Abstract
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.
Original language | English |
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Article number | 2194 |
Pages (from-to) | 1-24 |
Number of pages | 24 |
Journal | Remote Sensing |
Volume | 13 |
Issue number | 11 |
DOIs | |
Publication status | Published - 01 Jun 2021 |