Deep Learning in the Expansion of the Urban Spot

A modeling of urban expansion is presented that empirically and methodologically applies artificial neural networks and cellular automata. Fundamental to recognize the complex reality of the urban spatial dimension, based on key factors of urban growth. Sentinel 2 satellite images with a spatial resolution of 20 m are used for the supervised classification of land use cover and vegetation. The analysis of the change in the use of available land to urban is applied in the Metropolitan Area of Toluca, covers the period 2016–2023, and a future prediction of the potential scenario of urban growth to the year 2030. The results show the potential for automating processes and analyzing changes that are useful for decision-making and intervention in planning and land management.