Remote sensing satellites capture peta-scale, multi-modal data capturing our dynamic planet across space, time, and spectrum. This rich data source holds immense potential for addressing local and planetary-scale challenges including food insecurity, poverty, climate change, and ecosystem preservation. Fully realizing this potential will require a new paradigm of machine learning approaches capable of tackling the unique character of remote sensing data. Machine learning approaches must be flexible enough to make use of the multi-modal multi-fidelity satellite data, process meter-scale observations over planetary scales, and generalize to the challenging diversity of remote sensing tasks. In this talk, I will present examples of how we are developing machine learning approaches for planetary data processing including self-supervised transformers for remote sensing data. I will also demonstrate how treating ML research and deployment as a unified approach instead of siloed steps leads to research advances that result in immediate societal impact, highlighting examples of how we are partnering directly with stakeholders to deploy our innovations in areas of critical need across the globe.