ThermalTrack

ThermalTrack is an RGB-LWIR paired dataset of wheel tracks captured under harsh winter conditions, including white-outs (severely degraded visibility), low-contrast snow terrain, and diverse wheel track geometries. Designed to enable robust alternative navigation strategies for winter autonomy systems, this dataset builds upon WADS (https:\/\/digitalcommons.mtu.edu\/wads\/), a specialized dataset for autonomous vehicle research in inclement winter weather. To improve model generalization, we augmented the dataset using random flips, rotations, brightness\/contrast adjustments, scaling, noise injection, and perspective transformations - simulating a broader range of challenging conditions. Starting with 951 base images, we expanded the dataset to 7,608 samples, enhancing the model's ability to handle real-world variability.