Neural Processes for Optimal Sensor Placement

We develop machine learning models that learn from oceanographic data to estimate environmental variables—such as temperature, salinity, or current velocity—across space and time. These regression models can be used to generate high-resolution maps from sparse measurements, effectively filling in data gaps and predicting conditions in unobserved regions. When these models include uncertainty estimates, they not only provide predictions but also reveal how confident the model is and which inputs most influence the results.

Specifically, we use Neural Processes—a class of models that combines the strengths of Gaussian Processes and Neural Networks. Neural Processes are designed for meta-learning: they learn across multiple related tasks and can generalize to new ones. A distinctive feature of these models is their ability to organize diverse input data into structured, gridded representations.

One key application of our work is the sensor placement problem in marine data science. This challenge involves determining where to place sensors in the ocean to gather the most informative data, maximizing coverage and accuracy while minimizing costs and redundancy. To address this, we apply Convolutional Conditional Neural Processes, which identify optimal sensor locations by minimizing uncertainty in the predicted environmental variables. By strategically using the most informative measurements, our models produce more accurate and reliable environmental forecasts.

Our framework supports a range of critical tasks, including spatial interpolation, downscaling, and gap-filling. These methods are essential for converting sparse or incomplete ocean observations into continuous, high-resolution environmental maps—vital tools for understanding and managing marine systems.