01659nas a2200253   4500000000100000000000100001008004100002260001200043653003300055653002100088653002500109653003200134653001200166653001000178100001200188700001200200700001300212245007400225856008000299300001000379490000600389520099600395022001401391       2022                            d  c09/202210aEcological Environment Index10aObject Detection10aRemote Sensing Image10aStreet Trees Coverage Ratio10aNetwork10aYOLOv1 aWen Han1 aLei Cao1 aSheng Xu00aA Method of the Coverage Ratio of Street Trees Based on Deep Learning  uhttps://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_3.pdf  a23-290 v73 aThe 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.  a1989-1660