  We test this premise and explore representation spaces from a single deep
convolutional network and their visualization to argue for a novel unified
feature extraction framework. The objective is to utilize and re-purpose
trained feature extractors without the need for network retraining on three
remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and
semantic based image visualization. By leveraging the same convolutional
feature extractors and viewing them as visual information extractors that
encode different image representation spaces, we demonstrate a preliminary
inductive transfer learning potential on multiscale experiments that
incorporate edge-level details up to semantic-level information.
