Transformer-Based Wildlife Species Classification from Daily Movement Trajectories
Authors: Obed Irakoze, Prasenjit Mitra
Summary
The authors train sequence models to identify animal species from GPS movement trajectories alone, using data from seven species tracked on Movebank. They test how well models generalize when entire studies or geographic regions are held out (a harder test than random splitting). Transformers beat LSTM, CNN, and temporal convolutional baselines by 8–22 percentage points in balanced accuracy, and adding richer movement features (speed, direction, turning angle) beyond simple displacement helps especially for rare species like lions and zebras.
Main takeaways:
- Transformers consistently outperform recurrent and convolutional architectures on wildlife trajectory classification, achieving 0.83 balanced accuracy and 0.92 AUC on elephant detection
- Testing with whole studies or regions held out is much harder than random splits, but better reflects real deployment scenarios
- Enriching trajectories with speed, direction, and turning features (not just position changes) significantly improves performance, especially for underrepresented species
- One-hour resolution works better than 30-minute resolution across studies, likely because it reduces missing data and ensures consistent temporal coverage
- The approach shows promise for species identification from movement patterns alone, useful when visual identification is impractical
Relevance
Not related to my language model persona work. Included because it's an interesting application of Transformers to time-series classification with a thoughtful evaluation protocol (holdout by study/region), but no connection to behavior installation or conditional model behavior.
Abstract
arXiv:2605.06726v1 Announce Type: new Abstract: Inferring the identity of wildlife species from daily movement data alone is a challenging task. We train sequence models on large-scale, 7-species GPS trajectories from the Movebank platform. Trajectories models are evaluated using a protocol in which entire telemetry studies or regions are heldout during testing. We compare Transformer-based sequence models to LSTM, CNN, and Temporal Convolutional Networks, and find that Transformers consistently achieve higher balanced accuracy with gains of approximately 8 to 22 percentage points, depending on the species and experimental setting. In an elephant binary classification task with 1-hour resolution, the Transformer achieves a balanced accuracy of 0.83 and an AUC of 0.92, substantially outperforming all baseline models. We examine, under data-limited conditions, feature representations by analyzing the differences between a basic displacement-based encoding and an expanded range of movement descriptors that include speed, direction, and turning behavior. With feature augmentation, we see clear performance gains, especially for underrepresented and sparsely represented species, such as large carnivores, lions, and Zebras. Finally, experiments comparing 1-hour and 30-minutetemporal resolutions show that while finer sampling can capture short-term movement patterns for some species, a unified 1-hour resolution yields more promising performance across studies by reducing missing data and ensuring consistent temporal coverage.