# M4Fog: A Global Multi-Regional, Multi-Modal, and Multi-Stage Dataset for Marine Fog Detection and Forecasting to Bridge Ocean and Atmosphere

arXiv: https://arxiv.org/abs/2406.13317 · HTML: https://mlsanity.com/paper/2406.13317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [UniFormer: Unifying Convolution and Self-attention for Visual Recognition](https://mlsanity.com/paper/2201.09450/) (2022)
- [Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction](https://mlsanity.com/paper/2003.01460/) (2020)
- [Learning to Decompose and Disentangle Representations for Video Prediction](https://mlsanity.com/paper/1806.04166/) (2018)
- [Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics](https://mlsanity.com/paper/1811.07490/) (2018)
- [Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2206.12126/) (2022)
- [OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning](https://mlsanity.com/paper/2306.11249/) (2023)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images](https://mlsanity.com/paper/2106.12413/) (2021)
- [UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation](https://mlsanity.com/paper/2204.00631/) (2022)
- [Dual Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager](https://mlsanity.com/paper/2205.02069/) (2022)

## Built on by (most cited first, 1 of 1)

- [OmniEarth-Bench: Towards Holistic Evaluation of Earth's Six Spheres and Cross-Spheres Interactions with Multimodal Observational Earth Data](https://mlsanity.com/paper/2505.23522/) (2025)

## Abstract (arXiv)

Marine fog poses a significant hazard to global shipping, necessitating effective detection and forecasting to reduce economic losses. In recent years, several machine learning (ML) methods have demonstrated superior detection accuracy compared to traditional meteorological methods. However, most of these works are developed on proprietary datasets, and the few publicly accessible datasets are often limited to simplistic toy scenarios for research purposes. To advance the field, we have collected nearly a decade's worth of multi-modal data related to continuous marine fog stages from four series of geostationary meteorological satellites, along with meteorological observations and numerical analysis, covering 15 marine regions globally where maritime fog frequently occurs. Through pixel-level manual annotation by meteorological experts, we present the most comprehensive marine fog detection and forecasting dataset to date, named M4Fog, to bridge ocean and atmosphere. The dataset comprises 68,000 "super data cubes" along four dimensions: elements, latitude, longitude and time, with a temporal resolution of half an hour and a spatial resolution of 1 kilometer. Considering practical applications, we have defined and explored three meaningful tracks with multi-metric evaluation systems: static or dynamic marine fog detection, and spatio-temporal forecasting for cloud images. Extensive benchmarking and experiments demonstrate the rationality and effectiveness of the construction concept for proposed M4Fog. The data and codes are available to whole researchers through cloud platforms to develop ML-driven marine fog solutions and mitigate adverse impacts on human activities.