MICHAL BIRKENFELD

Recent studies demonstrate the effectiveness of integrated archaeo-geophysical tools in resolving various geological-environmental challenges. This involves combining geophysical methods in archaeological fieldwork or remote sensing methods for preliminary survey and analysis of archaeological sites, potentially enhanced by machine learning techniques to estimate object shapes and characteristics. This study highlights the potential of employing informational and probabilistic approaches as optimal tools for evaluating and integrating critical information for archaeological research. Our proposed procedure for assessing the reliability of tools or toolsets is based on improved methodologies utilizing conditional probability, which were suggested in previous authors' publications. We illustrate examples of combining remote sensing, known for its low cost, portability, and effectiveness in initial archaeological site identification, with machine learning methods to locate and discover new sites in archaeologically well-studied areas in Israel. Subsequently, we conduct an informational assessment of remote sensing data and propose steps to correlate this data with other geophysical information probabilistically.