Current machine learning methods are not capable of fully analyzing and interacting with satellite remote sensing data, limiting their ability to benefit society through real-world applications. Data collected by Earth-observing satellites differ significantly from image data (e.g., photos taken with a smartphone or digital camera) or text data (e.g., online articles or social media posts). Satellite data captures the Earth’s diverse and dynamic ecosystems, environments, and human activities that are constantly changing. These patterns and changes can be subtle or obvious, large or small, difficult or easy to see with the human eye. Satellites record these patterns in many different wavelengths and sensor types, which hold much more information than the visible color wavelengths humans see. This project will advance fundamental machine learning research methods for analyzing satellite data, unlocking its untapped potential for solving societal challenges including agriculture, conservation, and natural hazards. The project will develop new technologies that improve the performance and accessibility of satellite machine learning models for different applications, thus advancing scientific progress, human and environmental sustainability, and societal welfare. This award will develop (1) a hypermodal geospatial foundation model that accommodates diverse sensor modalities and input formats, (2) a novel algorithm for zero-shot mapping using natural language prompts instead of