  The air quality inference problem aims to utilize historical data from a
limited number of observation sites to infer the air quality index at an
unknown location. Considering the sparsity of data due to the high maintenance
cost of the stations, good inference algorithms can effectively save the cost
and refine the data granularity. While spatio-temporal graph neural networks
have made excellent progress on this problem, their non-Euclidean and discrete
data structure modeling of reality limits its potential. In this work, we make
the first attempt to combine two different spatio-temporal perspectives, fields
and graphs, by proposing a new model, Spatio-Temporal Field Neural Network, and
its corresponding new framework, Pyramidal Inference. Extensive experiments
validate that our model achieves state-of-the-art performance in nationwide air
quality inference in the Chinese Mainland, demonstrating the superiority of our
proposed model and framework.
