Predicting the Needle in a Petabyte Scale Haystack: Open-Vocabulary Event Anticipation in Satellite Imagery

Satellite constellations now image the Earth daily, capturing the lifecycle of events from deforestation to urban construction. Anticipating these events before completion enables timely intervention, yet existing systems cannot jointly identify *where* a change occurs, *what* it will become, and *when* it will finish across an open vocabulary. Building such a model requires diverse event data, but events are like needles in a haystack, making manual annotation at global scale infeasible. We address this problem by representing change as the vector difference between vision-language model embeddings at distinct time points. This arithmetic approach allows us to search for semantic transformations directly in the latent space, enabling (1) an automated data engine for global event discovery without manual annotation and (2) the largest global satellite event dataset to our knowledge, comprising 21,000+ locations across 64+ event types with ~2M images from PlanetScope and Sentinel-2 spanning six continents, to train (3) a novel global-scale event anticipation model. Our model detects change with a 93.5% max F1 score, outperforms baselines 3.6$\times$ on predicted event retrieval, and forecasts completion dates with a 3-day median error, even nine months before completion.