# Environmental Sensor Placement with Convolutional Gaussian Neural Processes

arXiv: https://arxiv.org/abs/2211.10381 · HTML: https://mlsanity.com/paper/2211.10381/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 74 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Neural Processes](https://mlsanity.com/paper/1807.01622/) (2018)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast](https://mlsanity.com/paper/2211.02556/) (2022)
- [Convolutional Conditional Neural Processes](https://mlsanity.com/paper/1910.13556/) (2019)
- [Exact Gaussian Processes on a Million Data Points](https://mlsanity.com/paper/1903.08114/) (2019)
- [The Promises and Pitfalls of Deep Kernel Learning](https://mlsanity.com/paper/2102.12108/) (2021)
- [Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes](https://mlsanity.com/paper/2007.01332/) (2020)
- [The Gaussian Neural Process](https://mlsanity.com/paper/2101.03606/) (2021)
- [Convolutional conditional neural processes for local climate downscaling](https://mlsanity.com/paper/2101.07950/) (2021)
- [Practical Conditional Neural Processes Via Tractable Dependent Predictions](https://mlsanity.com/paper/2203.08775/) (2022)
- [Meta-Learning Mean Functions for Gaussian Processes](https://mlsanity.com/paper/1901.08098/) (2019)
- [Efficient Gaussian Neural Processes for Regression](https://mlsanity.com/paper/2108.09676/) (2021)
- [Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning](https://mlsanity.com/paper/2210.10964/) (2022)

## Built on by (most cited first, 14 of 14)

- [FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation](https://mlsanity.com/paper/2406.01645/) (2024)
- [ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition](https://mlsanity.com/paper/2506.07259/) (2025)
- [FloeNet: A mass-conserving global sea ice emulator that generalizes across climates](https://mlsanity.com/paper/2603.12449/) (2026)
- [Aardvark weather: end-to-end data-driven weather forecasting](https://mlsanity.com/paper/2404.00411/) (2024)
- [Incremental Transformer Neural Processes](https://mlsanity.com/paper/2602.18955/) (2026)
- [Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes](https://mlsanity.com/paper/2506.09163/) (2025)
- [Advancing Ocean State Estimation with efficient and scalable AI](https://mlsanity.com/paper/2511.06041/) (2025)
- [Tracking Wildfire Assets with Commodity RFID and Gaussian Process Modeling](https://mlsanity.com/paper/2512.15956/) (2025)
- [Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks](https://mlsanity.com/paper/2405.12614/) (2024)
- [Deep Optimal Sensor Placement for Black Box Stochastic Simulations](https://mlsanity.com/paper/2410.12036/) (2024)
- [Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)](https://mlsanity.com/paper/2510.22517/) (2025)
- [Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs](https://mlsanity.com/paper/2511.22567/) (2025)
- [Efficient Gaussian process learning via subspace projections](https://mlsanity.com/paper/2601.16332/) (2026)
- [Amortising Inference and Meta-Learning Priors in Neural Networks](https://mlsanity.com/paper/2602.08782/) (2026)

## Abstract (arXiv)

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.