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

2020 ◽  
Author(s):  
Gioachino Roberti ◽  
Jacob McGregor ◽  
Sharon Lam ◽  
David Bigelow ◽  
Blake Boyko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Semantic Networks ◽  
Scientific Models ◽  
Susceptibility Map ◽  
The European Union ◽  
Stream Network ◽  
Data Infrastructure

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.

scholarly journals INSPIRE standards as a framework for artificial intelligence applications: a landslide example

2020 ◽  
Vol 20 (12) ◽  
pp. 3455-3483
Author(s):  
Gioachino Roberti ◽  
Jacob McGregor ◽  
Sharon Lam ◽  
David Bigelow ◽  
Blake Boyko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Land Surface ◽  
Spatial Information ◽  
Semantic Networks ◽  
Scientific Models ◽  
Susceptibility Map ◽  
The European Union

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.

scholarly journals LANDSLIDE SUSCEPTIBILITY ASSESSMENT THROUGH FUZZY LOGIC INFERENCE SYSTEM (FLIS)

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 355-360 ◽  
Author(s):  
T. Bibi ◽  
Y. Gul ◽  
A. Abdul Rahman ◽  
M. Riaz
Keyword(s):  
Fuzzy Logic ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Hazard Mapping ◽  
Complex Nature ◽  
Susceptibility Map ◽  
Likelihood Ratios ◽  
Regular Feature ◽  
Inference System

Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.

scholarly journals Artificial intelligence, Digital Single Market and the proposal of a right to fair and reasonable inferences: a legal issue between ethics and techniques

2019 ◽  
Vol 5 (2) ◽  
pp. 75-91
Author(s):  
Alexandre Veronese ◽  
Alessandra Silveira ◽  
Amanda Nunes Lopes Espiñeira Lemos
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Data Protection ◽  
Personal Data ◽  
Legal Framework ◽  
Single Market ◽  
Legal Issue ◽  
The European Union ◽  
European Union Law ◽  
General Data Protection Regulation

The article discusses the ethical and technical consequences of Artificial intelligence (hereinafter, A.I) applications and their usage of the European Union data protection legal framework to enable citizens to defend themselves against them. This goal is under the larger European Union Digital Single Market policy, which has concerns about how this subject correlates with personal data protection. The article has four sections. The first one introduces the main issue by describing the importance of AI applications in the contemporary world scenario. The second one describes some fundamental concepts about AI. The third section has an analysis of the ongoing policies for AI in the European Union and the Council of Europe proposal about ethics applicable to AI in the judicial systems. The fourth section is the conclusion, which debates the current legal mechanisms for citizens protection against fully automated decisions, based on European Union Law and in particular the General Data Protection Regulation. The conclusion will be that European Union Law is still under construction when it comes to providing effective protection to its citizens against automated inferences that are unfair or unreasonable.

The European Union Artificial Intelligence Act: Potential implications for Australia

2021 ◽  
pp. 1037969X2110523
Author(s):  
Dan Svantesson
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Early Stage ◽  
The European Union ◽  
Key Features ◽  
Proposed Regulation

The European Union (EU) published its proposed Regulation laying down harmonised rules for Artificial Intelligence (the Artificial Intelligence Act) on 21 April 2021. Once it comes into force, this Act will impact upon Australia. It is therefore important that Australians take note of the proposal at this relatively early stage. This article brings attention to the key features of the EU’s proposed Artificial Intelligence Act. However, the main aim is to highlight why it is important for Australia and to examine, in some detail, the rules that will determine when the Act applies to Australians.

scholarly journals Triangulation Applied to the Intra-European Union Tomato Market

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jaime De Pablo Valenciano ◽  
José Antonio Torres Arriaza ◽  
Juan Uribe-Toril ◽  
José Luis Ruiz-Real
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Foreign Trade ◽  
Trade Flows ◽  
Strong Impact ◽  
The European Union ◽  
Input Output ◽  
Specific Product ◽  
Fresh Tomato ◽  
Interdependent Relationships

