用激光扫描验证发现，人工标注让分割模型表现虚高。
Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS
- 用地面激光扫描生成真实标签，替代人工标注作为验证基准
- 模型在真实数据上平均精度仅0.094，远低于人工标注时的0.670
- 闭合树冠林中分割效果差，适合关注真实性能的研究者参考
在个体树木尺度监测森林动态对评估生态系统应对气候变化至关重要，但传统野外调查耗时且覆盖有限。无人机获取的RGB影像结合深度学习模型有望实现精准的单株树冠分割，但现有方法常以人工标注图像为验证标准，缺乏独立真实数据支撑。本研究基于同位置的地面激光扫描（TLS）数据，为混合未管理的北方和地中海森林的无人机影像生成高保真验证标签。评估了两种广泛使用的深度学习树冠分割模型——DeepForest（RetinaNet）和Detectree2（Mask R-CNN）的表现，并与在地中海森林上的人工标注数据对比。在地中海森林的TLS真实标签下，模型性能显著下降（AP50: 0.094 vs. 0.670）。仅保留树冠部分后差距缩小（冠层AP50: 0.365），但仍远低于人工标注结果。在北方森林数据上表现更差（AP50: 0.142），冠层部分提升至0.308。两者在严格重叠率阈值下定位精度极低（最大AP75: 0.051）。类似结果见于航拍激光雷达研究，表明闭合树冠环境下基于航空影像的分割方法存在根本性局限。
原文摘要 · Abstract (English)
Monitoring forest dynamics at an individual tree scale is essential for accurately assessing ecosystem responses to climate change, yet traditional methods relying on field-based forest inventories are labor-intensive and limited in spatial coverage. Advances in remote sensing using drone-acquired RGB imagery combined with deep learning models have promised precise individual tree crown (ITC) segmentation; however, existing methods are frequently validated against human-annotated images, lacking rigorous independent ground truth. In this study, we generate high-fidelity validation labels from co-located Terrestrial Laser Scanning (TLS) data for drone imagery of mixed unmanaged boreal and Mediterranean forests. We evaluate the performance of two widely used deep learning ITC segmentation models - DeepForest (RetinaNet) and Detectree2 (Mask R-CNN) - on these data, and compare to performance on further Mediterranean forest data labelled manually. When validated against TLS-derived ground truth from Mediterranean forests, model performance decreased significantly compared to assessment based on hand-labelled from an ecologically similar site (AP50: 0.094 vs. 0.670). Restricting evaluation to only canopy trees shrank this gap considerably (Canopy AP50: 0.365), although performance was still far lower than on similar hand-labelled data. Models also performed poorly on boreal forest data (AP50: 0.142), although again increasing when evaluated on canopy trees only (Canopy AP50: 0.308). Both models showed very poor localisation accuracy at stricter IoU thresholds, even when restricted to canopy trees (Max AP75: 0.051). Similar results have been observed in studies using aerial LiDAR data, suggesting fundamental limitations in aerial-based segmentation approaches in closed canopy forests.
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