用YOLOv8和可解释AI分析激光点云，提升树种分类准确率至96%。

Aiming to advance research in the field of interpretability of deep learning models for tree species classification using TLS 3D point clouds we present insights in the classification abilities of YOLOv8 through a new framework which enables systematic analysis of saliency maps derived from CAM (Class Activation Mapping). To investigate the contribution of structural tree features to the classification decisions of the models, we link regions with high saliency derived from the application of Finer-CAM to segments of 2D side-view images that correspond to structural tree features. Using TLS 3D point clouds from 2445 trees across seven European tree species, we trained five YOLOv8 models with cross-validation, reaching a mean accuracy of 96% (SD = 0.24%) when applied to the test data. Our results demonstrate that Finer-CAM can be considered faithful in identifying discriminative regions that discriminate target tree species. This renders Finer-CAM suitable for enhancing the interpretability of the tree species classification models. Analysis of 630 saliency maps indicate that the models primarily rely on image regions associated with tree crowns for species classification. While this result is pronounced in Silver Birch, European Beech, English oak, and Norway Spruce, image regions associated with stems contribute more frequently to the differentiation of European ash, Scots pine, and Douglas-fir. We demonstrate that the visibility of detailed structural tree features in the 2D side-view images enhances the discriminative performances of the models, indicating YOLOv8`s abilities to leverage detailed point cloud representations. Our results represent a first step toward enhancing the understanding of the classification decision processes of tree species classification models, aiding in the identification of data set and model limitations, and building confidence in model predictions.