基于深度学习与手机图像的准确高效城市行道树清查
Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery
AI总结：
本文提出一种利用深度学习和手机成像进行城市行道树清查的方法，通过一对手机图像分割树干并计算胸径，在400棵树的数据集上误差率低于2.5%，显著提升了清查准确性与效率。
摘要：
森林砍伐是导致气候变化的主要因素，会带来农业部门中断、全球变暖、山洪暴发和山体滑坡等不利后果。传统的城市行道树清查方法存在不准确的问题，且需要专用设备。为克服这些挑战，本文提出了一种创新方法，利用深度学习技术和手机成像进行城市行道树清查。该方法利用智能手机摄像头拍摄的一对图像，准确分割树干并计算胸径（DBH）。与传统方法相比，该方法具有多项优势，包括更高的准确性、减少对专用设备的依赖，以及适用于难以到达的区域。我们在包含400棵树的综合数据集上评估了该方法，实现了DBH估计准确率误差率低于2.5%。该方法在大幅改善森林管理实践方面具有巨大潜力。通过提高树木清查的准确性和效率，我们的模型使城市管理部门能够减轻森林砍伐和气候变化的不利影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
