  Environmental sensors are crucial for monitoring weather conditions and the
impacts of climate change. However, it is challenging to place sensors in a way
that maximises the informativeness of their measurements, particularly in
remote regions like Antarctica. Probabilistic machine learning models can
suggest informative sensor placements by finding sites that maximally reduce
prediction uncertainty. Gaussian process (GP) models are widely used for this
purpose, but they struggle with capturing complex non-stationary behaviour and
scaling to large datasets. This paper proposes using a convolutional Gaussian
neural process (ConvGNP) to address these issues. A ConvGNP uses neural
networks to parameterise a joint Gaussian distribution at arbitrary target
locations, enabling flexibility and scalability. Using simulated surface air
temperature anomaly over Antarctica as training data, the ConvGNP learns
spatial and seasonal non-stationarities, outperforming a non-stationary GP
baseline. In a simulated sensor placement experiment, the ConvGNP better
predicts the performance boost obtained from new observations than GP
baselines, leading to more informative sensor placements. We contrast our
approach with physics-based sensor placement methods and propose future steps
towards an operational sensor placement recommendation system. Our work could
help to realise environmental digital twins that actively direct measurement
sampling to improve the digital representation of reality.
