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Deforestation, a major contributor to climate change, poses detrimentalconsequences such as agricultural sector disruption, global warming, flashfloods, and landslides. Conventional approaches to urban street tree inventorysuffer from inaccuracies and necessitate specialised equipment. To overcomethese challenges, this paper proposes an innovative method that leverages deeplearning techniques and mobile phone imaging for urban street tree inventory.Our approach utilises a pair of images captured by smartphone cameras toaccurately 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, andapplicability in hard-to-reach areas. We evaluated our method on acomprehensive dataset of 400 trees and achieved a DBH estimation accuracy withan error rate of less than 2.5%. Our method holds significant potential forsubstantially improving forest management practices. By enhancing the accuracyand efficiency of tree inventory, our model empowers urban management tomitigate the adverse effects of deforestation and climate change.

Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery