Confidence-Filtered Relevance (CFR): An Interpretable and Uncertainty-Aware Machine Learning Framework for Naturalness Assessment in Satellite Imagery

• URL: http://arxiv.org/abs/2507.13034v1
• Date: Thu, 17 Jul 2025 12:06:08 GMT
• Title: Confidence-Filtered Relevance (CFR): An Interpretable and Uncertainty-Aware Machine Learning Framework for Naturalness Assessment in Satellite Imagery
• Authors: Ahmed Emam, Ribana Roscher,
• Abstract summary: Confidence-Filtered Relevance (CFR) is a data-centric framework that combines LRP Attention Rollout with Deep Deterministic Uncertainty estimation.<n>CFR partitions the dataset into subsets based on uncertainty thresholds, enabling systematic analysis of how uncertainty shapes explanations of naturalness in satellite imagery.<n>As uncertainty increases, the interpretability of relevance heatmaps declines and their entropy grows, indicating less selective and more ambiguous attributions.
• Score: 3.846084066763095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Protected natural areas play a vital role in ecological balance and ecosystem services. Monitoring these regions at scale using satellite imagery and machine learning is promising, but current methods often lack interpretability and uncertainty-awareness, and do not address how uncertainty affects naturalness assessment. In contrast, we propose Confidence-Filtered Relevance (CFR), a data-centric framework that combines LRP Attention Rollout with Deep Deterministic Uncertainty (DDU) estimation to analyze how model uncertainty influences the interpretability of relevance heatmaps. CFR partitions the dataset into subsets based on uncertainty thresholds, enabling systematic analysis of how uncertainty shapes the explanations of naturalness in satellite imagery. Applied to the AnthroProtect dataset, CFR assigned higher relevance to shrublands, forests, and wetlands, aligning with other research on naturalness assessment. Moreover, our analysis shows that as uncertainty increases, the interpretability of these relevance heatmaps declines and their entropy grows, indicating less selective and more ambiguous attributions. CFR provides a data-centric approach to assess the relevance of patterns to naturalness in satellite imagery based on their associated certainty.

Related papers

• Enhancing Uncertainty Quantification for Runtime Safety Assurance Using Causal Risk Analysis and Operational Design Domain [0.0]
  We propose an enhancement of traditional uncertainty quantification by explicitly incorporating environmental conditions.<n>We leverage Hazard Analysis and Risk Assessment (HARA) and fault tree modeling to identify critical operational conditions affecting system functionality.<n>At runtime, this BN is instantiated using real-time environmental observations to infer a probabilistic distribution over the safety estimation.
  arXiv  Detail & Related papers  (2025-07-04T12:12:32Z)
• On the Generalization of Representation Uncertainty in Earth Observation [18.74462879840487]
  We investigate the generalization of representation uncertainty in Earth Observation (EO) data.<n>Unlike uncertainties pretrained on natural images, EO-pretraining exhibits strong generalization across unseen EO domains, geographic locations, and target granularities.<n>Initiating the discussion on representation uncertainty in EO, our study provides insights into its strengths and limitations.
  arXiv  Detail & Related papers  (2025-03-10T09:04:50Z)
• Uncertainty Representation in a SOTIF-Related Use Case with Dempster-Shafer Theory for LiDAR Sensor-Based Object Detection [0.0]
  Uncertainty in LiDAR sensor-based object detection arises from environmental variability and sensor performance limitations.<n>Dempster-Shafer Theory (DST) is employed to construct a Frame of Discernment (FoD) to represent detection outcomes.<n>Yager's Rule of Combination is used to resolve conflicting evidence from multiple sources.
  arXiv  Detail & Related papers  (2025-03-03T22:13:51Z)
• Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching [61.73532883992135]
  We propose a new uncertainty-aware stereo matching framework.<n>We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty.
  arXiv  Detail & Related papers  (2024-12-24T23:28:20Z)
• Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference [5.120567378386614]
  We propose an evidential semantic mapping framework, which can enhance reliability in perceptually challenging off-road environments. By adaptively handling semantic uncertainties, the proposed framework constructs robust representations of the surroundings even in previously unseen environments.
  arXiv  Detail & Related papers  (2024-03-21T05:13:34Z)
• Evaluating Probabilistic Classifiers: The Triptych [62.997667081978825]
  We propose and study a triptych of diagnostic graphics that focus on distinct and complementary aspects of forecast performance. The reliability diagram addresses calibration, the receiver operating characteristic (ROC) curve diagnoses discrimination ability, and the Murphy diagram visualizes overall predictive performance and value.
  arXiv  Detail & Related papers  (2023-01-25T19:35:23Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models [84.18625250574853]
  We investigate the ability of KalmanNet, a proposed hybrid model-based deep state tracking algorithm, to estimate an uncertainty measure. We show that the error covariance matrix can be computed based on its internal features, as an uncertainty measure. We demonstrate that when the system dynamics are known, KalmanNet-which learns its mapping from data without access to the statistics-provides uncertainty similar to that provided by the Kalman filter.
  arXiv  Detail & Related papers  (2021-10-10T08:52:18Z)
• Joint Dermatological Lesion Classification and Confidence Modeling with Uncertainty Estimation [23.817227116949958]
  We propose an overall framework that jointly considers dermatological classification and uncertainty estimation together. The estimated confidence of each feature to avoid uncertain feature and undesirable shift is pooled from confidence network. We demonstrate the potential of the proposed approach in two state-of-the-art dermoscopic datasets.
  arXiv  Detail & Related papers  (2021-07-19T11:54:37Z)
• Approaching Neural Network Uncertainty Realism [53.308409014122816]
  Quantifying or at least upper-bounding uncertainties is vital for safety-critical systems such as autonomous vehicles. We evaluate uncertainty realism -- a strict quality criterion -- with a Mahalanobis distance-based statistical test. We adopt it to the automotive domain and show that it significantly improves uncertainty realism compared to a plain encoder-decoder model.
  arXiv  Detail & Related papers  (2021-01-08T11:56:12Z)
• Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning [70.01650994156797]
  Off- evaluation of sequential decision policies from observational data is necessary in batch reinforcement learning such as education healthcare. We develop an approach that estimates the bounds of a given policy. We prove convergence to the sharp bounds as we collect more confounded data.
  arXiv  Detail & Related papers  (2020-02-11T16:18:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.