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https://doi.org/10.5194/nhess-2020-134
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-134
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 27 May 2020

Submitted as: research article | 27 May 2020

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This preprint is currently under review for the journal NHESS.

INSPIRE standards as framework for artificial intelligence applications: a landslides example

Gioachino Roberti1, Jacob McGregor1, Sharon Lam1, David Bigelow1, Blake Boyko1, Chris Ahern1, Victoria Wang1, Bryan Barnhart1, Clinton Smyth1, David Poole1,2, and Stephen Richard1 Gioachino Roberti et al.
  • 1Minerva Intelligence Inc., 301 – 850 West Hastings Street, Vancouver, British Columbia, Canada V6C 1E1
  • 2Computer Science department, University of British Columbia, Vancouver, V6T 1Z4, Canada

Abstract. This study presents a landslide susceptibility map using an artificial intelligence (AI) approach that is based on standards set by the INSPIRE framework. We show how INSPIRE standards enhance the interoperability of geospatial data, and enable deeper knowledge development for their interpretation and explainability in AI applications. 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. Despite the theoretical effectiveness of the SDI, very few real-world applications make use of INSPIRE standards. We designed an ontology of landslides, embedded with INSPIRE vocabularies and then aligned geology, stream network and land cover data sets 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 slopes likely to generate landslides as well as map polygons representing real slopes. 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 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 beyond. Further, this application facilitates the explainability of the generated results. We conclude that public and private organisations, within and outside the European Union, can enhance the value of their data by bringing them into INSPIRE-compliance for use in AI applications.

Gioachino Roberti et al.

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Gioachino Roberti et al.

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Short summary
We show how INSPIRE, the European initiative to standardize data across borders, can be used to produce explainable AI-based applications. We do so by producing landslide susceptibility maps for the Veneto region, Italy. EU countries are mandated by law to have implemented the INSPIRE data framework by 2021 but they are aligning and serving INSPIRE data at a slow pace. Our paper can provide a boost to INSPIRE implementation as it shows the value of standardized data.
We show how INSPIRE, the European initiative to standardize data across borders, can be used to...
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