Tree Detection in RGB Satellite Imagery Using YOLO-Based Deep Learning Models

Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being. Traditional forest mapping and monitoring methods are often costly and limited in scope, necessitating the adoption of advanced, automated approaches for improved forest conservation and management. This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery. A dataset of 3157 images was collected and divided into training (2528), validation (495), and testing (134) sets. To enhance model robustness and generalization, data augmentation was applied to the training part of the dataset. Various YOLO-based models, including YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, were evaluated using different hyperparameters and optimization techniques, such as stochastic gradient descent (SGD) and auto-optimization. These models were assessed in terms of detection accuracy and the number of detected trees. The highest-performing model, YOLOv12m, achieved a mean average precision (mAP@50) of 0.908, mAP@50:95 of 0.581, recall of 0.851, precision of 0.852, and an F₁-score of 0.847. The results demonstrate that YOLO-based object detection offers a highly efficient, scalable, and accurate solution for individual tree detection in satellite imagery, facilitating improved forest inventory, monitoring, and ecosystem management. This study underscores the potential of AI-driven tree detection to enhance environmental sustainability and support data-driven decision-making in forestry.