INSPIRE standards as a framework for artificial intelligence applications: a landslide example
| Autor: | Victoria Wang, Bryan Barnhart, Blake Boyko, Sharon Lam, David Poole, Jacob McGregor, Gioachino Roberti, Christopher Ahern, Stephen M. Richard, Clinton Smyth, David Bigelow |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Geospatial analysis
010504 meteorology & atmospheric sciences Interoperability Ontology (information science) 010502 geochemistry & geophysics computer.software_genre Scientific modelling 01 natural sciences lcsh:TD1-1066 media_common.cataloged_instance European union lcsh:Environmental technology. Sanitary engineering lcsh:Environmental sciences 0105 earth and related environmental sciences media_common lcsh:GE1-350 Spatial data infrastructure lcsh:QE1-996.5 lcsh:Geography. Anthropology. Recreation Landslide Data science lcsh:Geology lcsh:G General Earth and Planetary Sciences Applications of artificial intelligence computer |
| Zdroj: | Natural Hazards and Earth System Sciences, Vol 20, Pp 3455-3483 (2020) |
| ISSN: | 1684-9981 1561-8633 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|