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WLC-Net: a robust and fast deep-learning wood-leaf classification method
Wood-leaf classification is an essential and fundamental prerequisite in the analysis and estimation of forest attributes from terrestrial laser scanning (TLS) point clouds, including critical measurements such as diameter at breast height(DBH), above-ground biomass(AGB), wood volume. To address this, we introduce the Wood-Leaf Classification Network(WLC-Net), a deep learning model derived from PointNet++, designed to differentiate between wood and leaf points within tree point clouds. WLC-Net enhances classification accuracy, completeness, and speed by incorporating linearity as an inherent feature, refining the input-output framework, and optimizing the centroid sampling technique. WLC-Net was trained and assessed using three distinct tree species datasets, comprising a total of 102 individual tree point clouds:21 Chinese ash trees, 21 willow trees, and 60 tropical trees. For comparative evaluation, five alternative methods, including PointNet++, DGCNN, Krishna Moorthy's method, LeWoS, and Sun's method, were also applied to these datasets. The classification accuracy of all six methods was quantified using three metrics:overall accuracy(OA), mean Intersection over Union(mIoU), and F1-score. Across all three datasets, WLC-Net demonstrated superior performance, achieving OA scores of 0. 9778, 0. 9712, and 0. 9508;mIoU scores of 0. 9761, 0. 9693, and 0. 9141;and F1-scores of 0. 8628, 0. 7938, and 0. 9019, respectively. The time costs of WLC-Net were also recorded to evaluate the efficiency. The average processing time was 102. 74s per million points for WLC-Net. In terms of visual inspect, accuracy evaluation and efficiency evaluation, the results suggest that WLC-Net presents a promising approach for wood-leaf classification, distinguished by its high accuracy.
Modular Action Concept Grounding in Semantic Video Prediction
Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the learning of interaction between agents and objects.
Action Concept Grounding Network for Semantically-Consistent Video Generation
Recent works in self-supervised video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the problem of semantic learning.
