Dino Ienco (INRAE TETIS)
The huge amount of data currently produced by modern earth observation(EO) missions has raised up new challenges for the remote sensing communities. EO sensors are now able to offer (very) high spatial resolution images with revisit time frequencies never achieved before. Additionally, considering successive acquisitions of satellite imagery over the same area, make it possible to organize this data as satellite image time series (SITS), to monitor phenomena over time. In this talk I will give some examples of modern machine learning techniques applied to EO data with applications related to the agricultural and environmental domains as well as connections between the models outputs and their interpretability (lien diaporama)