Earth Embeddings: Harnessing the Information in Earth Observation Data with Machine Learning

Machine learning (ML) for Earth Observation (EO) data is revolutionizing the speed and scope at which science and policy can operate - filling critical data gaps across fields such as ecology and development economics. Helping fuel this progress is a class of ML for EO models that distill global satellite data into compact, multi-purpose representations of the Earth. These "Earth embedding"models include image embeddings designed to capture the unique characteristics of satellite imagery and an emerging class of location encoders that serve as implicit neural representations of Earth's data. These models are already unlocking new use cases and capabilities, with much research yet to be explored.