Learning and Recognizing Archeological Features from LiDAR Data

We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS Geoscope. Today, archeologists get overwhelmed by the task of visually surveying huge amounts of (raw) LiDAR data in order to identify areas of interest for inspection on the ground. We showcase a software system pipeline that results in significant savings in terms of expert productivity while missing only a small fraction of the artifacts. Our work employs artificial neural networks in conjunction with an efficient spatial segmentation procedure based on domain knowledge. Data processing is constraint by a limited amount of training labels and noisy LiDAR signals due to vegetation cover and decay of ancient structures. We aim at identifying geo-spatial areas with archeological artifacts in a supervised fashion allowing the domain expert to flexibly tune parameters based on her needs

Integrated Geomatic Techniques for Georeferencing and Reconstructing the Position of Underground Archaeological Sites: The Case Study of the Augustus Sundial (Rome)

A large part of the archaeological remains still to be discovered and excavated are not in remote and depopulated areas of the earth but are often beneath urban centres that have buried them with centuries of debris and later constructions. Excavating in these contexts is much more complex than digging in rural or sparsely inhabited areas because of the constraints imposed by existing buildings and infrastructure. It should also be considered that within an urbanised area, any archaeological remains are concentrated in the subsoil of the historic centre, which is, therefore, often surmounted by buildings that are more recent than the remains but historical as well, and thus, of considerable value and vulnerability. For this reason, an archaeological excavation in an urban area must be preceded by a real feasibility study, where the potential risks for the structures above are minimised and accurately quantified. In many situations, as in the case under study, the discovery of a small segment of a structure is the only clue to reconstruct the development of the remaining part still to be excavated, which may stretch tens or hundreds of metres away from the measurable part. As a consequence, an error of a few centimetres in the survey of the excavated part can lead to errors of metres in estimating the positions of the far parts still to be excavated, and this, in many cases, as in the one under study, must absolutely be avoided. In practice, high-precision geomatic surveys, in support of the archaeological and historical interpretation of the observable structures, will help to establish the exact locations to possibly continue the excavations, helping the accurate planning of the excavation itself. Here, we have shown how the various techniques, compared to each other, have made it possible to reconstruct the location of a short stretch (less than 7 metres) of the Emperor Augustus' Sundial, the only currently visible evidence of a scientific instrument of imposing dimensions (tens of metres in length and height) that served to define some of the characteristics of the calendar that we still use today. The portion of the sundial currently observable, according to the most reliable hypotheses, is located approximately at one end of a structure and extends for several tens of metres. The accurate positioning of the observable parts in a geodetic reference system will enable to identify with certainty the possible areas in which excavation may continue and will also allow to accurately reconstruct the principle of operation of the sundial through an approach that could be defined as "reverse engineering" of the scientific instrument itself. The aim of this work is to study and thus define the combination and integration of existing geomatic techniques for this specific field of applicatio