# Exploring Wilderness Characteristics Using Explainable Machine Learning in Satellite Imagery

arXiv: https://arxiv.org/abs/2203.00379 · HTML: https://mlsanity.com/paper/2203.00379/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing](https://mlsanity.com/paper/2104.01375/) (2021)

## Built on by (most cited first, 4 of 4)

- [Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery](https://mlsanity.com/paper/2311.08923/) (2023)
- [Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness](https://mlsanity.com/paper/2311.08936/) (2023)
- [MapInWild: A Remote Sensing Dataset to Address the Question What Makes Nature Wild](https://mlsanity.com/paper/2212.02265/) (2022)
- [Confidence-Filtered Relevance (CFR): An Interpretable and Uncertainty-Aware Machine Learning Framework for Naturalness Assessment in Satellite Imagery](https://mlsanity.com/paper/2507.13034/) (2025)

## Abstract (arXiv)

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.