Machine learning (ML) for Earth Observation (EO) data is revolutionizing the scale and scope at which science and policy can operate — filling critical data gaps across fields such as ecology and development economics. In this talk, I will present a class of ML for EO models that distill global satellite data into compact, multi-purpose representations of the Earth. I’ll trace the recent evolution of these “Earth embedding models,” from early image embeddings designed to capture the unique characteristics of satellite imagery, to an emerging class of location encoders that serve as implicit neural representations of EO data. The talk will conclude with a discussion of open research challenges and possible paths forward for the AI / ML research community.
Esther Rolf is an assistant professor of computer science at the University of Colorado, Boulder. Esther's research in statistical and geospatial machine learning blends methodological and applied techniques to study and design machine learning algorithms and systems with an emphasis on usability, data-efficiency and fairness. Her lab's research spans developing algorithms and infrastructure for reliable environmental monitoring using machine learning, responsible and fair algorithm design and use, and the influence of data acquisition and representation on the efficacy and applicability of machine learning systems.
Prior to her position at CU Boulder, Esther was a postdoctoral fellow with the Harvard Data Science Initiative and the Center for Research on Computation and Society. Esther completed her PhD in Computer Science in 2022 at UC Berkeley, where she was advised by Ben Recht and Michael I. Jordan. Esther’s PhD was supported by an NSF Graduate Research Fellowship, a Google Research Fellowship, and a UC Berkeley Stonebreaker Fellowship.
Esther has won best paper awards at ICML (2018) and the Workshop on AI for Social Good at Neurips (2019). The impact of her work has been recognized with a SDG Digital Gamechangers award (2023) from the United Nations Development Programme and the International Telecommunication Union.