Measuring urban quality and change through the detection of physical attributes of decay

The quality of the urban environment is crucial for societal well-being. Yet, measuring and tracking the quality of urban environment, their evolution, and spatial disparities is difficult due to the amount of on-the-ground data needed to capture these patterns. The growing availability of street view images presents new prospects in identifying urban features. However, the reliability and consistency of these methods across different locations and time remains largely unexplored. We aim to develop a comprehensive index of urban quality and change at the street segment level using Google Street View (GSV) imagery. We focus on eight object classes that indicate urban decay or contribute to an unsightly urban space, such as potholes, graffiti, garbage, tents, barred or broken windows, discolored or dilapidated façades, weeds, and utility markings. We train an object detection model on a dataset of images from different cities and assess the performance of these urban indices. We evaluate the effectiveness of this method in various urban contexts over time and discuss its potential for urban planning and public policy. We demonstrate the use of these indices in three applications: the Tenderloin in San Francisco, the Doctores and Historic Center neighborhoods in Mexico City, and South Bend, Indiana.