Exploring Wilderness Characteristics Using Explainable Machine Learning in Satellite Imagery

TL;DR

This paper introduces an explainable neural network approach to analyze satellite imagery, enabling detailed sensitivity maps that distinguish wilderness from anthropogenic areas, thereby supporting conservation and ecological studies.

Contribution

It presents a novel, interpretable neural network method for high-resolution wilderness analysis in satellite images, advancing explainable machine learning in remote sensing.

Findings

Generated high-resolution sensitivity maps of wilderness characteristics.

Demonstrated the interpretability of neural network activation spaces.

Enhanced confidence in remote sensing analysis for conservation.

Abstract

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.