Potential Analysis of Feature Extraction Based Quick Response for Environmental Change with Social Media Photos

Yuanfeng Wu, Lianru Gao, Wenzhi Liao, Paolo Gamba, Bing Zhang

Abstract: A framework based on color feature extraction of social media photos and correlation analysis with air quality parameters is proposed to monitor environmental change. More specifically, photos of the Beijing Olympic Park from Panoramio website have been analyzed as a case study. The aerosol optical depth data at 500 nm wavelength (AOD 500) obtained from sun-photometer observation network station has been used as reference. Results show a proof of concept that social media photos have an interesting potential for air pollution estimate and remote sensing parameter validation with a low cost.