做轨迹数据增强的团队终于有了选择策略的指导——Outlierness 和 Uncertainty 在稀疏场景下比随机采样更稳定，做动物行为或交通分析的开发者可以直接参考论文中的评估框架来优化自己的数据增强流程。
该论文针对轨迹数据增强中随机选择轨迹的不足，提出了一个系统性的选择框架，评估了五种策略：Outlierness、Diversity、Representativeness、Uncertainty 和 Random。在四个数据集（动物行为、海事交通、城市交通）上测试后，发现系统性选择（尤其是 Outlierness 和 Uncertainty）比随机基线更稳定，在稀疏数据集中能修复拓扑断裂，但在高质量密集数据集中可能引入噪声。研究还指出，在高速运动场景中，标准扰动技术会导致特征空间发散，表明数据增强的价值是有条件的。
A Systematic Approach for Selecting Trajectories for Data Augmentation
Trajectory data augmentation is a promising approach to mitigate data scarcity in machine learning applications, but its utility has been limited by the complexity of preserving spatio-temporal coherence. Although prior work demonstrated the viability of geometric perturbation, it relied on naive random selection, leaving a critical gap in understanding which trajectories should be augmented for maximal benefit. This thesis addresses this gap by developing a systematic and scalable framework to evaluate five systematic selection strategies: Outlierness, Diversity, Representativeness, Uncertainty, and Random selection. These strategies were rigorously tested across four datasets covering animal behavior (Foxes and Starkey), maritime traffic (AIS), and urban traffic (Car) using a suite of linear and non-linear machine learning models. As part of this evaluation, an Optuna-based hyperparameter optimization loop was integrated to empirically identify the best-performing augmentation parameters for each dataset within the explored search space. The results indicate that, while systematic selection is not a universal solution, it offers distinct advantages over the random baseline. Systematic strategies, particularly Outlierness and Uncertainty, demonstrated higher stability and were less prone to performance degradation observed with random sampling in dense datasets. However, the findings also reveal that the value of augmentation is strictly conditional. Visual analysis via UMAP demonstrates that while systematic augmentation successfully repairs topological fragmentation in sparse datasets, it can act as a corrupting noise signal in high-quality, dense datasets. Furthermore, the study identified physical limitations in high-velocity domains, where standard perturbation techniques lead to divergence in feature space...