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

Autor: Jie Xuan, Xuejian Li, Huaqiang Du, Guomo Zhou, Fangjie Mao, Jingyi Wang, Bo Zhang, Yulin Gong, Di’en Zhu, Lv Zhou, Zihao Huang, Cenheng Xu, Jinjin Chen, Yongxia Zhou, Chao Chen, Cheng Tan, Jiaqian Sun
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Remote Sensing, Vol 15, Iss 1, p 97 (2022)
Druh dokumentu: article
ISSN: 2072-4292
DOI: 10.3390/rs15010097
Popis: 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.
Databáze: Directory of Open Access Journals
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