Institut national de l’information géographique et forestière (IGN), Saint-Mandé (near Paris), France
Posted: July 6, 2018
The primary objective of this thesis is to use deep recurrent neural networks to classify each parcel and to develop an architecture well-adapted to the multiple time scale structuring agricultural time-series. Anonymized tax returns from previous years ensure that a large quantity of high-quality annotated data is available, as is suited for training such deep networks.
A keen graduate student with basic knowledge of machine learning and computer programming (python, c++ and deep learning frameworks such as PyTorch a plus). Familiarity with deep learning and remote sensing preferable, but not required. Must have a good level of english is required, French optional.