Individual Coniferous Tree

Research on individual coniferous trees focuses on understanding their characteristics and states, from health assessment to structural analysis. Current approaches leverage machine learning, employing models like convolutional neural networks and random forests to classify tree decay stages using remote sensing data (LiDAR and imagery) and achieving high accuracy in automated assessments. Furthermore, computational methods are being explored to model tree-like structures for tasks such as hierarchical document analysis and efficient path planning in dynamic environments, utilizing algorithms based on tree construction and adaptive replanning. These advancements improve forest management, document processing, and robotics applications.