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Inferring fine-grained air pollution map via a spatiotemporal super-resolution scheme

Air pollution monitoring is a concerned issue in urban management. Nowadays, vehicle-based mobile sensing systems are deployed to enhance sensing granularity. However, in these systems, the number of online sensors may change over time and the long-term maintenance is costly. Therefore, an inference algorithm is necessary to maintain the high spatiotemporal granularity of pollution field under both dense and sparse sampling. In this paper, we propose an algorithm based on super-resolution scheme. To address the challenges of complex external factors and spatiotemporal dependencies, three modules are included: a heterogeneous data fusion subnet to extract useful information from external data, a spatiotemporal residual subnet to capture the spatiotemporal dependencies in pollution field, and an upsampling subnet to generate the fine-grained pollution map. Experiments on real-world datasets show that our model outperforms the state-of-the-art baselines.