mlsanityData to 14 Oct 2026

AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks

arXiv 2501.13141 ↗First posted 22 Jan 2025cs.LG cs.AIcited by 2

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Abstract (arXiv)

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 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 40 of its 92 references that are in the map. First posted on arXiv 22 Jan 2025.

Builds on

40 of 92 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Modelscited by 2
  2. 2026Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecastingcited by 1