Sub-surface or hidden Cultural Heritage sites can be discovered through Earth Observation (EO) data from a variety of sensors (e.g., hyperspectral, multispectral, radar) by identifying and analysing anomalies or traces on bare soils, crops or vegetation that could be connected to the presence of archaeological deposits under them. Buried heritage contexts in fact can limit the growth and development of vegetation or determine a change in the colour and/or humidity level of bare soils above them, leading to an alteration of their spectral characteristics that can be identified through EO spectral images. The main goal of PERSEO project is to ascertain the suitability of PRISMA hyperspectral data for applications in the cultural heritage domain, namely it aims to set a benchmark in the use of such data for the automatic detection of undiscovered heritage sites. Therefore, it will: i) advance the use of hyperspectral data in the cultural sector and the information extraction from the imagery’s spectral content compared to the possibilities offered by multispectral data; and ii) define a clear pipeline for the use of Machine Learning approaches for automatic image analysis in cultural heritage domain as well as in other comparable contexts.