NaT-ReX: Naturalness Assessment with Transformer-Based Reliable Explainability

Ahmed Emam, Mohamed Farag, Marc Rußwurm, Ribana Roscher

Abstract: Protected natural areas, minimally affected by modern human influence, are essential for ecological stability and processes such as water cycles and pollination. A continuous and efficient monitoring of these areas has become increasingly important, and machine learning and satellite imagery offer new opportunities for getting insights and assessing their naturalness. However, current approaches rely on predefined assumptions of naturalness or do not account for model uncertainty, limiting robustness and interpretability of the results. We propose NaT-ReX, a Transformer-based Reliable Explainability framework that integrates explainable machine learning with uncertainty quantification. NaT-ReX highlights areas that are reliably associated with naturalness while discounting regions with higher uncertainty. To support this, we introduce the ReX score, a novel metric to evaluate both pixel-wise relevance to naturalness and land cover classes based on their contributions to naturalness and the associated uncertainty. Our experiments on two satellite datasets—AnthroProtect and MapInWild—demonstrate both qualitative and quantitative insights. Our findings demonstrate that shrublands and wetlands contribute the most to naturalness, while open water and snow-dominated regions exhibit the lowest ReX scores due to higher uncertainty or lower semantic attribution. These findings align with both data-driven and expert-informed assessments of naturalness, highlighting the potential of NaT-ReX as an efficient and uncertainty-aware monitoring and analysis framework.

External IDs:doi:10.1007/978-3-032-12840-9_36