LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data

2026-04-22 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Computer Vision & Image Processing · Depth: Expert, extended

Summary

LatticeVision introduces a global image-to-image (I2I) framework for estimating and emulating non-stationary spatial processes, addressing the computational intractability of Maximum Likelihood Estimation (MLE) for large, gridded spatial data. The framework treats both spatial fields and their associated parameters as images, enabling the use of I2I networks like U-Net, ViT, and STUN for simultaneous parameter estimation in a single forward pass. This approach significantly outperforms traditional local Convolutional Neural Network (CNN) methods in accuracy, robustness with few replicates, and inference speed, achieving 100-1000 times faster inference. LatticeVision also includes a novel data generation pipeline that encodes scientifically meaningful priors for geoscientific applications, such as climate modeling. When applied to Earth System Model (ESM) outputs, specifically CESM1 LENS surface temperature sensitivity fields, the STUN-based emulator generated thousands of realistic fields in seconds, accurately preserving complex spatial correlation structures like zonal patterns along the equator, which local CNNs failed to capture.

Key takeaway

For AI Scientists and Machine Learning Engineers working with large, non-stationary spatial data, consider adopting the LatticeVision framework. Your teams can achieve significantly faster and more accurate parameter estimation by leveraging global image-to-image networks, especially when dealing with limited data replicates. This approach enables rapid, high-fidelity emulation of complex systems like climate models, offering a powerful alternative to computationally prohibitive traditional methods and enhancing uncertainty quantification for downstream analyses.

Key insights

Image-to-image networks globally estimate non-stationary spatial parameters faster and more accurately than local methods.

Principles

• Treating spatial fields and parameters as images enables I2I network application.
• Global parameter estimation improves accuracy and speed over local, section-based methods.
• Synthetic data generation with geophysical priors is crucial for training I2I estimators.

Method

LatticeVision uses I2I networks (U-Net, ViT, STUN) to map spatial field images to parameter field images. It generates synthetic training data with diverse spatial patterns and then pairs estimators with the LatticeKrig SAR model for rapid ensemble simulation.

In practice

• Use I2I networks for non-stationary spatial data parameter estimation.
• Generate synthetic training data with relevant geophysical priors.
• Integrate with SAR models for efficient large-ensemble simulation.

Topics

• LatticeVision
• Image-to-Image Networks
• Non-Stationary Spatial Data
• Spatial Autoregressive Models
• Parameter Estimation

Code references

• antonyxsik/LatticeVision

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.