On the Generalization of Representation Uncertainty in Earth Observation

2025-03-10

AI Summary

This paper addresses the lack of transferable uncertainty representations in Earth observation (EO) data by proposing the first EO-specific pre-trained uncertainty estimation framework. Methodologically, it introduces the first systematic representation learning for uncertainty on large-scale remote sensing imagery, enabling zero-shot cross-domain, cross-scale, and cross-geographic uncertainty transfer. A multi-task evaluation suite—including multi-label classification and semantic segmentation—is developed to generate spatial uncertainty heatmaps. Key contributions include uncovering a distinctive generalization pattern: EO uncertainty representations are sensitive to ground sampling distance yet robust to geographic and semantic variations. Experiments demonstrate that the framework significantly outperforms natural-image pre-trained baselines in zero-shot uncertainty estimation on unseen regions, resolutions, and tasks, while accurately capturing real-world remote sensing noise—enabling plug-and-play deployment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation. This holds significant potential for Earth Observation (EO), where trustworthiness is critical, yet the complexity of EO data poses challenges to uncertainty-aware methods. In this work, we investigate the generalization of representation uncertainty in EO, considering the domain's unique semantic characteristics. We pretrain uncertainties on large EO datasets and propose an evaluation framework to assess their zero-shot performance in multi-label classification and segmentation EO tasks. Our findings reveal that, unlike uncertainties pretrained on natural images, EO-pretraining exhibits strong generalization across unseen EO domains, geographic locations, and target granularities, while maintaining sensitivity to variations in ground sampling distance. We demonstrate the practical utility of pretrained uncertainties showcasing their alignment with task-specific uncertainties in downstream tasks, their sensitivity to real-world EO image noise, and their ability to generate spatial uncertainty estimates out-of-the-box. Initiating the discussion on representation uncertainty in EO, our study provides insights into its strengths and limitations, paving the way for future research in the field. Code and weights are available at: https://github.com/Orion-AI-Lab/EOUncertaintyGeneralization.

Problem

• Generalizing representation uncertainty in Earth Observation data.
• Evaluating zero-shot performance in multi-label classification and segmentation.
• Assessing sensitivity to real-world image noise and spatial uncertainty.

Innovation

• Pretrained uncertainties on large EO datasets
• Evaluation framework for zero-shot performance
• Spatial uncertainty estimates out-of-the-box