Exploring the Latent Manifold of City Patterns

Abstract

Understanding how cities evolve through time and how humans interact with their surroundings is a complex but essential task that is necessary for designing better urban environments. Recent developments in artificial intelligence can give researchers and city developers powerful tools, and through their usage, new insights can be gained on this issue. Discovering a high-level structure in a set of observations within a low-dimensional manifold is a common strategy used when applying machine learning techniques to tackle several problems while finding a projection from and onto the underlying data distribution. This so-called latent manifold can be used in many applications such as clustering, data visualization, sampling, density estimation, and unsupervised learning. Moreover, data of city patterns has some particularities, such as having superimposed or natural patterns that correspond to those of the depicted locations. In this research, multiple manifolds are explored and derived from city pattern images. A set of quantitative and qualitative tests are proposed to examine the quality of these manifolds. In addition, to demonstrate these tests, a novel specialized dataset of city patterns of multiple locations is created, with the dataset capturing a set of recognizable superimposed patterns.