Using User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities

TL;DR

This paper proposes a novel method using computer vision to analyze online social media photos for estimating and monitoring air pollution levels in major cities, offering a potentially cost-effective alternative to traditional sensors.

Contribution

It introduces a new approach that correlates haze levels from photos with official PM 2.5 data, demonstrating the feasibility of social media images for air quality assessment.

Findings

Image-based haze levels correlate with PM 2.5 data

Synthetic and real photos validate the approach

Potential for cost-effective air quality monitoring

Abstract

With the rapid development of economy in China over the past decade, air pollution has become an increasingly serious problem in major cities and caused grave public health concerns in China. Recently, a number of studies have dealt with air quality and air pollution. Among them, some attempt to predict and monitor the air quality from different sources of information, ranging from deployed physical sensors to social media. These methods are either too expensive or unreliable, prompting us to search for a novel and effective way to sense the air quality. In this study, we propose to employ the state of the art in computer vision techniques to analyze photos that can be easily acquired from online social media. Next, we establish the correlation between the haze level computed directly from photos with the official PM 2.5 record of the taken city at the taken time. Our experiments based on both synthetic and real photos have shown the promise of this image-based approach to estimating and monitoring air pollution.