Fine-Grained Air Quality Inference with Remote Sensing Data and Ubiquitous Urban Data

Abstract

Air quality has gained much attention in recent years and is of great importance to protecting people’s health. Due to the influence of multiple factors, the limited air quality monitoring stations deployed in cities are unable to provide fine-grained air quality information. One cost-effective way is to infer air quality with records from existing monitoring stations. However, the severe data sparsity problem (e.g., only 0.2% data are known) leads to the failure of most inference methods. We observe that remote sensing data are of high quality and have a strong correlation with the air quality. Therefore, we propose to integrate remote sensing data and ubiquitous urban data for the air quality inference. But there are two main challenges, i.e., data heterogeneity and incompleteness of the remote sensing data. To address the challenges, we propose a two-stage approach. In the first stage, we infer and predict air quality conditions of some places leveraging the remote sensing data and meteorological data with two proposed ANN-based methods, respectively. This stage significantly alleviates the data sparsity problem. In the second stage, the records and estimated air quality data are put in a tensor. A tensor decomposition method is applied to complete the tensor. The features extracted from urban data are classified into the spatial features (i.e., road features and POI features) and the temporal features (i.e., meteorological features) as the constraints to further address the data sparsity problem. In addition, an iterative training framework is proposed to improve the inference performance. Experiments on a real-world dataset show that our approach outperforms state-of-the-art methods, such as U-Air.