Result: Design and development of customized applications for the analysis, visualizations, and assessment of the evolution of urban areas using artificial intelligence and machine learning methods ; Σχεδιασμός και ανάπτυξη εξειδικευμένων εφαρμογών ανάλυσης και οπτικοποίησης της εξέλιξης αστικών περιοχών στα πλαίσια ενός διαδυκτιακού γεωχωρικού συστήματος στήριξης αποφάσεων (Web – based Geospatial Decision Support System)

A lack of planning and management of urban development can result in unsustainable urban development. In order to achieve this goal, effective tools must be implemented, and urban growth models have proven to be an invaluable tool when addressing this issue. With the assistance of machine learning, the goal of this thesis is to develop an urban growth model that can be applied to any geographical area within the European Union utilizing a neural network approach. By developing machine-readable formats for the collected historical open spatial data using a methodology that involved collecting, organizing, handling, and transforming the open spatial data, it was possible to develop machine-readable formats for the collected historical open spatial data. The impact factors include the social, economic, and biophysical forces, as well as the neighboring and political influences, which requires the transformation of such data into tabular form. Furthermore, the thesis introduces an artificial neural network (ANN) model, coupled with a detailed methodology for its training and evaluation. This involves leveraging a robust analytical software tool, built on Python programming language, to ascertain the optimal weights for the various impact factors integrated into the model. The culmination of this rigorous process extends to making predictions for the year 2030, in which the research outputs and detailed maps for each of the five-case study European Union (EU) metropolitan areas are meticulously presented. It is essential to underline that the study's scope is not confined to the specific case study areas but is broadened by the utilization of pan-European datasets. This strategic approach ensures that the developed model is not only applicable to the immediate study locations but can be seamlessly extended to encompass any European region. The inclusivity of pan-European datasets is facilitated through the incorporation of an open-source utility designed to support the model. This innovative feature significantly ...