用跨分辨率注意力提升高分辨率空气污染预测速度与精度
Cross-Resolution Attention Network for High-Resolution PM2.5 Prediction
- 双分支视觉变换器融合低分辨率气象数据与高分辨率污染数据
- 1公里分辨率欧洲地图预测仅需1.8秒，比基线减少4.7%~10.7%误差
- 适合需要实时高精度环境监测的科研与公共政策应用
视觉变换器在时空预测中表现优异，但在超大尺度、高分辨率的环境监测场景中扩展性受限。以1公里分辨率绘制整个欧洲空气质量图包含2900万像素，远超普通自注意力计算极限。本文提出CRAN-PM，一种双分支视觉变换器，通过跨分辨率注意力机制高效融合25公里分辨率气象数据与当前时刻1公里分辨率PM2.5数据。不依赖温度、地形等物理输入，而是引入高程感知自注意力和风向引导的跨注意力，促使网络学习具有物理一致性的特征表示。该模型全可训练且内存高效，在单张GPU上1.8秒内完成2900万像素的欧洲地图生成。在2022年欧洲每日PM2.5预测任务（362天，2971个欧洲环境署监测站）中，相较最优单尺度基线，T+1时刻RMSE降低4.7%，T+3时刻降低10.7%，复杂地形区域偏差减少36%。
原文摘要 · Abstract (English)
Vision Transformers have achieved remarkable success in spatio-temporal prediction, but their scalability remains limited for ultra-high-resolution, continent-scale domains required in real-world environmental monitoring. A single European air-quality map at 1 km resolution comprises 29 million pixels, far beyond the limits of naive self-attention. We introduce CRAN-PM, a dual-branch Vision Transformer that leverages cross-resolution attention to efficiently fuse global meteorological data (25 km) with local high-resolution PM2.5 at the current time (1 km). Instead of including physically driven factors like temperature and topography as input, we further introduce elevation-aware self-attention and wind-guided cross-attention to force the network to learn physically consistent feature representations for PM2.5 forecasting. CRAN-PM is fully trainable and memory-efficient, generating the complete 29-million-pixel European map in 1.8 seconds on a single GPU. Evaluated on daily PM2.5 forecasting throughout Europe in 2022 (362 days, 2,971 European Environment Agency (EEA) stations), it reduces RMSE by 4.7% at T+1 and 10.7% at T+3 compared to the best single-scale baseline, while reducing bias in complex terrain by 36%.
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