Sub-surface, yet-undiscovered cultural heritage sites (such as buried ancient structures and monuments) can be identified on remote sensing data from a variety of sensors in the form of anomalies or traces detectable on bare soils, crops, and vegetation. The current availability of free remote sensing datasets through platforms, like Copernicus, is unprecedented. Such datasets have huge potential and are already amply used within the worldwide cultural heritage 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 cultural heritage remote sensing scholarship, therefore, is to develop or improve instruments that can facilitate the 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 cultural heritage objects and patterns related to anthropogenic interference on landscapes in the past using the 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.