Sub-surface, yet-undiscovered cultural heritage (CH) sites (such as buried ancient structures and monuments) can be identified on remote sensing data (RS) from a variety of sensors (multispectral and hyperspectral data from satellite platforms, Radar data etc.) in the form of anomalies or traces detectable on bare soils, crops or vegetation. Current availability of free remote sensing datasets through platforms, like Copernicus and USGS, is unprecedented. Such datasets have huge potential and are already amply used within the worldwide CH community thanks especially to the availability of time series imagery. However, the extraordinary proliferation of data has posed significant hurdles in terms of managing, processing and interpreting them to the point that the quantity of data is not manageable by traditional ‘human’ visual interpretation. The new challenge in CH remote sensing scholarship therefore is to develop or improve instruments that can facilitate automatic detection of objects of interest. This 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 Artificial Intelligence (AI). This project will also considerably expand existing approaches to the identification of ancient land division systems —and more generally of landscape patterning— by automating procedures of similarly-oriented linear feature detection. Automating RS analytics via AI 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.
