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[2602.12869] X-VORTEX: Spatio-Temporal Contrastive Learning for Wake Vortex Trajectory Forecasting
Summary
The paper presents X-VORTEX, a novel spatio-temporal contrastive learning framework designed to enhance wake vortex trajectory forecasting using sparse LiDAR data.
Why It Matters
Wake vortices pose significant safety and operational challenges in air traffic management. This research introduces a method that leverages unlabeled data to improve vortex tracking, potentially enhancing safety and efficiency in aviation.
Key Takeaways
- X-VORTEX utilizes contrastive learning to improve vortex trajectory forecasting.
- The framework effectively addresses challenges of sensor sparsity and temporal dynamics.
- It achieves superior vortex localization with only 1% of the labeled data compared to traditional methods.
- The approach is grounded in Augmentation Overlap Theory, enhancing model robustness.
- Real-world evaluations demonstrate the framework's effectiveness on extensive LiDAR datasets.
Computer Science > Machine Learning arXiv:2602.12869 (cs) [Submitted on 13 Feb 2026] Title:X-VORTEX: Spatio-Temporal Contrastive Learning for Wake Vortex Trajectory Forecasting Authors:Zhan Qu, Michael Färber View a PDF of the paper titled X-VORTEX: Spatio-Temporal Contrastive Learning for Wake Vortex Trajectory Forecasting, by Zhan Qu and Michael F\"arber View PDF HTML (experimental) Abstract: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 subsamp...
