Overviewing the emerging methods for predicting urban Sprawl features

Abstract

Urban sprawl, a common phenomenon characterized by uncontrolled urban growth, has far-reaching socio-economic and environmental implications. It’s a complex phenomenon, and finding a better way to tackle it is essential. Accurate simulation and prediction of urban sprawl features would facilitate decision-making in urban planning and the formulation of city growth policies. This article provides an overview of the techniques used to this end. Initially, it highlights the use of a certain category of so-called traditional methods, such as statistical models or classical machine learning methods. It then focuses particularly on the intersection of deep learning and urban sprawl modelling, examining how deep learning methods are being exploited to simulate and predict urban sprawl. I finally studies hybrid approaches that combine deep learning with agent-based models, cellular automata, or other techniques offer a synergistic way to leverage the strengths of different methodologies for urban sprawl modelling.