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any area in Italy. Therefore, this algorithm could be a valuable tool for risk management authorities, as it could be used to understand the extent of the catastrophe in case of emergency, but also to identify the most vulnerable areas to plan possible mitigation strategies. Clearly the predictions made by the model need to be further validated, e.g., by comparing the results with on-site surveys or other reliable sources, considering areas with different sizes and in different Italian locations. Finally, other CNNs could be trained to predict other meaningful building parameters that influence seismic vulnerability to increase the accuracy of the seismic risk estimates. Acknowledgements This work is part of the European project BESTOFRAC. The authors would like to offer special thanks to the Institut für Strukturmechanik (ISM) of the Bauhaus-Universität in Weimar (Germany). References Boeing G., 2021. Spatial information and the legibility of urban form: Big data in urban morphology. 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