Chair objectives

Exploitation and analysis of heterogeneous data are usually conducted thanks to specific methods, generally dedicated to each kind of data, which account for the measurement process and the very nature of the data itself.

Within advanced scenarii of concomitant availability of several yet distinct measurements, fully analyzing these extended datasets requires a unifying framework overcoming a crude and marginal description of a single measurement.

The main objective of this research program is to develop learning algorithms able to extract meaningful information from multi-source, multi-scale and multi-temporal data. Particular applicative contexts deal with remote sensing for Earth observation and automotive systems.

 Programs Acceptable & certifiable AI

 Themes: Learning with little or complex data, AI and physical models

 Chair holder: Nicolas Dobigeon, Toulouse INP

 Senior collaborating researchers

  • Mathieu Fauvel, INRAE
  • Cédric Févotte, CNRS
  • Jodi Inglada, Cnes
  • Thomas Oberlin, ISAE-Supaero
Know more

AI, is it all about data?  

An Exploreur article about Nicolas Dobigeon's work 

Read the article in French


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