Towards Streaming Continual Learning for Earth Observation Multimodal Foundation Models

Earth Observation (EO) systems generate continuous multimodal data streams at unprecedented scales. However, in this context, the literature offers solutions based on foundation models that operate within static training paradigms, which limit their effectiveness. Trained once on historical datasets and deployed without further learning, these models face critical issues when confronted with the dynamic nature of the environment, which includes emerging phenomena, sensor degradation, and evolving environmental patterns. This vision paper identifies three fundamental gaps: (1) the absence of memory-efficient anti-forgetting mechanisms at the foundation scale, (2) static cross-modal fusion strategies that cannot adapt to changing observational contexts, and (3) temporal representations that fail to distinguish cyclical patterns from distributional drift. Addressing these limitations requires convergence of foundation models, Continual Learning, and Streaming Machine Learning. This work envisions three key research directions: efficient model updating through selective replay and parameter regularization, explicit drift detection mechanisms, and context-dependent fusion strategies. These directions aim to enable EO systems that continuously learn from terabyte-per-day satellite streams while maintaining transfer learning capabilities and computational feasibility essential for operational deployment.