用深度模型从已有站点推算全国空气质量，成本低精度高。
AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
- 用可学习掩码令牌重建未监测区域的空气质量特征
- 在1085个站点数据上验证，比多个基线方法更准
- 适合缺监测站地区做空气质量推演
实时监测空气质量对保障公共健康和社会发展至关重要，但大规模部署监测站受限于高昂成本。为此，我们提出AirRadar，一种深度神经网络，通过利用现有监测站数据，准确推断缺乏监测站点地区的实时空气质量。AirRadar采用两阶段策略：首先捕捉空间相关性，再校正分布偏移。基于中国1,085个站点长达一年的数据集进行验证，结果显示其性能优于多种基线方法，即使在不同缺失数据比例下仍保持优势。源代码已公开于https://github.com/CityMind-Lab/AirRadar。
原文摘要 · Abstract (English)
Monitoring real-time air quality is essential for safeguarding public health and fostering social progress. However, the widespread deployment of air quality monitoring stations is constrained by their significant costs. To address this limitation, we introduce \emph{AirRadar}, a deep neural network designed to accurately infer real-time air quality in locations lacking monitoring stations by utilizing data from existing ones. By leveraging learnable mask tokens, AirRadar reconstructs air quality features in unmonitored regions. Specifically, it operates in two stages: first capturing spatial correlations and then adjusting for distribution shifts. We validate AirRadar's efficacy using a year-long dataset from 1,085 monitoring stations across China, demonstrating its superiority over multiple baselines, even with varying degrees of unobserved data. The source code can be accessed at https://github.com/CityMind-Lab/AirRadar.
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