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Deep learning approaches for marine fog detection and forecasting haveoutperformed traditional methods, demonstrating significant scientific andpractical importance. However, the limited availability of open-source datasetsremains a major challenge. Existing datasets, often focused on a single regionor satellite, restrict the ability to evaluate model performance across diverseconditions and hinder the exploration of intrinsic marine fog characteristics.To address these limitations, we introduce \textbf{MFogHub}, the firstmulti-regional and multi-satellite dataset to integrate annotated marine fogobservations from 15 coastal fog-prone regions and six geostationarysatellites, comprising over 68,000 high-resolution samples. By encompassingdiverse regions and satellite perspectives, MFogHub facilitates rigorousevaluation of both detection and forecasting methods under varying conditions.Extensive experiments with 16 baseline models demonstrate that MFogHub canreveal generalization fluctuations due to regional and satellite discrepancy,while also serving as a valuable resource for the development of targeted andscalable fog prediction techniques. Through MFogHub, we aim to advance both thepractical monitoring and scientific understanding of marine fog dynamics on aglobal scale. The dataset and code are at\href{https://github.com/kaka0910/MFogHub}{https://github.com/kaka0910/MFogHub}.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting