INSPIRE standards as a framework for artificial intelligence applications: a landslide example
<p>This study presents a landslide susceptibility map using an artificial intelligence (AI) approach based on standards set by the INSPIRE (Infrastructure for Spatial Information in the European Community) framework. INSPIRE is a European Union spatial data infrastructure (SDI) initiative to s...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-12-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/20/3455/2020/nhess-20-3455-2020.pdf |
| _version_ | 1818385500834103296 |
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| author | G. Roberti J. McGregor S. Lam D. Bigelow B. Boyko C. Ahern V. Wang B. Barnhart C. Smyth D. Poole D. Poole S. Richard |
| author_facet | G. Roberti J. McGregor S. Lam D. Bigelow B. Boyko C. Ahern V. Wang B. Barnhart C. Smyth D. Poole D. Poole S. Richard |
| author_sort | G. Roberti |
| collection | DOAJ |
| description | <p>This study presents a landslide susceptibility map using an artificial intelligence (AI) approach based on standards set by the INSPIRE (Infrastructure for Spatial Information in the European Community) framework. INSPIRE is a European Union spatial data infrastructure (SDI) initiative to standardize spatial data across borders to ensure interoperability for management of cross-border infrastructure and environmental issues. However, despite the theoretical effectiveness of the SDI, few real-world applications make use of INSPIRE standards. In this study, we show how INSPIRE standards enhance the interoperability of geospatial data and enable deeper knowledge development for their interpretation and explainability in AI applications. We designed an ontology of landslides, embedded with INSPIRE vocabularies, and then aligned geology, stream network, and land cover datasets covering the Veneto region of Italy to the standards. INSPIRE was formally extended to include an extensive landslide type code list, a landslide size code list, and the concept of landslide susceptibility to describe map application inputs and outputs. Using the terms in the ontology, we defined conceptual scientific models of areas likely to generate different types of landslides as well as map polygons representing the land surface. Both landslide models and map polygons were encoded as semantic networks and, by qualitative probabilistic comparison between the two, a similarity score was assigned. The score was then used as a proxy for landslide susceptibility and displayed in a web map application. The use of INSPIRE-standardized vocabularies in ontologies that express scientific models promotes the adoption of the standards across the European Union and globally. Further, this application facilitates the explanation of the generated results. We conclude that public and private organizations, within and outside the European Union, can enhance the value of their data by making them INSPIRE-compliant for use in AI applications.</p> |
| first_indexed | 2024-12-14T03:39:09Z |
| format | Article |
| id | doaj.art-9645d5dc98aa4650b59e5606f2afaa9c |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-12-14T03:39:09Z |
| publishDate | 2020-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-9645d5dc98aa4650b59e5606f2afaa9c2022-12-21T23:18:31ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812020-12-01203455348310.5194/nhess-20-3455-2020INSPIRE standards as a framework for artificial intelligence applications: a landslide exampleG. Roberti0J. McGregorS. LamD. BigelowB. BoykoC. AhernV. WangB. BarnhartC. SmythD. Poole1D. Poole2S. Richard3Minerva Intelligence Inc., 301–850 West Hastings Street, Vancouver, British Columbia, Canada V6C 1E1Minerva Intelligence Inc., 301–850 West Hastings Street, Vancouver, British Columbia, Canada V6C 1E1Computer Science department, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4Minerva Intelligence Inc., 301–850 West Hastings Street, Vancouver, British Columbia, Canada V6C 1E1<p>This study presents a landslide susceptibility map using an artificial intelligence (AI) approach based on standards set by the INSPIRE (Infrastructure for Spatial Information in the European Community) framework. INSPIRE is a European Union spatial data infrastructure (SDI) initiative to standardize spatial data across borders to ensure interoperability for management of cross-border infrastructure and environmental issues. However, despite the theoretical effectiveness of the SDI, few real-world applications make use of INSPIRE standards. In this study, we show how INSPIRE standards enhance the interoperability of geospatial data and enable deeper knowledge development for their interpretation and explainability in AI applications. We designed an ontology of landslides, embedded with INSPIRE vocabularies, and then aligned geology, stream network, and land cover datasets covering the Veneto region of Italy to the standards. INSPIRE was formally extended to include an extensive landslide type code list, a landslide size code list, and the concept of landslide susceptibility to describe map application inputs and outputs. Using the terms in the ontology, we defined conceptual scientific models of areas likely to generate different types of landslides as well as map polygons representing the land surface. Both landslide models and map polygons were encoded as semantic networks and, by qualitative probabilistic comparison between the two, a similarity score was assigned. The score was then used as a proxy for landslide susceptibility and displayed in a web map application. The use of INSPIRE-standardized vocabularies in ontologies that express scientific models promotes the adoption of the standards across the European Union and globally. Further, this application facilitates the explanation of the generated results. We conclude that public and private organizations, within and outside the European Union, can enhance the value of their data by making them INSPIRE-compliant for use in AI applications.</p>https://nhess.copernicus.org/articles/20/3455/2020/nhess-20-3455-2020.pdf |
| spellingShingle | G. Roberti J. McGregor S. Lam D. Bigelow B. Boyko C. Ahern V. Wang B. Barnhart C. Smyth D. Poole D. Poole S. Richard INSPIRE standards as a framework for artificial intelligence applications: a landslide example Natural Hazards and Earth System Sciences |
| title | INSPIRE standards as a framework for artificial intelligence applications: a landslide example |
| title_full | INSPIRE standards as a framework for artificial intelligence applications: a landslide example |
| title_fullStr | INSPIRE standards as a framework for artificial intelligence applications: a landslide example |
| title_full_unstemmed | INSPIRE standards as a framework for artificial intelligence applications: a landslide example |
| title_short | INSPIRE standards as a framework for artificial intelligence applications: a landslide example |
| title_sort | inspire standards as a framework for artificial intelligence applications a landslide example |
| url | https://nhess.copernicus.org/articles/20/3455/2020/nhess-20-3455-2020.pdf |
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