  Given the increasingly serious air pollution problem, the monitoring of air
quality index (AQI) in urban areas has drawn considerable attention. This paper
presents ImgSensingNet, a vision guided aerial-ground sensing system, for
fine-grained air quality monitoring and forecasting using the fusion of haze
images taken by the unmanned-aerial-vehicle (UAV) and the AQI data collected by
an on-ground three-dimensional (3D) wireless sensor network (WSN).
Specifically, ImgSensingNet first leverages the computer vision technique to
tell the AQI scale in different regions from the taken haze images, where
haze-relevant features and a deep convolutional neural network (CNN) are
designed for direct learning between haze images and corresponding AQI scale.
Based on the learnt AQI scale, ImgSensingNet determines whether to wake up
on-ground wireless sensors for small-scale AQI monitoring and inference, which
can greatly reduce the energy consumption of the system. An entropy-based model
is employed for accurate real-time AQI inference at unmeasured locations and
future air quality distribution forecasting. We implement and evaluate
ImgSensingNet on two university campuses since Feb. 2018, and has collected
17,630 photos and 2.6 millions of AQI data samples. Experimental results
confirm that ImgSensingNet can achieve higher inference accuracy while greatly
reduce the energy consumption, compared to state-of-the-art AQI monitoring
approaches.
