  Wilderness areas offer important ecological and social benefits and there are
urgent reasons to discover where their positive characteristics and ecological
functions are present and able to flourish. We apply a novel explainable
machine learning technique to satellite images which show wild and
anthropogenic areas in Fennoscandia. Occluding certain activations in an
interpretable artificial neural network we complete a comprehensive sensitivity
analysis regarding wild and anthropogenic characteristics. This enables us to
predict detailed and high-resolution sensitivity maps highlighting these
characteristics. Our artificial neural network provides an interpretable
activation space increasing confidence in our method. Within the activation
space, regions are semantically arranged. Our approach advances explainable
machine learning for remote sensing, offers opportunities for comprehensive
analyses of existing wilderness, and has practical relevance for conservation
efforts.
