Urban Tree

Urban tree research focuses on efficiently assessing and managing urban forests to improve urban environments and mitigate climate change. Current efforts leverage deep learning, particularly convolutional neural networks, and image processing techniques (like YOLOv8 and DeepSORT) applied to various data sources, including aerial imagery, mobile phone photos, and video footage, to automate tasks such as tree detection, diameter estimation, and damage assessment. These advancements enable more accurate and large-scale monitoring of tree health, distribution, and canopy cover, informing urban planning decisions related to tree placement and green space optimization for improved human well-being and climate resilience. The resulting data-driven approaches are improving the efficiency and accuracy of urban forestry management.