Finding common ground : human and computer vision in archaeological prospection

The (slow) emergence of semi-automated or supervised detection techniques to identify anthropogenic objects in archaeological prospection using remote sensing data has received a mixed reception during the past decade. Critics have stressed the superiority of human vision and the irreplaceability of human judgement in recognising archaeological traces, perceiving a threat that will undermine professional expertise and that archaeological experience and knowledge could be written out of the interpretative process (e.g. Hanson 2008, 2010; Palmer & Cowley 2010; Parcak 2009). Uneasiness amongst some archaeologists of losing control, even partially, of the interpretation process certainly seems to be a significant factor in criticisms, citing the undeniable fact that archaeological remains (or proxies for those remains) can assume a near-unlimited assortment of shapes, sizes and spectral properties. It is argued that only the human observer can deal with such complexity. Thus, while increasingly automated and supervised procedures for object detection and recognition and processing are flourishing in a variety of fields (e.g. medical imaging, facial recognition, cartography, navigation, surveillance; Szeliski 2011), their application to archaeological and, more generally, cultural landscapes is still in its infancy. However, as a number of published works (see References and General Reading List) and ongoing research demonstrate there are major benefits in developing this broad agenda. This paper provides a general review of the issues from a synergistic rather than competitive perspective, highlighting opportunities and discussing challenges. It also summarises a session on Computer vision vs human perception in remote sensing image analysis: time to move on held at the 44th Computer Applications and Quantitative Methods in Archaeology Conference (CAA 2016 Oslo 'Exploring Oceans of Data') that had a similar objective.