Physics-informed inference of aerial animal movements from weather radar data

TL;DR

This paper presents a physics-informed deep learning approach to reconstruct aerial animal movement patterns from weather radar data, integrating physical constraints for improved accuracy and data efficiency.

Contribution

It introduces a novel method combining convolutional encoders, Gaussian transition models, and physics-informed loss to improve movement inference from radar data.

Findings

Effective reconstruction of animal movements on synthetic data

Enhanced physical consistency through mass conservation constraints

Promising results in data-efficient movement pattern inference

Abstract

Studying animal movements is essential for effective wildlife conservation and conflict mitigation. For aerial movements, operational weather radars have become an indispensable data source in this respect. However, partial measurements, incomplete spatial coverage, and poor understanding of animal behaviours make it difficult to reconstruct complete spatio-temporal movement patterns from available radar data. We tackle this inverse problem by learning a mapping from high-dimensional radar measurements to low-dimensional latent representations using a convolutional encoder. Under the assumption that the latent system dynamics are well approximated by a locally linear Gaussian transition model, we perform efficient posterior estimation using the classical Kalman smoother. A convolutional decoder maps the inferred latent system states back to the physical space in which the known radar observation model can be applied, enabling fully unsupervised training. To encourage physical consistency, we additionally introduce a physics-informed loss term that leverages known mass conservation constraints. Our experiments on synthetic radar data show promising results in terms of reconstruction quality and data-efficiency.