Sub-surface, yet-undiscovered Cultural Heritage sites (CH) can be identified on Remote Sensing (RS) data from a variety of sensors (multispectral, hyperspectral and radar satellite platforms, etc.) in the form of anomalies or traces detectable on bare soils, crops or vegetation. The current extraordinary availability of free RS data through platforms, like Copernicus, poses severe hindrances in terms of processing and interpreting them to the point that the quantity of data is not manageable by traditional ‘human’ visual interpretation. This entails developing Artificial Intelligence (AI) methods to automatically process the data in order to identify buried CH sites The CLS project takes charge of it by supporting the development of specific methods that look at automatically identify specific CH objects and patterns related to anthropogenic interference on landscapes in the past using latest breakthroughs in Machine Learning and Computer Vision. In particular, this project seeks to kickstart the application of cutting-edge computational methods in order to define a broad-spectrum, adaptable and robust automated recognition procedure customised for CH objects in remotely sensed data available from Copernicus platform. Automating remote se sensing analytics via Artificial Intelligence will produce large benefits in terms of CH —and especially archaeological — object detection in satellite imagery and represents a significant breakthrough in the discipline as it will replace existing procedures based on subjective observation.