Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness in Fennoscandia with Confidence
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 advantage...
| Main Authors: | , , |
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| Format: | Conference Object |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://juser.fz-juelich.de/record/1021075 https://juser.fz-juelich.de/search?p=id:%22FZJ-2024-00529%22 |
| Summary: | 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. ... |
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