Wake vortices are strong, coherent air turbulences created by aircraft, and they pose a major safety and capacity challenge for air traffic management. Tracking how vortices move, weaken, and dissipate over time from LiDAR measurements is still difficult because scans are sparse, vortex signatures fade as the flow breaks down under atmospheric turbulence and instabilities, and point-wise annotation is prohibitively expensive. Existing approaches largely treat each scan as an independent, fully supervised segmentation problem, which overlooks temporal structure and does not scale to the vast unlabeled archives collected in practice. We present X-VORTEX, a spatio-temporal contrastive learning framework grounded in Augmentation Overlap Theory that learns physics-aware representations from unlabeled LiDAR point cloud sequences. X-VORTEX addresses two core challenges: sensor sparsity and time-varying vortex dynamics. It constructs paired inputs from the same underlying flight event by combining a weakly perturbed sequence with a strongly augmented counterpart produced via temporal subsampling and spatial masking, encouraging the model to align representations across missing frames and partial observations. Architecturally, a time-distributed geometric encoder extracts per-scan features and a sequential aggregator models the evolving vortex state across variable-length sequences. We evaluate on a real-world dataset of over one million LiDAR scans. X-VORTEX achieves superior vortex center localization while using only 1% of the labeled data required by supervised baselines, and the learned representations support accurate trajectory forecasting.

翻译：尾涡是飞机产生的强烈、相干空气湍流，对空中交通管理的安全性和容量构成重大挑战。由于扫描数据稀疏、涡旋特征在大气湍流和不稳定性作用下随流动破碎而衰减，以及逐点标注成本极高，从激光雷达测量中追踪涡旋随时间的移动、减弱和消散过程仍然十分困难。现有方法大多将每次扫描视为独立的、完全监督的分割问题，忽略了时间结构，且难以扩展到实践中收集的大量未标注档案。我们提出了X-VORTEX，一个基于增强重叠理论的时空对比学习框架，可从无标注的激光雷达点云序列中学习具有物理感知能力的表示。X-VORTEX解决了两个核心挑战：传感器稀疏性和时变涡旋动力学。它通过将弱扰动序列与经过时间子采样和空间掩蔽生成的强增强对应序列相结合，从同一基础飞行事件中构建配对输入，促使模型在缺失帧和部分观测之间对齐表示。在架构上，一个时间分布的几何编码器提取每帧扫描特征，而一个序列聚合器则对可变长度序列中演化的涡旋状态进行建模。我们在一个包含超过一百万次激光雷达扫描的真实数据集上进行评估。X-VORTEX在仅使用监督基线所需标注数据1%的情况下，实现了更优的涡旋中心定位，且学习到的表示支持精确的轨迹预测。