Exploring Wilderness Characteristics Using Explainable Machine Learning in Satellite Imagery
This work addresses the need for better wilderness conservation efforts by providing interpretable tools for remote sensing analysis, though it is incremental in advancing explainable machine learning for this domain.
The researchers tackled the problem of identifying wilderness characteristics in satellite imagery by applying a novel explainable machine learning technique to Fennoscandia, resulting in the generation of detailed high-resolution sensitivity maps that highlight wild and anthropogenic features.
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.