  This paper presents an interactive approach for multi-class segmentation of
aerial images. Precisely, it is based on a deep neural network which exploits
both RGB images and annotations. Starting from an initial output based on the
image only, our network then interactively refines this segmentation map using
a concatenation of the image and user annotations. Importantly, user
annotations modify the inputs of the network - not its weights - enabling a
fast and smooth process. Through experiments on two public aerial datasets, we
show that user annotations are extremely rewarding: each click corrects roughly
5000 pixels. We analyze the impact of different aspects of our framework such
as the representation of the annotations, the volume of training data or the
network architecture. Code is available at https://github.com/delair-ai/DISIR.
