Exploring Wilderness Characteristics Using Explainable Machine Learning in Satellite Imagery

Wildernessareasofferimportantecologicalandsocialbenefitsand thereareurgentreasonstodiscoverwheretheirpositivecharacter-istics 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. Our approach advances explainable machine learning for remote sensing, offers opportunities for comprehensive analyses of existing wilderness, and has practical relevance for conservation efforts.