查方法存在精度不足且需要专业设备的问题。为克服这些挑战，本文提出了一种创新方法，利用深度学习技术与手机成像进行城市街道树木清查。我们的方法利用智能手机相机拍摄的一对图像，精确分割树干并计算胸径（DBH）。与传统方法相比，该方法具有精度高、对专业设备依赖度低、适用于难以到达区域等优势。我们在包含400棵树的综合数据集上进行了评估，DBH估算误差率低于2.5%。该方法在显著改善森林管理实践方面具有巨大潜力。通过提升树木清查的准确性和效率，我们的模型使城市管理者能够缓解滥伐森林和气候变化带来的不利影响。