AI RESEARCH
Hide and Seek: Investigating Redundancy in Earth Observation Imagery
arXiv CS.AI
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ArXi:2603.13524v1 Announce Type: cross 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 pronounced impact on the domain and its applications than what current literature reflects.