Digital Geoarchaeology by Christoph Siart Markus Forbriger & Olaf Bubenzer

Author:Christoph Siart, Markus Forbriger & Olaf Bubenzer
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

Applying the above described workflow to large images results in a huge number of false detections. Therefore, the geometric properties of the known LSEs were used for filtering the results. Mapping their sizes against their rectangularity measures resulted in a clear distribution of the known LSEs towards one end of the overall distribution, such that a linear classifier could be defined that discarded the majority of the false detections. This classifier can be used in other contexts as well.

Testing the workflow on the original dataset showed that all known LSEs were reliably detected. In addition, a low number of hitherto unrecorded LSEs were detected, too. Applying the workflow to a similar dataset from the Bernese Alps also yielded promising results, which have yet to be validated in the field.

The Silvretta case study shows how computational tools can extract meaningful archaeological information from complex remote sensing data, thereby assisting archaeological fieldwork and enquiry. As in other disciplines, the combination of domain knowledge with methodological expertise from remote sensing and computer science is the key to tapping the full potential of remote sensing data.

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