The increase availability of spaceborne sensors that can provide systematic optical and radar satellite images allows researchers to carried out landscape studies in wider scales. Processing of satellite images, that cover beyond the visible part of the spectrum, allows researchers to better understand environmental and soil related processes, as well as in the reconstruction of landscape evolution through time.
The increased automation of the satellite image analysis and mining workflows have been recently reported in the literature for archaeological applications. Besides, the availability of cloud platforms able to process big earth observation datasets using parallel computing and machine learning approaches is an example of the trend observed in the last years in the space sector. In comparison to ground sensors, remote-sensing observations from satellite sensors may offer considerable potential to characterize spatio-temporal patterns of anomalies from local to broader scales in a relevant, consistent, timely, and cost-efficient manner. Local anomalies detected from satellite observations are considered as indicators for sub-surface proxies, thus providing evidence to archaeologists for more extensive areas and landscapes.
High resolution, multispectral and panchromatic satellite images, permit the sub-meter detailed observation of landscape, the detection of features with archaeological interest which are otherwise invisible, while they can support mapping and monitoring purposes.
Different software and procedures are today available, applied and researched. The European Copernicus program, Europe’s flagship Earth Observation program will be exploited for the benefits of the project.
Aerial photography became a fundamental tool in Archaeology, because it allows a wide and detailed observation of a territory, and the visibility of features that cannot be perceived and identify from the ground.
Ground-based remote sensing techniques allow a non-invasive and non-destructive survey of objects, even on a large scale.