The flood of archaeological remote sensing data in present times calls for digital solutions which can reduce the time and cost required to manually analyse these vast amounts of data. In recent...Show moreThe flood of archaeological remote sensing data in present times calls for digital solutions which can reduce the time and cost required to manually analyse these vast amounts of data. In recent times, Deep Learning techniques based on Covolutional Neural Networks for auto- mated detection of archaeological objects, are fast gaining traction due to the potential they hold. However, much of these studies remain re- stricted to detection of discrete objects with uniform morphologies. Thus, there lies a gap in the use of these methodologies for mapping of larger archaeological systems, which can contribute immensely to landscape archaeology, and our knowledge of human cultural activity in the past. This thesis attempts to make this shift by using a CNN-based in- stance segmentation methodology to detect individual plots of large Celtic Field systems. It was implemented on LiDAR data from the Veluwe region in the Netherlands, using the Mask R-CNN model. The results show that the methodology has the ability to not only detect field plots present in the landscape, but also outline their exact shape. These results when embedded in a wider framework can con- tribute greatly to archaeological prospection and our understanding of the archaeological landscape in the Veluwe.Show less
