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A future perspective on automation in remote sensing

A future perspective on automation in remote sensing
A future perspective on automation in remote sensing
In the UK there is a longstanding tradition for the use of remote sensing to detect archaeological sites. As research in the UK has been mostly pioneering in the field it is surprising that the upcoming automation practises have not been adopted in research nor practise. Automation has been mostly criticised for the lack in accuracy but recent successes in computer science could overturn this. The key factor for this change is deep learning which has already been overwhelmingly successful in other domains such as self-driving cars and medical imagery. This paper will present how the archaeological field could benefit from these techniques in especially large mapping projects.

Examples will be drawn from the ImageLearn project developed by the Ordnance Survey (OS) and the Electronics and Computer Science Department at the University of Southampton. In this project the high resolution aerial imagery and extensive set of labelled data from the OS was successfully used to automatically generate land cover classification. For the next phase of this project archaeological object detection will be studied as it provides a unique opportunity to test this model on some of most ‘overwritten’ signatures within our landscape.
Kramer, Iris, Caroline
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Kramer, Iris, Caroline
ba56efc0-ce81-4897-9d74-4bec3e9e1fca

Kramer, Iris, Caroline (2017) A future perspective on automation in remote sensing. UK Chapter of Computer Applications and Quantitative Methods in Archaeology: Through the looking glass, Winchester, United Kingdom. 04 - 05 Mar 2017.

Record type: Conference or Workshop Item (Other)

Abstract

In the UK there is a longstanding tradition for the use of remote sensing to detect archaeological sites. As research in the UK has been mostly pioneering in the field it is surprising that the upcoming automation practises have not been adopted in research nor practise. Automation has been mostly criticised for the lack in accuracy but recent successes in computer science could overturn this. The key factor for this change is deep learning which has already been overwhelmingly successful in other domains such as self-driving cars and medical imagery. This paper will present how the archaeological field could benefit from these techniques in especially large mapping projects.

Examples will be drawn from the ImageLearn project developed by the Ordnance Survey (OS) and the Electronics and Computer Science Department at the University of Southampton. In this project the high resolution aerial imagery and extensive set of labelled data from the OS was successfully used to automatically generate land cover classification. For the next phase of this project archaeological object detection will be studied as it provides a unique opportunity to test this model on some of most ‘overwritten’ signatures within our landscape.

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Published date: March 2017
Venue - Dates: UK Chapter of Computer Applications and Quantitative Methods in Archaeology: Through the looking glass, Winchester, United Kingdom, 2017-03-04 - 2017-03-05

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Local EPrints ID: 416397
URI: http://eprints.soton.ac.uk/id/eprint/416397
PURE UUID: 0c3e1e66-6f42-44f0-a7ee-484afeafa4e5

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Date deposited: 15 Dec 2017 17:30
Last modified: 13 Mar 2019 19:07

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