The growing prevalence of location-based devices has resulted in a signi!cant abundance of location data from various tracking vendors. Nevertheless, a noticeable de!cit exists regarding readily accessible, extensive, and publicly available datasets for research purposes, primarily due to privacy concerns and ownership constraints. There is a pressing need for expansive datasets to advance machine learning techniques in this domain. The absence of such resources currently represents a substantial hindrance to research progress in this !eld. Data augmentation is emerging as a popular technique to mitigate this issue in several domains. However, applying state-of-the-art techniques as-is proves challenging when dealing with trajectory data due to the intricate spatio-temporal dependencies inherent to such data. In this work, we propose a novel strategy for augmenting trajectory data that applies a geographical perturbation on trajectory points along a trajectory. Such a perturbation results in controlled changes in the raw trajectory and, consequently, causes changes in the trajectory feature space. We test our strategy in two trajectory datasets and show a performance improvement of approximately 20% when contrasted with the baseline. We believe this strategy will pave the way for a more comprehensive framework for trajectory data augmentation that can be used in !elds where few labeled trajectory data are available for training machine learning models.