001021075 001__ 1021075
001021075 005__ 20240226075331.0
001021075 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-00529
001021075 037__ $$aFZJ-2024-00529
001021075 1001_ $$0P:(DE-HGF)0$$aEmam, Ahmed$$b0$$eCorresponding author
001021075 1112_ $$aNorthern Lights Deep Learning Conference$$cTromso$$d2024-01-09 - 2024-01-12$$wNorway
001021075 245__ $$aConfident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness in Fennoscandia with Confidence
001021075 260__ $$c2023
001021075 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1705384384_14266
001021075 3367_ $$033$$2EndNote$$aConference Paper
001021075 3367_ $$2BibTeX$$aINPROCEEDINGS
001021075 3367_ $$2DRIVER$$aconferenceObject
001021075 3367_ $$2DataCite$$aOutput Types/Conference Abstract
001021075 3367_ $$2ORCID$$aOTHER
001021075 520__ $$aUnaffected 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.
001021075 536__ $$0G:(DE-HGF)POF4-2171$$a2171 - Biological and environmental resources for sustainable use (POF4-217)$$cPOF4-217$$fPOF IV$$x0
001021075 536__ $$0G:(GEPRIS)458156377$$aDFG project 458156377 - MapInWild: Kartierung und Interpretation von Wildnis aus dem Weltraum (458156377)$$c458156377$$x1
001021075 7001_ $$0P:(DE-HGF)0$$aFarag, Mohamed$$b1
001021075 7001_ $$0P:(DE-Juel1)195965$$aRoscher, Ribana$$b2$$ufzj
001021075 8564_ $$uhttps://juser.fz-juelich.de/record/1021075/files/emam2024_confident_naturalness_abstract.pdf$$yOpenAccess
001021075 8564_ $$uhttps://juser.fz-juelich.de/record/1021075/files/emam2024_confident_naturalness_abstract.gif?subformat=icon$$xicon$$yOpenAccess
001021075 8564_ $$uhttps://juser.fz-juelich.de/record/1021075/files/emam2024_confident_naturalness_abstract.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001021075 8564_ $$uhttps://juser.fz-juelich.de/record/1021075/files/emam2024_confident_naturalness_abstract.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001021075 8564_ $$uhttps://juser.fz-juelich.de/record/1021075/files/emam2024_confident_naturalness_abstract.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001021075 909CO $$ooai:juser.fz-juelich.de:1021075$$pdriver$$pVDB$$popen_access$$popenaire
001021075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)195965$$aForschungszentrum Jülich$$b2$$kFZJ
001021075 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2171$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
001021075 9141_ $$y2023
001021075 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001021075 9201_ $$0I:(DE-Juel1)IBG-2-20101118$$kIBG-2$$lPflanzenwissenschaften$$x0
001021075 9801_ $$aFullTexts
001021075 980__ $$aabstract
001021075 980__ $$aVDB
001021075 980__ $$aUNRESTRICTED
001021075 980__ $$aI:(DE-Juel1)IBG-2-20101118