Sat-JEPA-Diff: Bridging Self-Supervised Learning and Generative Diffusion for Remote Sensing

ArXi:2603.13943v1 Announce Type: cross Predicting satellite imagery requires a balance between structural accuracy and textural detail. Standard deterministic methods like PredRNN or SimVP minimize pixel-based errors but suffer from the "regression to the mean" problem, producing blurry outputs that obscure subtle geographic-spatial features. Generative models provide realistic textures but often misleadingly reveal structural anomalies. To bridge this gap, we