Intelligent Estimating the Tree Height in Urban Forests Based on Deep Learning Combined with a Smartphone and a Comparison with UAV-LiDAR

In this paper, a method for extracting the height of urban forest trees based on a smartphone was proposed to efficiently and accurately determine tree heights. First, a smartphone was used to obtain person-tree images, LabelImg was used to label the images, and a dataset was constructed. Secondly, based on a deep learning method called You Only Look Once v5 (YOLOv5) and the small-hole imaging and scale principles, a person-tree scale height measurement model was constructed. This approach supports recognition and mark functions based on the characteristics of a person and a tree in a single image. Finally, tree height measurements were obtained. By using this method, the heights of three species in the validation set were extracted; the range of the absolute error was 0.02 m-0.98 m, and the range of the relative error was 0.20-10.33%, with the RMSE below 0.43 m, the rRMSE below 4.96%, and the R2 above 0.93. The person-tree scale height measurement model proposed in this paper greatly improves the efficiency of tree height measurement while ensuring sufficient accuracy and provides a new method for the dynamic monitoring and investigation of urban forest resources.