  Space situational awareness typically makes use of physical measurements from
radar, telescopes, and other assets to monitor satellites and other spacecraft
for operational, navigational, and defense purposes. In this work we explore
using textual input for the space situational awareness task. We construct a
corpus of 48.5k news articles spanning all known active satellites between 2009
and 2020. Using a dependency-rule-based extraction system designed to target
three high-impact events -- spacecraft launches, failures, and
decommissionings, we identify 1,787 space-event sentences that are then
annotated by humans with 15.9k labels for event slots. We empirically
demonstrate a state-of-the-art neural extraction system achieves an overall F1
between 53 and 91 per slot for event extraction in this low-resource,
high-impact domain.
