(Part II) Automation is here to stay! Building on the 2016 CAA session in Oslo on Computer vision for automated object identification, this session aims to develop further the automation agenda for remote sensing image processing. We can see a willingness to engage with such approaches, often through applying techniques borrowed from other disciplines (such as medical imaging, face recognition, surveillance and security, social media) and the increasing power of computer vision techniques and machine learning approaches. Amongst the many available methods that include tools based on (to name a few) deep convolutional networks (CNN), object-based image analysis, cognitive reasoning, self-learning algorithms and adaptive template matching there are convincing applications that overcome the earlier limitations of spectral and object-based methods and that they can enable the recognition of landscape patterns/objects produced by the near-unlimited assortment of forms, dimensions and spectral properties that mark soil-concealed anthropogenic remains. This session offers a forum for practitioners of these new techniques, welcoming presentations on theory, experiences, and projects related to the theme of automatic object identification in archaeological remote sensing. We aim to highlight prospects and opportunities of the discipline, and discuss challenges ahead, regardless of the employed image datasets. Ideally, it also meant to bring together archaeologists and experts from other disciplines presenting methods that can be applied to the archaeological domain. Themes of interests include, but are not restricted to: ● pattern recognition and pattern matching ● object-based image analysis ● deep convolutional networks (CNN) ● mathematical morphology ● emerging methods from other disciplines
