Scaling Remote Sensing Foundation Models: Data Domain Tradeoffs at the Peta-Scale

Charith Wickrema; Eliza Mace; Hunter Brown; Heidys Cabrera; Nick Krall; Matthew O'Neill; Shivangi Sarkar; Lowell Weissman; Eric Hughes; Guido Zarrella

arXiv · 2512.23903

Scaling Remote Sensing Foundation Models: Data Domain Tradeoffs at the Peta-Scale

Charith Wickrema·Eliza Mace·Hunter Brown·Heidys Cabrera·Nick Krall·Matthew O'Neill·Shivangi Sarkar·Lowell Weissman·Eric Hughes·Guido Zarrella

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Abstract

We explore the scaling behaviors of artificial intelligence to establish practical techniques for training foundation models on high-resolution electro-optical (EO) datasets that exceed the current state-of-the-art scale by orders of magnitude. Modern multimodal machine learning (ML) applications, such as generative artificial intelligence (GenAI) systems for image captioning, search, and reasoning, depend on robust, domain-specialized encoders for non-text modalities. In natural image domains where internet-scale data is plentiful, well-established scaling laws help optimize the joint scaling of model capacity, training compute, and dataset size. Unfortunately, these relationships are much less well understood in high-value domains like remote sensing (RS). Using over a quadrillion pixels of commercial satellite EO data and MITRE's Federal AI Sandbox, we train progressively larger vision transformer (ViT) backbones, report successes and failure modes observed at peta-scale, and analyze implications for bridging domain gaps across additional RS modalities. We observe that even at this scale, performance is consistent with a data-limited regime rather than a model parameter-limited one. These practical insights are intended to inform data collection strategies, compute budgets, and optimization schedules that advance the future development of frontier scale RS foundation models.

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Charith Wickrema, Eliza Mace, Hunter Brown, Heidys Cabrera, Nick Krall, Matthew O'Neill, Shivangi Sarkar, Lowell Weissman, Eric Hughes, Guido Zarrella. 2026-01-14. Scaling Remote Sensing Foundation Models: Data Domain Tradeoffs at the Peta-Scale. https://arxiv.org/abs/2512.23903

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