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 for MCS detection(MCSDNet). 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 for the detection of MCS regions 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, a Spatiotemporal Mix Unit(STMU) is introduced to MCSDNet to capture both intra-frame features and inter-frame correlations, which is a scalable module and can be replaced by other spatiotemporal module, e.g., CNN, RNN, Transformer and our proposed Dual Spatiotemporal Attention(DSTA). This means that the future works about spatiotemporal modules can be easily integrated to our model. Finally, we present MCSRSI, the first publicly available dataset for multi-frames MCS detection based on visible channel images from the FY-4A satellite. We also conduct several experiments on MCSRSI and find that our proposed MCSDNet achieve the best performance on MCS detection task when comparing to other baseline methods.

翻译：中尺度对流系统（MCS）因其可能引发冰雹、雷暴和强降雨等严重天气现象并造成重大破坏，其准确检测对气象监测至关重要。然而，现有MCS检测方法大多针对单帧检测，仅考虑静态特征而忽略了MCS生命周期的时空演化过程。本文提出一种新颖的编码器-解码器神经网络用于MCS检测（MCSDNet）。该网络架构简洁且易于扩展。与先前模型不同，MCSDNet针对多帧检测任务，利用多尺度时空信息对遥感影像中的MCS区域进行检测。据我们所知，这是首个利用多尺度时空信息检测MCS区域的工作。首先，我们设计了一个多尺度时空信息模块，从不同编码器层级提取多层次语义特征，使模型能够提取更精细的时空特征。其次，我们在MCSDNet中引入时空混合单元（STMU），用于捕获帧内特征与帧间相关性。该模块具有可扩展性，可替换为其他时空模块（如CNN、RNN、Transformer及我们提出的双路时空注意力机制DSTA），这意味着未来关于时空模块的研究成果可便捷地集成至本模型。最后，我们构建了基于FY-4A卫星可见光通道影像的首个公开多帧MCS检测数据集MCSRSI。在MCSRSI上的实验表明，与其他基线方法相比，本文提出的MCSDNet在MCS检测任务中取得了最佳性能。