Mdl4eo – groupe de recherche

"Modern Earth Observation systems provide huge amount of data from different sensors at different temporal, spatialand spectral resolutions. Such amount of information is commonly represented by means of multispectral imagery and, due to its complexity, it requires new techniques and method to be correctly exploited to extract valuable knowledge.Recently, data science and, in particular, machine (and deep) learning algorithms have demonstrated their ability to cope with image and signal analysis providing cutting-edge results. Multiple data science challenges were already launched using satellite imagery (i.e. building footprints, road networks, iceberg detection, etc…) but crucial open questions remain unsolved (i.e. biodiversity monitoring, urban mapping, deforestation tracking and food risk prevention, triaging disaster zones, etc..). We are at the beginning of a new era for the analysis of Earth Observation data (EOD) where one of the main question is how to leverage the complementarity and the diversity of the different Earth Observation systems to answer important social challenges and monitor changes on the Earth Surface.The MDL4EO team (Machine and Deep Learning for Earth Observation) at the UMR TETIS (Montpellier, France) has the objective to scientifically contribute to this new era providing AI methods and algorithms to extract valuable knowledge from modern Earth Observation Data. The amount of data being collected by remote sensors is accelerating rapidly and we cannot manage them manually, this is why machine/deep learning lends itself well to remote sensing. More in detail, some of the research questions of the MDL4EO team are the follows:How to intelligently exploit Time Series of Satellite Images to leverage temporal dynamics How to combine/fusion together multi spectral/temporal/resolution/sensor information with the objective to add value to the information thanks to the combination of multi source How to transfer knowledge from different geographical Area: transfer land cover classification model from one site (i.e. France) to another one geographically distant (i.e. Africa). It’s time to fill the gap between Remote Sensing and AI. MDL4EO is working on that direction bringing together different expertises: Data Science, Computer Vision, Machine Learning, Remote Sensing and Geoinformatics."

Point of contact

Organisation name	Individual name	Electronic mail address	Role
UMR TÉTIS	Lenco Dino	dino.lenco@irstea.fr	Principal investigator
UMR TÉTIS	Interdonato Roberto	roberto.interdonato@irstea.fr	Principal investigator
UMR TÉTIS	Ose Kenji	kenji.ose@irstea.fr	Principal investigator
UMR TÉTIS	Cresson Remi	remi.cresson@irstea.fr	Principal investigator
UMR TÉTIS	Gaetano Raffaele	raffaele.gaetano@cirad.fr	Principal investigator
UMR TÉTIS	Pasquet Jérôme	pasquet@lirmm.fr	Principal investigator
UMR TÉTIS	Ho Tong Minh Dinh	dinh.ho-tong-minh@irstea.fr	Principal investigator

Keywords

Machine Learning
Earth Observation

TETIS Thesaurus, version 1.0 21112019

Language: fra

Character set: UTF8

Topic category

Imagery base maps earth cover

OnLine resource

Protocol	Linkage	Name
WWW:LINK-1.0-http--link	https://mdl4eo.irstea.fr/	Description :

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File identifier: e10b9f0a-111c-11ea-8d71-362b9e155668 XML

Metadata language: fra

Character set: UTF8

Hierarchy level: initiative

Date stamp: 2019-12-11T10:45:26

Metadata standard name: ISO 19115:2003/19139

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