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EO-WM proposes a video diffusion transformer for probabilistic Earth observation forecasting that incorporates physically informed conditioning to capture weather-driven uncertainties, achieving improved prediction of vegetation indices under extreme weather.
This paper investigates augmentation techniques for CNN-based classification of multispectral images from visible and thermal infrared cameras, using the ThermalWorld dataset to understand how different augmentations affect classification accuracy.