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There is a wide variety of Urban Growth models all of which pose limitations to their computational application. In this book, a modeling framework is described that is open and adaptable to the user¿s requirements, easy to use and resilient to the case study limitations. The CaFe model (Cellular Automata - Fuzzy Engine) applies an innovative workflow to connect the input and the output variables. All input variables are merged in a single thematic layer using the Sensitive Sum operator. It is a new fuzzy operator that employs a dynamic parallel connection between the input variables while taking into account their statistical correlation. As a result, the model does not require certain variables/data to run while it implements a reducible/extensible form of Knowledge Base which can include both data-driven and empirical rules. The simulation engine incorporates advanced Cellular Automata techniques which apply spatio-temporal multi-radius transition functions and support action in distance. CaFe is applied in Athens ¿ Greece and simulates urban growth efficinetly while scoring high fitting indicators and retaining a stable average error.