Hide and Seek: Investigating Redundancy in Earth Observation Imagery

2026-03-13

AI Summary

This study addresses the pervasive yet long-overlooked multidimensional redundancy—spanning spectral, temporal, spatial, and semantic dimensions—in Earth observation imagery, which has constrained model efficiency and scalability. The work systematically demonstrates for the first time that such redundancy is an intrinsic property of the data rather than an artifact of specific experimental setups. By integrating domain knowledge from computer vision and remote sensing, the authors conduct comprehensive experiments across diverse tasks, sensors, geographic locations, and model architectures. Their findings reveal that strategically leveraging this redundancy enables nearly a fourfold reduction in computational cost while sacrificing only approximately 1.5% in performance, thereby substantially enhancing both training and inference efficiency.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Dimensionality Reduction/Feature Selection
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

Abstract

The growing availability of Earth Observation (EO) data and recent advances in Computer Vision have driven rapid progress in machine learning for EO, producing domain-specific models at ever-increasing scales. Yet this progress risks overlooking fundamental properties of EO data that distinguish it from other domains. We argue that EO data exhibit a multidimensional redundancy (spectral, temporal, spatial, and semantic) which has a more pronounced impact on the domain and its applications than what current literature reflects. To validate this hypothesis, we conduct a systematic domain-specific investigation examining the existence, consistency, and practical implications of this phenomenon across key dimensions of EO variability. Our findings confirm that redundancy in EO data is both substantial and pervasive: exploiting it yields comparable performance ($\approx98.5\%$ of baseline) at a fraction of the computational cost ($\approx4\times$ fewer GFLOPs), at both training and inference. Crucially, these gains are consistent across tasks, geospatial locations, sensors, ground sampling distances, and architectural designs; suggesting that multi-faceted redundancy is a structural property of EO data rather than an artifact of specific experimental choices. These results lay the groundwork for more efficient, scalable, and accessible large-scale EO models.

Problem

• Earth Observation
• redundancy
• spectral
• temporal
• spatial

Innovation

• multidimensional redundancy
• Earth Observation
• computational efficiency
• domain-specific modeling
• scalable EO models