Visual Spatial Learning: Single-Field Spatial Interpolation Using Convolutional Neural Networks
arXiv:2605.30167v1 Announce Type: cross Abstract: 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 acc
The proliferation of advanced AI techniques, particularly in areas like deep learning, is enabling new approaches to long-standing problems in spatial data analysis.
This work demonstrates a new application of CNNs for single-field spatial interpolation, which could significantly improve prediction accuracy and efficiency in fields reliant on environmental or statistical modeling, reducing the need for extensive domain expertise.
Traditional interpolation methods, which often require complex variography and Gaussian process assumptions, may be augmented or replaced by more adaptable and less expertise-dependent CNN-based approaches, especially for non-stationary spatial data.
- · Environmental modeling sector
- · Spatial statistics researchers
- · AI/ML developers
- · Geospatial intelligence platforms
- · Developers of legacy spatial interpolation software
- · Consultants specializing solely in traditional geostatistics
Improved accuracy in predicting spatially correlated fields from sparse data.
Faster and more automated analysis in areas like climate modeling, resource management, and epidemiology.
Enhanced AI systems that can independently infer spatial patterns from minimal data, leading to more robust autonomous systems in diverse fields.
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Read at arXiv cs.LG