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Article

Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images

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The Institute for Sustainable Industries and Liveable Cities (ISILC), Victoria University, Melbourne, VIC 8001, Australia
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School of Computing and Mathematics, Charles Sturt University, Port Macquarie, NSW 2444, Australia
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Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Karachi 75600, Sindh, Pakistan
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Applied Ecology Research Group, Victoria University, Melbourne, VIC 8001, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Francois Girard
Remote Sens. 2021, 13(11), 2194; https://doi.org/10.3390/rs13112194
Received: 22 April 2021 / Revised: 28 May 2021 / Accepted: 28 May 2021 / Published: 4 June 2021
(This article belongs to the Section AI Remote Sensing)
Urban greenery is an essential characteristic of the urban ecosystem, which offers various advantages, such as improved air quality, human health facilities, storm-water run-off control, carbon reduction, and an increase in property values. Therefore, identification and continuous monitoring of the vegetation (trees) is of vital importance for our urban lifestyle. This paper proposes a deep learning-based network, Siamese convolutional neural network (SCNN), combined with a modified brute-force-based line-of-bearing (LOB) algorithm that evaluates the health of Eucalyptus trees as healthy or unhealthy and identifies their geolocation in real time from Google Street View (GSV) and ground truth images. Our dataset represents Eucalyptus trees’ various details from multiple viewpoints, scales and different shapes to texture. The experiments were carried out in the Wyndham city council area in the state of Victoria, Australia. Our approach obtained an average accuracy of 93.2% in identifying healthy and unhealthy trees after training on around 4500 images and testing on 500 images. This study helps in identifying the Eucalyptus tree with health issues or dead trees in an automated way that can facilitate urban green management and assist the local council to make decisions about plantation and improvements in looking after trees. Overall, this study shows that even in a complex background, most healthy and unhealthy Eucalyptus trees can be detected by our deep learning algorithm in real time. View Full-Text
Keywords: Eucalyptus trees; vegetation health; Siamese network; CNN; deep learning; Google Street View (GSV); target object detection; image processing; geolocation; line-of-bearing (LOB) Eucalyptus trees; vegetation health; Siamese network; CNN; deep learning; Google Street View (GSV); target object detection; image processing; geolocation; line-of-bearing (LOB)
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MDPI and ACS Style

Khan, A.; Asim, W.; Ulhaq, A.; Ghazi, B.; Robinson, R.W. Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images. Remote Sens. 2021, 13, 2194. https://doi.org/10.3390/rs13112194

AMA Style

Khan A, Asim W, Ulhaq A, Ghazi B, Robinson RW. Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images. Remote Sensing. 2021; 13(11):2194. https://doi.org/10.3390/rs13112194

Chicago/Turabian Style

Khan, Asim, Warda Asim, Anwaar Ulhaq, Bilal Ghazi, and Randall W. Robinson. 2021. "Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images" Remote Sensing 13, no. 11: 2194. https://doi.org/10.3390/rs13112194

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