论文标题
高分辨率雨电影预测在时空转移下
RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts
论文作者
论文摘要
本文提出了一种解决Weather4cast 2022挑战阶段2的解决方案。挑战的目的是使用低分辨率的多型卫星图像预测从地面雷达获得的未来高分辨率降雨事件。我们建议一种解决适合挑战的数据预处理的解决方案，然后使用新颖的雨网预测降雨电影。 Rainunet是一个层次的U形网络，使用脱钩的大核3D卷积，具有时间可分开的块（TS块），以提高预测性能。各种评估指标表明，与基线方法相比，我们的解决方案是有效的。源代码可从https://github.com/jinyxp/weather4cast-2022获得
This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground radar using low-resolution multiband satellite images. We suggest a solution that performs data preprocessing appropriate to the challenge and then predicts rainfall movies using a novel RainUNet. RainUNet is a hierarchical U-shaped network with temporal-wise separable block (TS block) using a decoupled large kernel 3D convolution to improve the prediction performance. Various evaluation metrics show that our solution is effective compared to the baseline method. The source codes are available at https://github.com/jinyxp/Weather4cast-2022
