Image-to-image networks estimate parameters of non-stationary SAR models faster and more accurately than traditional methods by framing fields and parameters as images.
We pass the standardized ensembles of temperature sensitivity anomalies into each of our estimation networks: STUN, UNet, ViT, CNN25, CNN17, and CNN9
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LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
Image-to-image networks estimate parameters of non-stationary SAR models faster and more accurately than traditional methods by framing fields and parameters as images.