WildGraph: Realistic Long-Horizon Trajectory Generation with Limited Sample Size
Ali Al-Lawati, Elsayed Eshra, Prasenjit Mitra · 2024
Trajectory generation is an important task in movement studies. Generated trajectories augment the training corpus of deep learning applications, facilitate experimental and theoretical research, and mitigate the privacy concerns associated with real trajectories. This is especially significant in the wildlife domain, where trajectories are scarce due to the ethical and technical constraints of the collection process. In this paper, we consider the problem of generating long-horizon trajectories, akin to wildlife migration, based on a small set of real samples. We propose a hierarchical approach to learn the global movement characteristics of the real dataset, and recursively refine localized regions. Our solution, WildGraph discretizes the geographic path into a prototype network of H31 regions and leverages a novel recurrent VAE to probabilistically generate paths over the regions, based on occupancy. Experiments performed on two wildlife migration datasets demonstrate the remarkable capability of WildGraph to generate realistic months-long trajectories using a sample size as small as 60 while improving generalization compared to existing work. Moreover, WildGraph achieves superior or comparable performance on performance measures, including geographic imagery similarity. Our code is published on the following repository: https://github.com/aliwister/wildgraph.