  We propose in this article to build up a collaboration between a deep neural
network and a human in the loop to swiftly obtain accurate segmentation maps of
remote sensing images. In a nutshell, the agent iteratively interacts with the
network to correct its initially flawed predictions. Concretely, these
interactions are annotations representing the semantic labels. Our
methodological contribution is twofold. First, we propose two interactive
learning schemes to integrate user inputs into deep neural networks. The first
one concatenates the annotations with the other network's inputs. The second
one uses the annotations as a sparse ground-truth to retrain the network.
Second, we propose an active learning strategy to guide the user towards the
most relevant areas to annotate. To this purpose, we compare different
state-of-the-art acquisition functions to evaluate the neural network
uncertainty such as ConfidNet, entropy or ODIN. Through experiments on three
remote sensing datasets, we show the effectiveness of the proposed methods.
Notably, we show that active learning based on uncertainty estimation enables
to quickly lead the user towards mistakes and that it is thus relevant to guide
the user interventions.
