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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2022
    • vol. 7, nº 5, september 2022
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2022
    • vol. 7, nº 5, september 2022
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    A Method of the Coverage Ratio of Street Trees Based on Deep Learning

    Autor: 
    Han, Wen
    ;
    Cao, Lei
    ;
    Xu, Sheng
    Fecha: 
    09/2022
    Palabra clave: 
    ecological environment index; object detection; remote sensing image; street trees coverage ratio; network; YOLOv4; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/13676
    DOI: 
    https://doi.org/10.9781/ijimai.2022.07.003
    Dirección web: 
    https://ijimai.org/journal/bibcite/reference/3141
    Open Access
    Resumen:
    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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