Tree Crown Detection

Tree crown detection focuses on automatically identifying and delineating individual tree crowns in aerial imagery, primarily to facilitate efficient and large-scale forest monitoring. Current research heavily utilizes deep learning, particularly object detection and semantic segmentation models like Mask R-CNN and RetinaNet, often incorporating multispectral or thermal data to improve accuracy and robustness in challenging conditions like shadowing and overlapping canopies. This technology offers significant advancements in forest management and conservation efforts by enabling rapid assessment of forest health, dieback, and the impact of disturbances such as bark beetle infestations, ultimately improving the speed and cost-effectiveness of ecosystem monitoring.