UNSUPERVISED RECONSTRUCTION OF SEA SURFACE CURRENTS FROM AIS MARITIME TRAFFIC DATA USING LEARNABLE VARIATIONAL MODELS

Space oceanography missions, especially altimeter missions, have considerably improved the observation of sea surface dynamics over the last decades. They can however hardly resolve spatial scales below similar to 100km. Meanwhile the AIS (Automatic Identification System) monitoring of the maritime traffic implicitly conveys information on the underlying sea surface currents as the trajectory of ships is affected by the current. Here, we show that an unsupervised variational learning scheme provides new means to elucidate how AIS data streams can be converted into sea surface currents. The proposed scheme relies on a learnable variational framework and relate to variational auto-encoder approach coupled with neural ODE (Ordinary Differential Equation) solving the targeted ill-posed inverse problem. Through numerical experiments on a real AIS dataset, we demonstrate how the proposed scheme could significantly improve the reconstruction of sea surface currents from AIS data compared with state-of-the-art methods, including altimetry-based ones.

Automated summary

Space oceanography missions, especially altimeter missions, have improved observation of sea surface dynamics, but struggle with smaller spatial scales. A new unsupervised variational learning scheme converts AIS data into sea surface currents, showing significant improvements over current state-of-the-art methods through numerical experiments on real AIS datasets.