A Method of the Coverage Ratio of Street Trees Based on Deep Learning.

Wen Han; Lei Cao; Sheng Xu

doi:10.9781/ijimai.2022.07.003

Authors

Wen Han Nanjing Forestry University
Lei Cao Nanjing Forestry University
Sheng Xu Nanjing Forestry University

DOI:

https://doi.org/10.9781/ijimai.2022.07.003

Keywords:

Ecological Environment Index, Object Detection, Remote Sensing Image, Street Trees Coverage Ratio, Network, YOLOv

Supporting Agencies

This work was supported in part by the National Natural Science Foundation of China (Grant No. 62102184), in part by the Natural Science Foundation of Jiangsu Province (Grant No. BK20200784), in part by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (Grant No. 19KJB520010), in part by China Postdoctoral Science Foundation (Grant No. 2019M661852) and in party by the National Key Research and Development Program of China (2019YFD1100404).

Abstract

The street trees coverage ratio provides reliable data support for urban ecological environment assessment, which plays an important part in the ecological environment index calculation. Aiming at the statistical estimation of urban street trees coverage ratio, an integrated model based on YOLOv4 and Unet network for detecting and extracting street trees from remote sensing images is proposed, and obtain the estimated street trees coverage ratio in images accurately. The experiments are carried out under self-made dataset, and the results show that the accuracy of street trees detection is 94.91%, and the street trees coverage ratio is 16.30% and 13.81% in the two experimental urban scenes. The MIoU of contour extraction is 98.25%, and the estimated coverage accuracy is improved by 6.89% and 5.79%, respectively. The result indicates that the proposed model achieves the automation of contour extraction of street trees and more accurate estimation of street trees coverage ratio.

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