###### Abstract  
The purpose of this study was to enhance the detection accuracy for pine-wilt-diseased trees (PWDT) using time series UAV imagery (TSUI) and deep learning semantic segmentation (DLSS) techniques. The detailed methods to accomplish the research objectives were as follows. Considering the atypical and highly varied ecological characteristics of PWDT, DLSS algorithms of U-Net, SegNet, and DeepLab V3+ (ResNet18 and 50) were adopted. A total of 2350 PWDT were vectorized at 9 sites, and 795 images of 2000 damaged trees were used as training data and 200 images where 350 PWDT were found, were used as the test dataset. The felled trees were tracked and the pest-controlled trees were used as to ground truth the TSUI of at least 2 years to ensure the reliability of the constructed learning data. The results demonstrated that among the evaluated algorithms, DeepLab V3+ (ResNet50) achieved the best f1-score (0.742) and also provided the best recall (0.727). SegNet did not detect any shaded PWDT, but DeepLabV3+ (ResNet50) found most of the PWDT, especially those with atypical shapes near the felled trees. All algorithms except DeepLabV3+ (ResNet50) generated false positives for browned broadleaf trees. For the trees, all algorithms did not detect PWDT that had been dead for a long time and had lost most of their leaves or had turned gray. Most of the older PWDT have been logged, but for the few that remain, the relative lack of training data may be contributing to their poor detection. For land cover, the false positives occurred mainly in bare ground, shaded areas, roads, and rooftops. This study thus verified the potential use of semantic segmentation in the detection of forest diseases such as PWD, while the detection accuracy is anticipated to increase with the acquisition of adequate quantities of learning data in future. p 2023  
PWD was first reported in North America [5], while it is the most severe forest disease in East Asia at present [6]. The disease has also induced serious damages to pine trees in Europe including Portugal [5]. Ecological damage has led to economic damage. In China, hundreds of millions of pine trees were damaged over a 700,000 ha area, and the economic loss was as high as CNY 30 billion [7]. As PWD disperses rapidly, the assessment of the current level of damage is difficult [8; 9]. In addition, the temperature rise due to climate change has increased winter pests with a higher probability of localization, while subtropical pests have emerged [10]. The only available method to prevent the spread of PWD is early detection followed by tree felling and fumigation [11]. Nonetheless, the analysis and accurate prediction of damaged areas are limited due to low geographical accessibility and economic constraints in the case of conventional land monitoring [9; 12; 13]. Various attempts have been made to use UAV imagery for PWDT detection, particularly using multispectral imagery and vegetation indices [9; 11], time series imagery [14; 15; 16], and deep learning (DL) [14; 17; 18]. To summarize the studies using vegetation indices and multispectral imagery, [PERSON] et al. [8] performed a UAV forecasting mainly targeting the boundary areas of the PWD damaged area so as to resolve the difficulty in differentiating the regions with similar color values in automatic sorting of RGB images. [PERSON] and [PERSON] [9] determined the optimal vegetation index via a comparative analysis of the estimation accuracy of various vegetation indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), and satellite constellation visualization (SAVI). [PERSON] et al. [16] used time series hyperspectral imaging to characterize the spatial distribution of PWDT adopting NDVI and Vegetation Index green (Vlgreen), and the results showed the characteristics of the overall dispersion of PWDT in a large-scale area based on the data of PWDT as a function of time.