Accurate and Efficient Urban Street Tree Inventory with Deep Learning on
  Mobile Phone Imagery
Asim Khan Umair Nawaz Anwaar Ulhaq Iqbal Gondal Sajid Javed
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.