Deep Learning-Based Palm Tree Detection for Urban Green Space Monitoring Using High-Resolution UAV Imagery

Yiğit, Abdurahman Yasin

Urban green space management is essential for sustainable urban planning, requiring accurate and efficient tree detection methods. This study compares three deep learning models—YOLOv11, faster region-based convolutional neural network (R-CNN), and DeepForest—for palm tree detection in an urban environment using high-resolution unmanned aerial vehicle (UAV) imagery. A UAV-based photogrammetric workflow was implemented, generating georeferenced orthomosaic maps to serve as the primary dataset. The models were assessed based on accuracy, processing speed, and suitability for urban vegetation mapping. YOLOv11 outperformed the other models, achieving an overall detection accuracy of 94.2% with an intersection over union (IoU) of 88.6%, making it the most suitable model for large-scale urban tr...