{"type":"ScholarlyArticle","title":"Spatio-Temporal Field Neural Networks for Air Quality Inference","authors":[{"name":"Yutong Feng","slug":"yutong-feng","given":"Yutong","family":"Feng","affiliation":"Hong Kong University of Science and Technology (Guangzhou) , China"},{"name":"Qiongyan Wang","slug":"qiongyan-wang","given":"Qiongyan","family":"Wang","affiliation":"Hong Kong University of Science and Technology (Guangzhou) , China"},{"name":"Yutong Xia","slug":"yutong-xia","given":"Yutong","family":"Xia","affiliation":"National University of Singapore , Singapore"},{"name":"Junlin Huang","slug":"junlin-huang","given":"Junlin","family":"Huang","affiliation":"Hong Kong University of Science and Technology (Guangzhou) , China"},{"name":"Siru Zhong","slug":"siru-zhong","given":"Siru","family":"Zhong","affiliation":"Hong Kong University of Science and Technology (Guangzhou) , China"},{"name":"Yuxuan Liang","slug":"yuxuan-liang","given":"Yuxuan","family":"Liang","affiliation":"Hong Kong University of Science and Technology (Guangzhou) , China; State Key Lab of Resources and Environmental Information System , China"}],"abstract":"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.","doi":"10.24963/ijcai.2024/803","url":"https://ijcai.org/proceedings/2024/803","pdf":"https://ijcai.org/proceedings/2024/0803.pdf","markdown":"https://ijcai.org/proceedings/2024/803.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/803","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":803,"pages":{"first":7260,"last":7268},"keywords":["Multidisciplinary Topics and Applications: General","Humans and AI: General","Data Mining: General"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2018] Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 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