Urban impervious surface extraction based on the integration of remote sensing images and social media data

This paper presents an inspiring approach for accurate estimation of impervious surfaces, which exploits the strength of two kind of heterogeneous features, i.e., physical features derived from satellite images and social features derived from social media datasets, respectively. On the one hand, we use a morphological attribute profiles guided spectral mixture analysis model to achieve estimates of physical features. On the other hand, we mine the social features from textual information of social media datasets. Then, a multivariable linear regression model is conducted to obtain the impervious surfaces. Experiment results, conducted with multi-spectral images collected by LANDSAT-8 and social media datasets scraped from Sina Weibo of Guangzhou city, suggest that our approach could lead to reliable and good estimation of the imperviousness.