###### Abstract
These studies suggest the potential for using vegetation indices, but they use them as a means of comparing vegetation indices and studying their spatial distribution rather than their detection accuracy. In addition, in the case of time series images, the focus is on improving classification accuracy through seasonal differences, and no studies have been conducted on the accuracy of training images by tracking damaged trees. The studies on PWD that apply DL are as follows. [PERSON] et al. [19] obtained multispectral images to analyze and compare the target detection and machine learning algorithms. In target detection, two algorithms, Faster R-convolutional neural network (CNN) and YOLOv4, were used. In machine learning, two algorithms, namely Random Forest and Support Vector Machine, were used. The analysis indicated that the accuracy was higher for the machine learning algorithms although direct identification of images from objects could not be performed. [PERSON] and [PERSON] [17] applied SegNet as a semantic segmentation algorithm and YOLOv2 as an object classification algorithm for automatic detection of PWDT data. The result showed approximately 57% accuracy for SegNet and 77% for YOLOv2. [PERSON] et al. [20] evaluated the accuracy using semantic segmentation, an object detection model, to compensate for the weakness that the object recognition model is effective for object recognition but does not reflect the location, shape, and area of the object. The models used were SegNet, FCN, U-Net, and DeepLab3, and the average classification accuracy of each model was about 86.9%, with a maximum classification accuracy of about 90%. It can be seen that the object detection model has a similar level of classification accuracy compared to the existing object recognition model. [PERSON] et al. [21] trained and assessed a diversity of DL models including fully convolutional networks (FCN), U-Net, DeepLabv3+, and SegNet, for the pine tree forests in the Laoshan district, Tsingtao, China. The detection accuracy of DeepLabv3+, a type of semantic segmentation, was shown to be the highest. [PERSON] [18] applied the semantic segmentation technique to UAV and satellite images, and proposed a system to prevent the spread of PWD that can differentiate and visualize the infected, suspected, and healthy trees. Semantic segmentation has been used in the detection of objects with atypical shapes. [PERSON] et al. [22] proposed the U-Net algorithm, which showed outstanding performance even with a small amount of learning data based on data augmentation. [PERSON] et al. [14] composed a patch-based classifier with the ResNet18, an image segmentation algorithm, as the backbone; five-band multispectral images were formed in classification and heat map formats, and to solve the problem of insufficient learning data, geometric transformation and displacement were used to perform data augmentation. The findings of previous studies can be summarized as follows. First, most of the existing studies utilize single-period images, which do not reflect the early, mid, and late characteristics of damaged trees over time. Second, there is a lack of research on detection accuracy in mixed forests or other land cover areas other than pine forests. In particular, it is necessary to study the detection accuracy of algorithms with regard to fall foliage or browning of broadleaf trees. Third, the atypical and highly varied ecological characteristics of PWDT are generally not reflected in the object classification. Fourth, object detection models are more suitable than object recognition models for images that contain a variety of information, such as high-resolution aerial photographs and UAV images [23]. Fifth, the need to ensure the reliability of the built training data is not clearly discussed, and most of the pilot studies are based on a small part of the research forest. Therefore, an in-depth discussion on the accuracy of detection of damaged trees by algorithms according to various ecological and natural environmental characteristics is needed.