MCSDNet: Mesoscale Convective System Detection Network via Multi-scale Spatiotemporal Information

The accurate detection of Mesoscale Convective Systems (MCS) is crucial for meteorological monitoring due to their potential to cause significant destruction through severe weather phenomena such as hail, thunderstorms, and heavy rainfall. However, the existing methods for MCS detection mostly targets on single-frame detection, which just considers the static characteristics and ignores the temporal evolution in the life cycle of MCS. In this paper, we propose a novel encoder-decoder neural network named MCSDNet to detect MCS regions. MCSDNet has a simple architecture and is easy to expand. Different from the previous models, MCSDNet targets on multi-frames detection and leverages multi-scale spatiotemporal information in remote sensing imagery(RSI). As far as we know, it is the first work to utilize multi-scale spatiotemporal information to detect MCS regions. Firstly, we design a multi-scale spatiotemporal information module to extract multi-level semantic from different encoder levels, which makes our models can extract more detail spatiotemporal features. Secondly, Spatiotemporal Mix Unit(STMU), a dual spatiotemporal attention, is introduced to MCSDNet to capture both intra-frame features and inter-frame. Finally, we present MCSRSI, the first publicly available dataset for multi-frames MCS detection based on FY-4A satellite. We also conduct several experiments on MCSRSI and find that our proposed MCSDNet achieves the best performance on MCS detection task when comparing to other baseline methods. We hope that the combination of our open-access dataset and promising results will encourage the future research for MCS detection task and provide a robust framework for related tasks in atmospheric science. Our code is available
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