Project

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TRACES

English title TRACES
Applicant Dao Hy
Number 205720
Funding scheme Project funding
Research institution Département de géographie et environnement Université de Genève
Institution of higher education University of Geneva - GE
Main discipline Information Technology
Start/End 01.01.2022 - 30.06.2025
Approved amount 316'392.00
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All Disciplines (2)

Discipline
Information Technology
Social geography and ecology

Keywords (14)

knowledge graph; ontologies; signal processing; information retrieval; semantic web; digital libraries; environmental trajectories; spatial analysis; statistical data processing; big data; web and information systems; data fusion; machine learning; agend-based models

Lay Summary (French)

Lead
L'observation des trajectoires environnementales des territoires est essentielle pour analyser leur évolution en termes de conservation de la nature ou de résilience au changement climatique, et pour élaborer les politiques publiques en matière d'environnement et d'aménagement du territoire.
Lay summary

Contenu et objectifs du travail de recherche

Le projet TRACES vise à permettre la modélisation et l'analyse des trajectoires environnementales des territoires (c’est-à-dire les profils de changements environnementaux), en s'appuyant sur trois domaines de l'Intelligence Artificielle : la représentation des connaissances, l’apprentissage automatique et les systèmes multi-agents. Une trajectoire environnementale sera définie par un territoire d'étude, une période d'observation, et un ensemble d'indicateurs caractérisant cette trajectoire qui seront quantifiés par des séries temporelles de données statistiques et géospatiales provenant de sources officielles ouvertes et accessibles. Les cas étudiés par le projet TRACES concerneront trois espaces en France, en Suisse et en zone transfrontalière franco-suisse.

L’analyse des trajectoires environnementales des territoires sera basée sur une approche centrée sur les graphes de connaissances et le Web sémantique, des moyens de représenter des connaissances sous une forme informatique standardisée et de les rendre accessibles et exploitables sur internet.

Contexte scientifique et social du projet de recherche

Les résultats du projet TRACES fourniront une base de connaissances, des méthodes et des outils d'aide à la décision pour les décideurs et les professionnels en charge de la gestion des territoires et de l’environnement, mais aussi utiles pour informer les citoyens vivant dans les territoires étudiés en France et en Suisse.

Direct link to Lay Summary Last update: 08.11.2021

Responsible applicant and co-applicants

Employees

Abstract

Observing the environmental trajectories of territories is essential for analysing their evolution in terms of nature conservation or resilience to climate change, and for developing future public policies in terms of the environment and land use planning. The TRACES project aims to enable the modelling and analysis of the environmental trajectories of territories, based on three areas of Artificial Intelligence. An environmental trajectory will be defined by a study territory, an observation period, and a set of indicators characterising this trajectory, which is multidimensional (space, time, theme) and multi-granular in nature. Several types of environmental trajectories will be proposed and considered. The indicator data used by the TRACES project will come from open and accessible official sources. The approach proposed here is centred on Knowledge Graphs (KGs) and the Semantic Web. Thus, in a first step, the TRACES project will generate, in the form of KGs, semantic environmental trajectories of territories (SETTs). These KGs will be developed from an ontological model of trajectories based on standard vocabularies, and on data extracted from a spatial data infrastructure dedicated to environmental indicators. The environmental observations that make up the trajectories and form the SETTs will be described in semantised data cubes, linked together over time. Web 3.0 standards will be used so that the SETTs are published in the Linked Open Data (LOD) Cloud, according to the FAIR (easy to Find, Accessible, Interoperable and Reusable) data principles. The Knowledge Graphs representing SETTs will then be enriched and completed by a targeted search in the open data sets made available by the LOD Cloud. Similarity measures adapted to the multidimensional nature of the SETTs will be proposed and integrated into Machine Learning techniques and algorithms in order to group similar SETTs into clusters, but also to extract frequent patterns, on which a completion and trajectory prediction phase based on recurrent neural networks will rely. Complementarily, a multi-agent system approach will develop explicit, agent-based models capturing the various behaviours of human actors and stakeholders, the dynamics between human actors and the environment and its impact on the environment, and the effects on the environment of policies implemented in the past. These models will be fed by the SETTs Knowledge Graphs, and by the information extracted from the LOD Cloud during their enrichment, as well as by the inferences produced by the Machine Learning algorithms. This knowledge will allow a better understanding of the mechanisms at work in the dynamics of the territories, and to simulate the effects of public policies, already tested or new. These different types of analysis will be completed by an interactive interface allowing the visualisation of these SETTs as graphs, using evolution curves, on maps showing the territorial dynamics, or through spatiotemporal cubes. All of these components will constitute an original and innovative processing chain, exploiting and extending current work on spatiotemporal Knowledge Graphs. The results of the TRACES project will provide a methodological and tool base for decision support for decision-makers and professionals in charge of territorial management, but also useful to inform citizens living in the territories studied. The cases studied by the TRACES project will concern territories in Switzerland, in France and on both sides of the border between the two countries from which the partner institutions of this project come.
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