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.

翻译：环境传感器对于监测天气状况及气候变化影响至关重要。然而，如何以最大化测量信息价值的方式布置传感器颇具挑战性，尤其在像南极洲这样的偏远地区。概率机器学习模型可通过寻找能最大程度降低预测不确定性的点位，为传感器信息布局提供建议。高斯过程模型广泛应用于此目的，但其难以捕捉复杂的非平稳行为，且难以扩展至大规模数据集。本文提出采用卷积高斯神经过程应对上述难题。卷积高斯神经过程利用神经网络在任意目标位置参数化联合高斯分布，兼具灵活性与可扩展性。以模拟的南极洲地表气温异常作为训练数据，卷积高斯神经过程学习了空间与季节性的非平稳特征，其性能优于非平稳高斯过程基准模型。在模拟传感器布局实验中，卷积高斯神经过程比高斯过程基线模型能更精准地预测新观测数据带来的性能提升，从而生成更具信息价值的传感器布局方案。我们将其与基于物理模型的传感器布局方法进行对比，并提出了构建实用化传感器布局推荐系统的未来方向。该工作有助于实现能够主动引导测量采样的环境数字孪生系统，从而提升对现实世界的数字化表征能力。