Opportunities for machine learning and artificial intelligence in a national mapping agency: a perspective on enhancing ordnance survey workflow
Opportunities for machine learning and artificial intelligence in a national mapping agency: a perspective on enhancing ordnance survey workflow
National Mapping agencies (NMA) are tasked with providing highly accurate geospatial data for a range of customers. This challenge has traditionally been met by combining remote sensing data gathering, field work and manual interpretation and processing of the data. This is a significant logistical undertaking which requires novel approaches to improve potential feature extraction from the available data. Using research undertaken at Great Britain’sNMA, Ordnance Survey (OS)as an example, this paper provides an overview of recent advances in the use of artificial intelligence (AI)to assist in improving feature classification from remotely sensed aerial imagery, describing research using high level neural network architecture to image classification that utilisesconvolutional neural network learning.
International Society for Photogrammetry and Remote Sensing
Murray, Jon
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Sargent, Isabel
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Holland, David
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Gardiner, A.
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Dionysopoulou, Kyriaki
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Coupland, S.
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Hare, Jonathon
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Atkinson, P
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Murray, Jon
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Sargent, Isabel
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Holland, David
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Gardiner, A.
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Dionysopoulou, Kyriaki
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Coupland, S.
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Hare, Jonathon
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Atkinson, P
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Murray, Jon, Sargent, Isabel, Holland, David, Gardiner, A., Dionysopoulou, Kyriaki, Coupland, S., Hare, Jonathon and Atkinson, P
(2019)
Opportunities for machine learning and artificial intelligence in a national mapping agency: a perspective on enhancing ordnance survey workflow.
In XXIV ISPRS Congress.
vol. XXIV,
International Society for Photogrammetry and Remote Sensing.
4 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
National Mapping agencies (NMA) are tasked with providing highly accurate geospatial data for a range of customers. This challenge has traditionally been met by combining remote sensing data gathering, field work and manual interpretation and processing of the data. This is a significant logistical undertaking which requires novel approaches to improve potential feature extraction from the available data. Using research undertaken at Great Britain’sNMA, Ordnance Survey (OS)as an example, this paper provides an overview of recent advances in the use of artificial intelligence (AI)to assist in improving feature classification from remotely sensed aerial imagery, describing research using high level neural network architecture to image classification that utilisesconvolutional neural network learning.
Text
XXIV_ISPRSCongress_Abstract_301_REV_JM1
- Accepted Manuscript
More information
Accepted/In Press date: 2 March 2019
Venue - Dates:
XXIV ISPRS Congress, , Nice, France, 2021-07-04 - 2021-07-10
Identifiers
Local EPrints ID: 440719
URI: http://eprints.soton.ac.uk/id/eprint/440719
PURE UUID: 15414a0b-29d2-4ca7-bf26-420b4adf1c93
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Date deposited: 14 May 2020 16:31
Last modified: 17 Mar 2024 03:05
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Contributors
Author:
Jon Murray
Author:
Isabel Sargent
Author:
David Holland
Author:
A. Gardiner
Author:
Kyriaki Dionysopoulou
Author:
S. Coupland
Author:
Jonathon Hare
Author:
P Atkinson
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