Timely and accurate information on tree species is of great importance for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. A review of the remote sensing data and deep learning tree species classification methods is lacking in its analysis of unimodal and multimodal remote sensing data and classification methods in this field. To address this gap, we search for major trends in remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss some limitations of tree species classification.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

The outbreak of Pine Shoot Beetle (PSB, Tomicus spp.) posed a significant threat to the health of Yunnan pine forests, necessitating the development of an efficient and accurate remote sensing monitoring method. The integration of unmanned aerial vehicle (UAV) imagery and deep learning algorithms shows great potential for monitoring forest-damaged trees. Previous studies have utilized various deep learning semantic segmentation models for identifying damaged trees in forested areas; however, these approaches were constrained by limited accuracy and misclassification issues, particularly in complex forest backgrounds. This study evaluated the performance of five semantic segmentation models in identifying PSB-damaged trees (UNet, UNet++, PAN, DeepLabV3+ and FPN). Experimental results showed that the FPN model outperformed the others in terms of segmentation precision (0.8341), F1 score (0.8352), IoU (0.7239), mIoU (0.7185) and validation accuracy (0.9687). Under the pure Yunnan pine background, the FPN model demonstrated the best segmentation performance, followed by mixed grassland-Yunnan pine backgrounds. Its performance was the poorest in mixed bare soil-Yunnan pine background. Notably, even under this challenging background, FPN still effectively identified diseased trees, with only a 1.7% reduction in precision compared to the pure Yunnan pine background (0.9892). The proposed method in this study contributed to the rapid and accurate monitoring of PSB-damaged trees, providing valuable technical support for the prevention and management of PSB.

The accurate classification of traditional Dai ethnic residential architectures is beneficial for the elucidation of the laws of homologous differentiation of Dai ethnic groups, and is of great significance for further research on the landscape division of Dai ethnic traditional settlements. However, at present, Nevertheless, the creation of a dataset of traditional Dai ethnic residential architectures has yet to be undertaken, and no literature on the deep learning algorithm for the classification of these structures could be retrieved. To address this gap in the existing literature , this paper presents a collection of image data on traditional Dai ethnic residential archi-tectures in the Pu'er region of Yunnan Province, China. The data was collected through field research. The original data have been subjected to cleansing procedures in order to construct a da-taset comprising five distinct types of traditional Dai ethnic residential architectures. The five types of traditional Dai ethnic residential architecture are as follows: 'fence-standing with square roof', 'fence-standing with rectangular roof', 'courtyard-standing with double-eaved sloped roof', 'courtyard-standing with single-eaved sloped roof', and 'traditional earthen flat-roof house'. Following the data enhancement, the Inception V3 network model was employed for classification. The model demonstrated superior performance, achieving a classification accuracy of 93.41%, which surpassed the accuracy, recall, and loss rate of ResNet50 (Residual Network 50, ResNet50) and AlexNet(Alex Krizhevsky Network, AlexNet). This artificial intelligence classification method offers an efficient and intelligent method for elucidating the genetic characteristics and landscape differences of different types of Dai ethnic traditional residential architectures. This approach facilitates the transition from manual to intelligent genealogical study of traditional residential architectures.