More than two-thirds of the world's population is expected to live in cities by 2050, according to the United Nations. As urbanization advances around the globe, researchers say that the quality of the urban physical environment will become increasingly critical to human well-being and to sustainable development initiatives. However, measuring and tracking the quality of an urban environment, its evolution, and its spatial disparities is difficult due to the amount of on-the-ground data needed to capture these patterns. To address the issue, Yong Suk Lee, an assistant professor from the University of Notre Dame, and Andrea Vallebueno from Stanford University used ML to develop a scalable method to measure urban decay at a spatially granular level over time. Lee said that by using machine learning, we can help urban planners and policymakers better understand the deterioration of urban space and its importance in future planning.