Abstract While automated feature recognition is still in its infancy in archaeology, within the geosciences recent developments have allowed its application to much more irregular typology, such as the supervised classification of landslides. This success is largely due to the geographical object-based image analysis software TRIMBLE eCognition. In archaeology, this programme has been applied to some extent, yet new additions to the array of available methods require a re-evaluation of its potential for feature recognition. For instance, the ability to integrate LiDAR data and aerial photography has long been desired within archaeology. Additionally, the ability to transfer rulesets for the detection of common features can facilitate data and knowledge-sharing amongst researchers. The case study will present three different automated detection methods; using the well-known eCognition ruleset generation based on cognitive reasoning; self-learning algorithms; and adaptive template matching. These techniques are applied to round barrow detection in the Avebury region in southern England, specifically distinguishing between the known variations of barrow, bank and ditch. Each method is assessed according to its usability for large regions and its potential for detecting variable features and complex shapes. The algorithms are intended to prioritise cognitive aspects of human vision such as elevation, size, shape and texture, using the LiDAR data and aerial photography. It is also stressed that ruleset exchange for generally known features and processes is highly important for mapping large areas across borders and is intrinsically supported by eCognition.
