A huge trend in recent earth observation missions is to target high temporal and spatial resolutions (e.g. SENTINEL-2 mission by ESA). Data resulting from these missions can then be used for fine-grained studies in many applications. In this project we will focus on three key environmental issues : agricultural practices and their impact, forest preservation and air quality monitoring. Based on identified key requirements for these application settings, MATS project will feature a complete rethinking of the literature in machine learning for time series, with a focus on large-scale methods that could operate even when little supervised information is available. In more details, MATS will introduce new paradigms in large-scale time series classification, spatio-temporal modeling and weakly supervised approaches for time series. Proposed methods will cover a wide range of machine learning problems including domain adaptation, clustering, metric learning and (semi-)supervised classification, for which dedicated methodology is lacking when time series data is at stake. Methods developed in the project will be made available to the scientific community as well as to practitioners through an open-source toolbox in order to help dissemination to a wide range of application areas. Moreover, the application settings considered in the project will be used to showcase benefits offered by methodologies developed in MATS in terms of time series analysis.
Accueil > Membres > OSZWALD Johan
OSZWALD Johan
- Fonction : Professeur
- Poste : permanent
- Employeur : Université Rennes2
- Publications sur HAL
- Site perso
Équipe de recherche
Axe Environnements continentaux
Recherche
Programmes internationaux
ANR
Dernières publications sur HAL
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Multidimensional analysis of landscape dynamics in a Central African forest‐savannah mosaic
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UAV-based canopy textures assess changes in forest structure from long-term degradation
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Assessing the ecological vulnerability of forest landscape to agricultural frontier expansion in the Central Highlands of Vietnam
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Dynamique de la déforestation dans la Réserve de biosphère de Yangambi (République démocratique du Congo) : variabilité spatiale et temporelle au cours des 30 dernières années
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Déconstruire la spatialisation de services écosystémiques par la modélisation critique
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Uncertainty in ecosystem services maps: the case of carbon stocks in the Brazilian Amazon forest using regression analysis
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Problématique des plantes envahissantes au sud du Togo (Afrique de l'Ouest) : apport de l'analyse systémique paysagère et de la télédétection
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Biodiversity loss along a gradient of deforestation in Amazonian agricultural landscapes
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Landscape as a complex system to study interactions between human beings and environment: coupling local knowledge and remote sensing to highlight landscape structure and dynamics
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The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest
