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.

翻译：卫星数据有望为机器学习带来根本性变革——促使我们重新审视为传统数据模态设计的现有实践。随着面向卫星数据的机器学习（SatML）因实际影响力而日益受到关注，该领域正面临关键抉择：要么继续沿用不恰当的方法，要么开创一项聚焦于卫星数据独特特征与挑战的新研究议程。本立场论文主张，卫星数据构成机器学习研究的独特模态，必须承认其独特性，方能从理论、方法到部署全面提升SatML研究的质量与影响力。我们提出关键讨论问题及可行建议，旨在将SatML从单纯的应用领域转变为专注的研究学科，助力应对机器学习与社会面临的重大挑战。