In this work, we introduce a new dataset of productive fields based on agriculture machine’s operation combined with Sentinel-2 images collected over a period of time, enabling to training highly accurate prediction models that automatically detect productive crop fields.

In this work, we propose a weakly supervised approach for classification scenarios where only small labeled sets are available. It exploits hypergraph structures based on ranking information to identify and select reliable similarity relations between pairs of labeled-unlabeled data.

In this work, we propose the new problem of low-budget label query, which aims at maximizing the classification performance by selecting a convenient and small set of samples (i.e., low budget) to be manually labeled from an arbitrary big set of unlabeled data.