Universal Digital Twin: Land use

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-02-10 DOI:10.1017/dce.2021.21
J. Akroyd, Zachary S. Harper, David Soutar, Feroz Farazi, Amita Bhave, S. Mosbach, M. Kraft
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引用次数: 10

Abstract

Abstract 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.
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环球数字孪生:土地利用
摘要本文开发了一种土地利用的本体论描述,并将其应用于将描述土地覆盖的地理空间信息纳入基于知识图的通用数字孪生中。对英国土地使用相关数据来源进行了调查。英格兰作物地图(CROME)由英国政府每年编制一次,被认为是开放数据的宝贵来源。已经开发了表示土地利用和此类调查产生的地理空间数据的正式本体论。本体已经使用高性能的图数据库进行了部署。开发了一个自定义词汇表,以扩展图形数据库的地理空间功能,从而支持CROME数据。CROME数据与通用数字孪生的集成在两个用例中得到了证明,这两个用例显示了通用数字孪生在跨行业共享数据的潜力。第一个用例将土地使用数据与能源供应情景的地理空间分析相结合。第二个例子说明了通用数字孪生如何利用土地利用数据来支持洪水风险的跨领域分析。讨论了本体论的扩展和丰富以及通用数字孪生的进一步发展的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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