Publication Details
Xuening Qin, , Jelle Hofman, Esther Rodrigo, Valerio Panzica La Manna, Nikos Deligiannis, Wilfried Philips

International Conference on Innovation in Artificial Intelligence (ICIAI 2021).

Contribution To Book Anthology


The spatial heterogeneity and temporal variability of air pollution in urban environments make air quality inference for fine-grained air pollution monitoring extremely challenging. Most of the existing work estimates the air quality using sparse measurements collected from a limited number of fixed monitoring stations. In this work, we propose a geographically context-aware random forest model for street-level air quality inference using high spatial resolution data collected by opportunistic mobile sensor network. Compared with traditional random forest model, the proposed method builds a local model for each location by considering the neighbors in both geographical and feature space. The model is evaluated on our real air quality dataset collected from mobile sensors in Antwerp, Belgium. The experimental results show that the proposed method outperforms a series of commonly used methods including Ordinary Kriging (OK), Inverse Distance Weighting (IDW) and Random forest (RF).