M4Fog: A Global Multi-Regional, Multi-Modal, and Multi-Stage Dataset for Marine Fog Detection and Forecasting to Bridge Ocean and Atmosphere
arxiv(2024)
Abstract
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.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined