  Satellite data has the potential to inspire a seismic shift for machine
learning -- one in which we rethink existing practices designed for traditional
data modalities. As machine learning for satellite data (SatML) gains traction
for its real-world impact, our field is at a crossroads. We can either continue
applying ill-suited approaches, or we can initiate a new research agenda that
centers around the unique characteristics and challenges of satellite data.
This position paper argues that satellite data constitutes a distinct modality
for machine learning research and that we must recognize it as such to advance
the quality and impact of SatML research across theory, methods, and
deployment. We outline critical discussion questions and actionable suggestions
to transform SatML from merely an intriguing application area to a dedicated
research discipline that helps move the needle on big challenges for machine
learning and society.
