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Space situational awareness typically makes use of physical measurements fromradar, telescopes, and other assets to monitor satellites and other spacecraftfor operational, navigational, and defense purposes. In this work we exploreusing textual input for the space situational awareness task. We construct acorpus of 48.5k news articles spanning all known active satellites between 2009and 2020. Using a dependency-rule-based extraction system designed to targetthree high-impact events -- spacecraft launches, failures, anddecommissionings, we identify 1,787 space-event sentences that are thenannotated by humans with 15.9k labels for event slots. We empiricallydemonstrate a state-of-the-art neural extraction system achieves an overall F1between 53 and 91 per slot for event extraction in this low-resource,high-impact domain.

Extracting Space Situational Awareness Events from News Text