Visual Spatial Learning: Single-Field Spatial Interpolation Using Convolutional Neural Networks
Predicting a complete spatially correlated field from sparse observations is a fundamental challenge in spatial statistics and environmental modelling. Classical interpolation methods such as Kriging rely on Gaussian process assumptions and variography, which can limit their effectiveness in non-stationary settings and require substantial domain expertise. In this work, we leverage an architecture based on convolutional neural networks (CNNs) for spatial interpolation that is trained and applied on a single partially observed field, without access to external data or prior fields. The model is supervised directly on the observed locations and learns to predict values at unobserved points ...
| Authors | Daniel Tinoco, Raquel Menezes, Carlos Baquero et al. |
|---|---|
| Primary category | stat.ML |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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