A future perspective for automated detection of archaeology using deep learning with remote sensor data
A future perspective for automated detection of archaeology using deep learning with remote sensor data
An essential aspect of archaeology is the protection of sites from looters, extensive agriculture and erosion. Under this constant threat of destruction, it is of utmost importance that sites are located so that they can be monitored and protected. This is mostly done on the ground or by using remote sensing data such as aerial images or LiDAR derived elevation models. This task is time consuming and requires highly specialised and experienced people and would thus immensely benefit from automation. Within this novel research, the potential of deep learning for the detection of archaeological sites is being assessed.
Deep Learning, Archaeology, Remote Sensing
Kramer, Iris, Caroline
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Hare, Jonathon
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Prugel-Bennett, Adam
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September 2017
Kramer, Iris, Caroline
ba56efc0-ce81-4897-9d74-4bec3e9e1fca
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Kramer, Iris, Caroline, Hare, Jonathon and Prugel-Bennett, Adam
(2017)
A future perspective for automated detection of archaeology using deep learning with remote sensor data.
ACM WomENcourage : Celebration of Women in Computing, Universitat Politecnica de Catalunya, Barcelona, Spain.
06 - 08 Sep 2017.
Record type:
Conference or Workshop Item
(Poster)
Abstract
An essential aspect of archaeology is the protection of sites from looters, extensive agriculture and erosion. Under this constant threat of destruction, it is of utmost importance that sites are located so that they can be monitored and protected. This is mostly done on the ground or by using remote sensing data such as aerial images or LiDAR derived elevation models. This task is time consuming and requires highly specialised and experienced people and would thus immensely benefit from automation. Within this novel research, the potential of deep learning for the detection of archaeological sites is being assessed.
Text
IK-womENcourage2017-A0
More information
Published date: September 2017
Venue - Dates:
ACM WomENcourage : Celebration of Women in Computing, Universitat Politecnica de Catalunya, Barcelona, Spain, 2017-09-06 - 2017-09-08
Keywords:
Deep Learning, Archaeology, Remote Sensing
Identifiers
Local EPrints ID: 416400
URI: http://eprints.soton.ac.uk/id/eprint/416400
PURE UUID: a5be73ee-fcd9-4262-b63c-427feaa56e67
Catalogue record
Date deposited: 15 Dec 2017 17:30
Last modified: 16 Mar 2024 03:50
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Contributors
Author:
Iris, Caroline Kramer
Author:
Jonathon Hare
Author:
Adam Prugel-Bennett
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