Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness in Fennoscandia with Confidence
Creators: name: Emam, Ahmed
name: Farag, Mohamed
name: Roscher, Ribana
Publisher: name: Forschungzentrum Jülich
Year: 2023

description: Northern Lights Deep Learning Conference, Tromso, Norway, 9 Jan 2024 - 12 Jan 2024
description: Unaffected by extensive human interference, protected natural areas represent regions of the Earth that maintain their original condition, largely untouched by urbanization, agriculture, logging, and other human activities. These regions host rich biodiversity and offer numerous ecological advantages. They provide unique opportunities to study natural ecosystem processes, such as water and pollination cycles.Consequently, careful mapping and monitoring of these areas are crucial for uncovering intricate geo-ecological patterns essential for preserving their authenticity. This explains the increasing focus on monitoring and comprehending natural areas in both remote sensing and environmental research. Satellite imagery enables consistent observation of remote protected areas, surpassing human accessibility challenges. It offers efficient, cost-effective data collection while minimizing disturbances to delicate ecosystems. Utilizing Machine Learning (ML) models, particularly Convolutional Neural Networks (CNNs), enables precise classification of natural regions by analyzing satellite imagery datasets. To illustrate, Ekim et al. construct a dataset and a foundational CNN model that precisely classifies and categorizes these protected natural regions. In their research analyzing naturalness, Stomberg et al. designed an inherently explanatory classification network that generates attribution maps. These maps effectively highlight patterns indicative of protected natural areas in satellite imagery. [] also introduce an approach that generates images with highlighted naturalness patterns utilizing Activation Maximization and Generative Adversarial Networks (GANs). This approach provides comprehensive and valid explanations for the authenticity of naturalness. Nevertheless, while these methods effectively identify designating patterns that characterize the authenticity of natural regions, they face challenges in offering a quantitative metric that precisely represents the contribution of these discerning patterns. Additionally, these methods do not tackle the issue of uncertainty associated with the assigned importance of each individual pattern.To overcome these limitations, we introduce an innovative approach that integrates explainability and uncertainty quantification. Our aim is to establish a novel metric that captures both the significance and confidence associated with each pattern. Notably, our contributions extend to developing certainty-aware segmentation masks. These masks not only yield precise segmentation outcomes but also pinpoint pixels where the model showcases high uncertainty. The cornerstone of our work is the development of the Confident Naturalness Explanation (CNE) metric. This metric is utilized to prioritize and arrange the contributing patterns based on their inherent quality, thereby deepening our grasp of the concept of naturalness in satellite imagery. Through these breakthroughs, we collectively amplify the interpretability and confidence levels of the model's insights. As a result, we facilitate a more comprehensive understanding of the intricate naturalness patterns inherent in satellite imagery. The CNE framework consists of two main parts, the explainability and the uncertainty quantification part. In the first part, we use a grey box approach to assign an importance value to each pattern contributing to the concept of naturalness. The grey box approach consists of a black box semantic segmentation model that lacks interpretability and a transparent model that is responsible for explaining the decision-making mechanism of the black box model. In the second part, we utilize the MC-Dropout technique to quantify the uncertainty in predicting the classes contributing to naturalness. We integrate the gained knowledge to create the CNE metric, which assigns confident importance to the patterns forming naturalness in Fennoscandia. Our investigation unveiled that various wetland patterns possess notably high CNE metric values, ranging from 0.8 to 1. These scores signify the existence of high-quality patterns that significantly contribute to the concept of naturalness with high certainty. Wetlands are pivotal ecosystems renowned for their roles in carbon storage, safeguarding biodiversity, regulating water resources, and providing niches for unique plant and animal species finely adapted to their specific surroundings.In contrast, Glaciers, Grasslands, and water bodies exhibit relatively low-quality patterns, with an approximate metric value of 0.2. These values indicate patterns with a diminished contribution to the naturalness concept, accompanied by heightened uncertainty.