  Protected natural areas are regions that have been minimally affected by
human activities such as urbanization, agriculture, and other human
interventions. 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 that
contribute to the concept of naturalness within these protected environments.
Additionally, addressing the uncertainty inherent in machine learning models is
crucial for a comprehensive understanding of this concept. However, existing
approaches have limitations. They either fail to provide explanations that are
both valid and objective or struggle to offer a quantitative metric that
accurately measures the contribution of specific patterns to naturalness, along
with the associated confidence. In this paper, we propose a novel framework
called the Confident Naturalness Explanation (CNE) framework. This framework
combines explainable machine learning and uncertainty quantification to assess
and explain naturalness. We introduce a new quantitative metric that describes
the confident contribution of patterns to the concept of naturalness.
Furthermore, we generate an uncertainty-aware segmentation mask for each input
sample, highlighting areas where the model lacks knowledge. To demonstrate the
effectiveness of our framework, we apply it to a study site in Fennoscandia
using two open-source satellite datasets.
