MoveBench: A Benchmark for Global-Scale Wildlife Movement Forecasting

2026-09-14

AI Summary

研究通过构建MoveBench，一个包含2.6百万个GPS点的大规模基准，来解决野生动物运动预测的问题，并采用概率评估方法分析了不同预测技术的有效性。

Abstract

Understanding and predicting wildlife movement is critical for ecology and conservation. While trajectory forecasting has advanced for human and vehicle movement, wildlife trajectories present distinct challenges: they are unconstrained in space, highly stochastic, and influenced by environmental conditions. We introduce MoveBench, the first large-scale benchmark for probabilistic wildlife movement forecasting, containing 2.6M GPS locations from 800+ individuals across 110 species in 127 countries, paired with 1.6B environmental raster tiles capturing 160 covariates known or hypothesized to influence movement. We propose a probabilistic evaluation protocol for movement trajectory forecasts, addressing limitations of point-prediction metrics for inherently stochastic phenomena. Through comprehensive empirical evaluation of four method families across multiple temporal and spatial scales, we reveal that: (1) existing predictive methods generalize better to future timepoints than to unseen individuals, (2) deep learning approaches do not consistently outperform simpler baselines, and (3) environmental covariate selection significantly impacts performance. MoveBench enables standardized evaluation of movement forecasting methods and provides a foundation for methodological advances on this ecologically important task.

Problem

• wildlife movement
• forecasting
• ecology
• conservation
• trajectory

Innovation

• wildlife movement forecasting
• probabilistic evaluation protocol
• environmental covariates