Transfer learning is a powerful way to adapt existing deep learning models to new emerging use-cases in remote sensing.
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.
Convolutional neural networks are currently the state-of-the-art algorithms for many remote sensing applications such as semantic segmentation or object detection.
Dense pixel-wise classification maps output by deep neural networks are of extreme importance for scene understanding.
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.