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Protected natural areas are regions that have been minimally affected byhuman activities such as urbanization, agriculture, and other humaninterventions. To better understand and map the naturalness of these areas,machine learning models can be used to analyze satellite imagery. Specifically,explainable machine learning methods show promise in uncovering patterns thatcontribute to the concept of naturalness within these protected environments.Additionally, addressing the uncertainty inherent in machine learning models iscrucial for a comprehensive understanding of this concept. However, existingapproaches have limitations. They either fail to provide explanations that areboth valid and objective or struggle to offer a quantitative metric thataccurately measures the contribution of specific patterns to naturalness, alongwith the associated confidence. In this paper, we propose a novel frameworkcalled the Confident Naturalness Explanation (CNE) framework. This frameworkcombines explainable machine learning and uncertainty quantification to assessand explain naturalness. We introduce a new quantitative metric that describesthe confident contribution of patterns to the concept of naturalness.Furthermore, we generate an uncertainty-aware segmentation mask for each inputsample, highlighting areas where the model lacks knowledge. To demonstrate theeffectiveness of our framework, we apply it to a study site in Fennoscandiausing two open-source satellite datasets.

Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness