Find ’em all: Large-scale automation to detect complex archaeological objects with Deep Learning – A case study on English hillforts

Abstract

Nowadays, archaeologists have vast amounts of Light Detection and Ranging (LiDAR) and other remote sensing data at their disposal, to search for previously undiscovered archaeological objects, often at a national scale. This leads to a Big Data problem in archaeology: some degree of automation is needed, as humans alone cannot cope with these ever-growing data sources. In this research, we have developed a novel workflow based on the Artificial Intelligence (AI) technology of Convolutional Neural Networks (CNNs), to automate the detection of unknown, complex archaeological objects. Our hypothesis is that a high-quality remote sensing data source such as LiDAR and a curated list of known objects, is sufficient to find a large number — or ideally all — additional undiscovered objects within a landscape. In a case study presented here, we use Prehistoric hillforts in England as an example for this workflow and present a three-step approach to demonstrate its efficiency.