Abstract
Abstract. 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.
Highlights
Data accessibility and interoperability are key for multinational cross-border applications and fundamental for economic development (European Parliament and the Council, 2007)
To develop an INSPIRE-compliant artificial intelligence (AI) application to map landslide susceptibility, we needed to extend the INSPIRE Natural Risk Zone theme to include the concept of landslide susceptibility and the specific code list dealing with landslide terminology
The Natural Hazard classification code list was extended (Minerva Intelligence, 2019b) to include a classification of various landslide types using the updated Varnes landslide classification (Hungr et al, 2014), which is a landslide classification widely adopted within the scientific community, and a new code list of landslide size classes (Minerva Intelligence, 2019c) based on Jakob (2005)
Summary
Data accessibility and interoperability are key for multinational cross-border applications and fundamental for economic development (European Parliament and the Council, 2007). Different countries have different languages and data standards, hindering infrastructure planning, disaster risk reduction initiatives, and effective legislative implementation. To overcome these challenges, the European Union initiated INSPIRE (Infrastructure for Spatial Information in the European Community; Directive 2007/2/EC; European Parliament and the Council, 2007). INSPIRE is structured in 34 spatial data themes organized in three annexes. The themes span administrative (e.g. street addresses) and environmental domains (e.g. geology), and all EU countries are mandated by law to have implemented the data framework by 2021 (European Parliament and the Council, 2014).
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