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@INPROCEEDINGS{Emam:1021075,
      author       = {Emam, Ahmed and Farag, Mohamed and Roscher, Ribana},
      title        = {{C}onfident {N}aturalness {E}xplanation ({CNE}): {A}
                      {F}ramework to {E}xplain and {A}ssess {P}atterns {F}orming
                      {N}aturalness in {F}ennoscandia with {C}onfidence},
      reportid     = {FZJ-2024-00529},
      year         = {2023},
      abstract     = {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.},
      month         = {Jan},
      date          = {2024-01-09},
      organization  = {Northern Lights Deep Learning
                       Conference, Tromso (Norway), 9 Jan 2024
                       - 12 Jan 2024},
      cin          = {IBG-2},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217) / DFG project 458156377 -
                      MapInWild: Kartierung und Interpretation von Wildnis aus dem
                      Weltraum (458156377)},
      pid          = {G:(DE-HGF)POF4-2171 / G:(GEPRIS)458156377},
      typ          = {PUB:(DE-HGF)1},
      doi          = {10.34734/FZJ-2024-00529},
      url          = {https://juser.fz-juelich.de/record/1021075},
}