Universal digital twin: land use

This article develops an ontological description of land use and applies it to incorporate geospatial information describing land coverage into a knowledge-graph-based Universal Digital Twin. Sources of data relating to land use in the UK have been surveyed. The Crop Map of England (CROME) is produc...

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Main Authors: Akroyd, Jethro, Harper, Zachary, Soutar, David, Farazi, Feroz, Bhave, Amit, Mosbach, Sebastian, Kraft, Markus
Other Authors: School of Chemical and Biomedical Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/164177
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1641772023-12-29T06:50:25Z Universal digital twin: land use Akroyd, Jethro Harper, Zachary Soutar, David Farazi, Feroz Bhave, Amit Mosbach, Sebastian Kraft, Markus School of Chemical and Biomedical Engineering Cambridge Centre for Advanced Research and Education in Singapore (CARES) Engineering::Computer science and engineering Crop Map Digital Twin This article develops an ontological description of land use and applies it to incorporate geospatial information describing land coverage into a knowledge-graph-based Universal Digital Twin. Sources of data relating to land use in the UK have been surveyed. The Crop Map of England (CROME) is produced annually by the UK Government and was identified as a valuable source of open data. Formal ontologies to represent land use and the geospatial data arising from such surveys have been developed. The ontologies have been deployed using a high-performance graph database. A customized vocabulary was developed to extend the geospatial capabilities of the graph database to support the CROME data. The integration of the CROME data into the Universal Digital Twin is demonstrated in two use cases that show the potential of the Universal Digital Twin to share data across sectors. The first use case combines data about land use with a geospatial analysis of scenarios for energy provision. The second illustrates how the Universal Digital Twin could use the land use data to support the cross-domain analysis of flood risk. Opportunities for the extension and enrichment of the ontologies, and further development of the Universal Digital Twin are discussed. National Research Foundation (NRF) Published version This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Part of the research was also funded by Data-Centric Engineering e3-23 https://doi.org/10.1017/dce.2021.21 Published online by Cambridge University Press the European Commission, Horizon 2020 Programme, DOME 4.0 Project, GA 953163. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Part of this work was supported by Towards Turing 2.0 under the EPSRC Grant EP/W037211/1 & The Alan Turing Institute. 2023-01-09T01:55:29Z 2023-01-09T01:55:29Z 2022 Journal Article Akroyd, J., Harper, Z., Soutar, D., Farazi, F., Bhave, A., Mosbach, S. & Kraft, M. (2022). Universal digital twin: land use. Data-Centric Engineering, 3(1), e3-1-e3-28. https://dx.doi.org/10.1017/dce.2021.21 2632-6736 https://hdl.handle.net/10356/164177 10.1017/dce.2021.21 2-s2.0-85124689417 1 3 e3-1 e3-28 en Data-Centric Engineering © The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Crop Map
Digital Twin
spellingShingle Engineering::Computer science and engineering
Crop Map
Digital Twin
Akroyd, Jethro
Harper, Zachary
Soutar, David
Farazi, Feroz
Bhave, Amit
Mosbach, Sebastian
Kraft, Markus
Universal digital twin: land use
description This article develops an ontological description of land use and applies it to incorporate geospatial information describing land coverage into a knowledge-graph-based Universal Digital Twin. Sources of data relating to land use in the UK have been surveyed. The Crop Map of England (CROME) is produced annually by the UK Government and was identified as a valuable source of open data. Formal ontologies to represent land use and the geospatial data arising from such surveys have been developed. The ontologies have been deployed using a high-performance graph database. A customized vocabulary was developed to extend the geospatial capabilities of the graph database to support the CROME data. The integration of the CROME data into the Universal Digital Twin is demonstrated in two use cases that show the potential of the Universal Digital Twin to share data across sectors. The first use case combines data about land use with a geospatial analysis of scenarios for energy provision. The second illustrates how the Universal Digital Twin could use the land use data to support the cross-domain analysis of flood risk. Opportunities for the extension and enrichment of the ontologies, and further development of the Universal Digital Twin are discussed.
author2 School of Chemical and Biomedical Engineering
author_facet School of Chemical and Biomedical Engineering
Akroyd, Jethro
Harper, Zachary
Soutar, David
Farazi, Feroz
Bhave, Amit
Mosbach, Sebastian
Kraft, Markus
format Article
author Akroyd, Jethro
Harper, Zachary
Soutar, David
Farazi, Feroz
Bhave, Amit
Mosbach, Sebastian
Kraft, Markus
author_sort Akroyd, Jethro
title Universal digital twin: land use
title_short Universal digital twin: land use
title_full Universal digital twin: land use
title_fullStr Universal digital twin: land use
title_full_unstemmed Universal digital twin: land use
title_sort universal digital twin: land use
publishDate 2023
url https://hdl.handle.net/10356/164177
_version_ 1787136681214738432