Urban Tree Detection: Comparing YOLOv8 and DeepForest for Accurate Single-Tree Identification from Aerial Imagery

This study addresses the need for accurate tree inventory monitoring in urban areas to support planning and environmental efforts, by leveraging advances in deep learning for single tree detection. Using very high resolution aerial images of a residential area in Munich, we compared the performance of two object detection algorithms, YOLOv8 and DeepForest, on the urban tree detection task. The ground truth data consisted of hand drawn tree bounding boxes, and the performance was monitored mostly by F1 score measures. Subsequently, efforts concentrated on improving DeepForest’s performance through fine-tuning. The enhancements yielded a notable increase in the model’s F1 score from 0.7365 to 0.8030, indicating the effectiveness of these techniques. These findings further underline the potential of deep learning for urban tree detection and highlight the viability of models like DeepForest for creating accurate urban tree inventories, with promising avenues for future enhancement.