Characterizing the perception of urban spaces from visual analytics of street-level imagery.

This project uses machine learning and computer vision techniques and a novel interactive visualization tool to provide street-level characterization of urban spaces such as safety and maintenance in urban neighborhoods. This is achieved by collecting and annotating street-view images, extracting objective metrics through computer vision techniques, and using crowdsourcing to statistically model the perception of subjective metrics such as safety and maintenance. For modeling human perception and scaling it up with a predictive algorithm, we evaluate perception predictions across two points in time separated by economic changes in the urban core of Raleigh, North Carolina, in the aftermath of the 2008 Great Recession.