An understanding of the intracommunity trade is essential for the agents involved in the fresh tomato market (farmers, entrepreneurs, public administrations, and consumers). The purpose of this paper is to analyze the interdependent relationships between exporting and importing countries within the European Union for a specific product such as fresh tomatoes and thus understand which have been the key countries in three specific years (2002–2007–2017). The methodology used to study the interrelationships of trade flows in the countries of the European Union (EU) is that of triangulation by means of the Leontief input-output model. Artificial intelligence techniques are used to process and triangulate the data based on pathfinding techniques using a cost function. The triangulation results have created a hierarchy of countries (suppliers and customers). This type of methodology has not been applied to the field of foreign trade. The results show that Netherlands and Spain are key countries in intracommunity trade as they have a strong impact both with regard to their exports and their imports and are fundamental when analyzing the growth of specific sectors and how they are able to stimulate the economies of other countries.

scholarly journals Neural Networks Usage in the Evaluation of European Union Cofinanced Projects

2010 ◽  
Vol 2 (1) ◽  
pp. 7-20 ◽  
Author(s):  
Tadeusz Grzeszczyk
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
European Union ◽  
Evaluation Methods ◽  
The European Union ◽  
Crucial Element ◽  
Computing Technologies ◽  
Modern Computing

Neural Networks Usage in the Evaluation of European Union Cofinanced Projects Research concerns the implementation of modern computing technologies in the evaluation of projects cofinanced by the European Union. Crucial element of this research is the enrichment of currently used evaluation methods with modern mechanisms basing on artificial intelligence. The article deals with the possibility analysis of neural networks usage in such applications.

scholarly journals Artificial intelligence approaches for spatial prediction of landslides in mountainous regions of western India

2021 ◽  
Author(s):  
PANKAJ PRASAD ◽  
Victor Joseph Loveson ◽  
Sumit Das ◽  
Priyankar Chandra
Keyword(s):  
Artificial Intelligence ◽  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Gradient Boosting ◽  
Susceptibility Map ◽  
Western India ◽  
Boosted Regression Tree ◽  
Study Region ◽  
Mountainous Regions ◽  
Stochastic Gradient Boosting

Abstract The prediction of landslide is a complex task but preparing the landslide susceptibility map through artificial intelligence approaches can reduce life loss and damages resulting from landslides. The purpose of this study is to evaluate and compare the landslide susceptibility mapping (LSM) using six machine learning models, including random forest (RF), deep boost (DB), stochastic gradient boosting (SGB), rotation forest (RoF), boosted regression tree (BRT), and logit boost (LB) in the mountainous regions of western India. The landslide inventory map consisting of 184 landslide locations has been divided into two groups for training (70% dataset) and validation (30% dataset) purposes. Fourteen landslide triggering factors including slope, topographical roughness index, road density, topographical wetness index, elevation, slope length, drainage density, stream power index, geomorphology, rainfall, soil, lithology, lineament density, and normalized difference vegetation index have been considered using the boruta approach for the LSM. The results reveal that the RF model has the highest precision in terms of AUC (0.88; 0.89), kappa (0.62; 0.50), accuracy (0.81; 0.77), and specificity (0.86; 0.86) both in the study region and secondary region, respectively. Hence, it can be concluded that the RF is an effective and promising technique as compared to DB, SGB, RoF, BRT, and LB for landslide susceptibility assessment in the research area as well as in regions having similar geo-environmental configuration.

scholarly journals Mākslīgais intelekts un nākotnes regulējuma dilemma

2021 ◽  
pp. 174-182
Author(s):  
Ritvars Purmalis ◽  
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Daily Life ◽  
Civil Liability ◽  
Industrial Sector ◽  
The European Union ◽  
Further Development ◽  
Insight Into ◽  
Artificial Intelligence Systems ◽  
Operational Capacity

Digital innovations such as artificial intelligence systems, although limited in their current operational capacity, can be considered to be part of our daily life. Various ways in which these systems are implemented into day-to-day aspects directly affect not only the further development of the industrial sector but the society as a whole. The purpose of this article is to provide a brief insight into the current situation and the various initiatives of the European Union institutions in relation to the methodology for the application of civil liability in the case of damage caused by artificial intelligence systems, as well as to assess the content of future regulatory framework that has been published by the European Parliament, with whom it is intended to establish a common methodology throughout the European Union for the application of civil liability regime, if the damage is caused by artificial intelligence systems.

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