Automated detection of archaeology in the New Forest using deep learning with remote sensor data

Abstract

As a result of the New Forest Knowledge project, many new sites were discovered. This was partly due to the undertaken LiDAR survey which was followed by an intensive manual process to interpret the results. The research presented in this paper looks at methods to automate this process especially for round barrow detection using deep learning.

Traditionally, automated methods require manual feature engineering to extract the visual appearance of a site on remote sensing data. Whereas this approach is difficult, expensive and bound to detect a single type of site, recent developments have moved towards automated feature learning of which deep learning is the most notable. In our approach, we use known site locations together with LiDAR data and aerial images to train Convolutional Neural Networks (CNNs). This network is typically constructed of many layers with each representing a different filter (e.g. to detect lines or edges). When this network is trained, each new site location that is fed to the network will update the weights of features to better represent the appearance of sites in the remote sensing data. For this learning process, an accurate dataset is required with a lot of examples and therefore the New Forest is a very suitable case study, especially thanks to the extensive research of the New Forest Knowledge project.

In this paper, our latest results will be presented together with a future perspective on how we can scale our approach to a country wide detection method when computing power becomes even more efficient.

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Identifiers

ORCID for Jonathon Hare: orcid.org/0000-0003-2921-4283

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