Satellite images track the changing human footprint on territories, including specific changing features around major infrastructures like harbours or airports. The amount of images and their resolution is continuously increasing.
Unfortunately the size of the teams of analysts assessing the features changes in structured reports (e.g. following the STANAG structuration) remains most of the time constant.
The emergence of Convolutional Deep Neural Networks in AI is an opportunity to partly solve this issue. Automated annotations in image and automated production of structured reports has been recently proposed in the litterature through static AI.
The original approach of SALTO is to address this issue by designing new active learning algorithms which optimize the global analyst annotation budget through an optimal selection of the areas to be annotated.
SALTO will moreover provide an entrusting mechanism for coalitions of analysts sharing the same active learning model. In practice SALTO will provide a prototype of secure active learning implementation which will allow to a pool of analyst to annotate 4 times more data than without SALTO.