Abstract
AbstractThis Thesis developed and applied Computational and Machine Learning techniques on institutional archaeological data collections and environmental variables, defining an innovative Archaeological Site Modeling procedure. Several case studies have been presented to show how the methodology works in practice (Cantons of Zurich, Aargau, Grisons, Vaud, Geneva, Fribourg in Switzerland). The work developed tackles the traditional archaeological question of site detection and the issue of preservation and conservation of archaeological sites in the long-term perspective by combining cutting-edge technologies with analytical archaeological reasoning. It represents a unique and innovative approach for modeling archaeological sites at any spatio-temporal scale.
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