The manual analysis of remotely-sensed data is a widespread practice in local and regional scale archaeological research, as well as heritage management. However, the amount of available high... Show moreThe manual analysis of remotely-sensed data is a widespread practice in local and regional scale archaeological research, as well as heritage management. However, the amount of available high-quality, remotely-sensed data is continuously growing at a staggering rate, which creates new challenges to effectively and efficiently analyze these data and find and document the seemingly overwhelming number of potential archaeological objects. Therefore, computer-aided methods for the automated detection of archaeological objects are needed. In this thesis, the development and application of automated detection methods, based on Deep Convolutional Neural Networks, for the detection of multiple classes of archaeological objects in LiDAR data is investigated. Furthermore, the implementation of these methods into archaeological practice and the opportunities of knowledge discovery—on both a quantitative and qualitative level—for landscape or spatial archaeology are explored. Show less
Remote sensing has a long and successful track record of detecting and mapping archaeological traces of human activity in the landscape. Since the early twentieth century, the tools and procedures... Show moreRemote sensing has a long and successful track record of detecting and mapping archaeological traces of human activity in the landscape. Since the early twentieth century, the tools and procedures of aerial archaeology evolved gradually, while earth observation remote sensing experienced major steps of technological and methodological advancements and innovation that today enable the monitoring of the earth’s surface at unprecedented accuracy, resolution and complexity. Much of the remote sensing data acquired in this process potentially holds important information about the location and context of archaeological sites and objects. Archaeology has started to make use of this tremendous potential by developing new approaches for the detection and mapping of archaeological traces based on digital remote sensing data and the associated tools and procedures. This chapter reviews the history, tools, methods, procedures and products of archaeological remote sensing and digital image analysis, emphasising recent trends towards convergence of aerial archaeology and earth observation remote sensing. Show less
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... Show moreThe (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. Show less
The applications of automated object detection in remote sensing archaeology have grown considerably in the last few years. This reading list has been compiled as a contribution to consolidating... Show moreThe applications of automated object detection in remote sensing archaeology have grown considerably in the last few years. This reading list has been compiled as a contribution to consolidating current perspectives at September 2016, and in support of the preceding paper on the broader issues of human and computer vision in archaeological prospection (Traviglia et al.). Show less
Zingman, I.; Saupe, D.; Penatti, O.A.B.; Lambers, K. 2016
We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately... Show moreWe develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance. Show less
