A Text-Guided Graph Neural Network for Predicting Long-Range Trajectories of Sea-Surface Objects

The sea-surface environment is influenced by various factors, such as wind, waves, tides, and ocean currents. Interactions among various factors, combined with heterogeneous spatiotemporal distribution, lead to highly uncertain dynamics for sea-surface objects. Accordingly, forecasting sea-surface object trajectories constitutes a highly significant challenge. A common approach is to employ temporal prediction networks to model the trajectories of objects in complex motion. However, these methods do not accurately capture the motion state of an object at any given moment. To address this challenge, we propose a spatial representation and motion pattern graph neural network (SRMPGNN). We represent the motion states of objects as tokenized textual descriptions and perform spatially aligned mapping and interaction with graph neural features. Moreover, long-horizon sea-surface objects are extremely faint, and we propose a graph super-resolution (GSR) reconstruction to address this challenge. We further design spatial representations and multifrequency enhancement (ME) to highlight the trajectory characteristics of moving objects on the sea surface. Through end-to-end training, the SRMPGNN achieves state-of-the-art (SOTA) performance on two benchmarks.