Julien Osman

Ingénieur développement logiciels. Docteur en télédétection.

Formation


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Initial training

PhD in remote sensing

2011 - 2015 - University Paul Sabatier, Toulouse (France)

Master's degree in Signal Image Speech Telecom

2008 - École Nationale Supérieure d'Ingénieurs Électriciens de Grenoble, Grenoble (France)

Engineer's degree in Signal Image Communication Multimédia

2005 - 2008 - École Nationale Supérieure d'Ingénieurs Électriciens de Grenoble, Grenoble (France)

PhD thesis

Expert knowledge and model for the exploitation of Earth observation images with high spatial, spectral and temporal resolutions.

Summary

The future Earth observation space missions, Venµs and Sentinel (1 and 2), will provide us with a flow of data unseen in terms of spatial, spectral and temporal resolution. To use these data efficiently for the generation of land cover maps or change detection, we need fast, robust approaches that require as little supervision as possible. For instance, a concrete use of these data could be the identification, as early as May, of the area growing corn in all the South-West part of France. Or obtaining a monthly land cover map, in a slight delay, on large areas.

Images alone don't allow us to reach such goals. Nevertheless, other information is available, which hasn't been really used. The main goal of this thesis is to identify available prior information, evaluate its relevance, and introduce it in preexisting processing chains to assess its contribution.

We focused on agriculture monitoring. The information we used is knowledge on farming practices (crop rotations, irrigation, crop class alternation, etc) and the size and the topography of the fields.

We mainly worked with 2 sources of prior knowledge:

  • Knowledge contained in databases such as the Registre Parcellaire Graphique (RPG). We used data mining methods to extract it.
  • Knowledge provided by experts. We modeled it with 1st order logic rules.

One contribution of this thesis is the selection and assessment of a tool allowing us to extract and process information in a way that we can introduce it efficiently in preexisting classification algorithms: Markov Logic. Markov Logic is a statistical tool able to work with both information from databases and information modeled with logic rules.

We show that using these data increases the quality of the land cover maps. We also show that this information allows us to obtain real time maps, whose quality increases with the arrival of new information.

As a conclusion of this thesis work, we provide outlooks for applying the same methodology to other areas, such as the monitoring of tropical forests and generic land cover mapping.

Jury

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