Review of An Improved UAV Identification and Detection Using Deep Learning

Abstract

Urban greenery, including trees, plays a crucial role in enhancing the livability of cities. Accurate identification and assessment
of urban trees are important for urban planning, environmental conservation, and management. Traditional methods of tree identification often relied on manual inspection, which can be time-consuming and labor-intensive. By training models on extensive datasets of high-resolution UAV imagery, this research endeavors to develop a system capable of autonomously and accurately identifying and classifying urban trees. Deep learning algorithms excel at learning complex patterns and features, making them well-suited for this task. This advancement holds great promise for enhancing urban planning and environmental conservation efforts by providing a more efficient and accurate means of tree identification in urban areas