mlsanityData to 14 Oct 2026

Spatio-Temporal Field Neural Networks for Air Quality Inference

arXiv 2403.02354 ↗First posted 2 Mar 2024cs.LG cs.AIcited by 12

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 2 Mar 2024.

Builds on

25 of 42 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2016Layer Normalizationcited by 5,669
  5. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  6. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  7. 2016Variational Graph Auto-Encoderscited by 1,382
  8. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  9. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  10. 2013Generating Sequences With Recurrent Neural Networkscited by 554
  11. 2017Convolutional Sequence to Sequence Learningcited by 552
  12. 2021Neural Fields in Visual Computing and Beyondcited by 540
13 more
  1. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  2. 2019Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networkscited by 368
  3. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 172
  4. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  5. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  6. 2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
  7. 2019Sequential Neural Processescited by 55
  8. 2019Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networkscited by 40
  9. 2021Spatio-Temporal Urban Knowledge Graph Enabled Mobility Predictioncited by 24
  10. 2023Graph Neural Processes for Spatio-Temporal Extrapolationcited by 19
  11. 2021Decoupling Long- and Short-Term Patterns in Spatiotemporal Inferencecited by 15
  12. 2023Machine Learning for Urban Air Quality Analytics: A Surveycited by 8
  13. 2023Neural Vector Fields: Generalizing Distance Vector Fields by Codebooks and Zero-Curl Regularizationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 5.

12 papers.

  1. 2025MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)cited by 3
  2. 2025AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networkscited by 2
  3. 2026Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Modelscited by 2
  4. 2025SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptationcited by 1
  5. 2025OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learningcited by 1
  6. 2025FlowNet: Modeling Dynamic Spatio-Temporal Systems via Flow Propagationcited by 1
  7. 2026Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecastingcited by 1
  8. 2026AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecastingcited by 1
  9. 2025Scale-Disentangled spatiotemporal Modeling for Long-term Traffic Emission Forecastingcited by 0
  10. 2025RainSeer: Fine-Grained Rainfall Reconstruction via Physics-Guided Modelingcited by 0
  11. 2026Learning Spatio-Temporal Foundation Models from Pure Synthetic Datacited by 0
  12. 2026From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observationscited by 0