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dc.contributor.authorHan, Wen
dc.contributor.authorCao, Lei
dc.contributor.authorXu, Sheng
dc.date2022-09
dc.date.accessioned2022-10-19T13:29:33Z
dc.date.available2022-10-19T13:29:33Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/13676
dc.description.abstractThe 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.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.urihttps://ijimai.org/journal/bibcite/reference/3141es_ES
dc.rightsopenAccesses_ES
dc.subjectecological environment indexes_ES
dc.subjectobject detectiones_ES
dc.subjectremote sensing imagees_ES
dc.subjectstreet trees coverage ratioes_ES
dc.subjectnetworkes_ES
dc.subjectYOLOv4es_ES
dc.subjectIJIMAIes_ES
dc.titleA Method of the Coverage Ratio of Street Trees Based on Deep Learninges_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2022.07.003


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