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

Automated detection of archaeology in the New Forest using deep learning with remote sensor data
Automated detection of archaeology in the New Forest using deep learning with remote sensor data
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.
Kramer, Iris, Caroline
ba56efc0-ce81-4897-9d74-4bec3e9e1fca
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Sargent, Isabel
3df2050d-b24e-4f60-bc6e-8b1fafdb3f5a
Kramer, Iris, Caroline
ba56efc0-ce81-4897-9d74-4bec3e9e1fca
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Sargent, Isabel
3df2050d-b24e-4f60-bc6e-8b1fafdb3f5a

Kramer, Iris, Caroline, Hare, Jonathon, Prugel-Bennett, Adam and Sargent, Isabel (2017) Automated detection of archaeology in the New Forest using deep learning with remote sensor data. New Forest Knowledge Conference 2017: New Forest Historical Research and Archaeology: Who’s doing it?, Lyndhurst, United Kingdom. 27 - 28 Oct 2017.

Record type: Conference or Workshop Item (Other)

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.

Slideshow
New Forest Conference 2017 - Kramer
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More information

Published date: December 2017
Venue - Dates: New Forest Knowledge Conference 2017: New Forest Historical Research and Archaeology: Who’s doing it?, Lyndhurst, United Kingdom, 2017-10-27 - 2017-10-28

Identifiers

Local EPrints ID: 416396
URI: http://eprints.soton.ac.uk/id/eprint/416396
PURE UUID: d9991c2e-f933-4bb5-a6fa-9ffd551ccbe9
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283

Catalogue record

Date deposited: 15 Dec 2017 17:30
Last modified: 14 Mar 2019 01:41

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