This repo is related to a paper that is yet to be published: This code is related to a paper that is yet to be published: Understanding how cities evolve through time and the way humans interact with their surroundings is a complex but essential task that is necessary for designing better urban environments. The recent development in artificial intelligence can give researchers and city developers powerful tools and new insights into 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. Finding a projection from and onto the underlying data distribution and this 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 are 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 this data set captures a set of recognizable superimposed patterns clusters