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Deep Learning based Automated Forest Health Diagnosis from Aerial Images

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Deep Learning based Automated Forest Health Diagnosis from Aerial Images. / Chiang, Chia-yen; Angelov, Plamen; Barnes, Chloe; Jiang, Richard.

In: IEEE Access, Vol. 8, 28.07.2020, p. 144064 - 144076.

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Chiang, Chia-yen ; Angelov, Plamen ; Barnes, Chloe ; Jiang, Richard. / Deep Learning based Automated Forest Health Diagnosis from Aerial Images. In: IEEE Access. 2020 ; Vol. 8. pp. 144064 - 144076.

Bibtex

@article{e53820607bdd493db519bb02a650a4da,
title = "Deep Learning based Automated Forest Health Diagnosis from Aerial Images",
abstract = "Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early detection of dead trees and living trees. In this paper, we applied a synthetic method to enlarge imagery dataset and present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN (Mask Region-based Convolutional Neural Network) approach, with a transfer learning scheme. We apply our framework to our aerial imagery datasets,and compare eight fine-tuned models. The mean average precision score (mAP) for the best of these models reaches 54\%. Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire.",
author = "Chia-yen Chiang and Plamen Angelov and Chloe Barnes and Richard Jiang",
year = "2020",
month = jul,
day = "28",
doi = "10.1109/ACCESS.2020.3012417",
language = "English",
volume = "8",
pages = "144064 -- 144076",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Deep Learning based Automated Forest Health Diagnosis from Aerial Images

AU - Chiang, Chia-yen

AU - Angelov, Plamen

AU - Barnes, Chloe

AU - Jiang, Richard

PY - 2020/7/28

Y1 - 2020/7/28

N2 - Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early detection of dead trees and living trees. In this paper, we applied a synthetic method to enlarge imagery dataset and present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN (Mask Region-based Convolutional Neural Network) approach, with a transfer learning scheme. We apply our framework to our aerial imagery datasets,and compare eight fine-tuned models. The mean average precision score (mAP) for the best of these models reaches 54\%. Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire.

AB - Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early detection of dead trees and living trees. In this paper, we applied a synthetic method to enlarge imagery dataset and present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN (Mask Region-based Convolutional Neural Network) approach, with a transfer learning scheme. We apply our framework to our aerial imagery datasets,and compare eight fine-tuned models. The mean average precision score (mAP) for the best of these models reaches 54\%. Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire.

U2 - 10.1109/ACCESS.2020.3012417

DO - 10.1109/ACCESS.2020.3012417

M3 - Journal article

VL - 8

SP - 144064

EP - 144076

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -