Aquascan: Graph-Based Learning for Distributed Marine Sensing

Marine monitoring faces unprecedented challenges as climate change and human activities reshape ocean ecosystems. Traditional tracking methods struggle with the scale and complexity of modern marine sensing needs. This paper proposes distributed networks of low-cost drifting sensors and presents a comparative study of heterogeneous graph neural networks (GNNs) versus Kalman filters for predicting marine entity trajectories in such distributed networks with intermittent observations. Using the Aquascan simulation framework, we model drifting sensors detecting marine entities across 480 km² of ocean surface with realistic movement patterns and sparse coverage. Experiments across multiple prediction horizons show GNNs significantly outperform Kalman filters: GNNs maintain over 95% AUC (Area Under the Curve) across all horizons while Kalman filters degrade from 97% to 69% AUC. The performance gap widens under challenging conditions—at 5 km sensor spacing, GNNs achieve 92.8% AUC versus 66.9% for Kalman filters. GNNs' superior performance stems from leveraging network topology and reasoning about non-detections to infer entity presence in coverage gaps. These results demonstrate that graph-based approaches offer substantial advantages for distributed marine monitoring.