There is a prevailing trend to study urban morphology quantitatively thanks to the growing accessibility to various forms of spatial big data, increasing computing power, and use cases benefiting from such information. The methods developed up to now measure urban morphology with numerical indices describing density, proportion, and mixture, but they do not directly represent morphological features from the human's visual and intuitive perspective. We take the first step to bridge the gap by ...| Urban Analytics Lab | Singapore
Urban road networks (URNs) are ubiquitous and essential components of cities. Visually, they present diverse patterns that embody latent planning principles. However, we still lack a global insight into such patterns. In this paper, we propose a scalable deep learning-based framework to automate accurate and multiscale classification of road network patterns in cities and present a comprehensive global implementation on 144 major cities around the world, yielding their multiscale pattern prof...| Urban Analytics Lab | Singapore