Planetary-Scale Inference from Earth Observation and Machine Learning | 9am Tues, January 13, 2025

[Grigory Bronevetsky]

Modeling Talks

Planetary-Scale Inference from Earth Observation and Machine Learning  Sherrie Wang, MIT Tues, Jan 13, 2026 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/planetary-scale-inference-from-earth-observation-and-machine-learning More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: Recent advances in Earth observation and machine learning enable inference about the Earth system at planetary scale. However, real-world applications are constrained by sparse ground truth, heterogeneous sensing conditions, and domain shift across regions. Addressing these challenges requires learning representations that generalize across geography and time, as well as enabling statistically valid inference from machine learning-derived Earth observation products. I will present two case studies, one on learning invariant features for crop type mapping and one on using machine learning-derived Earth observation maps to enable statistically valid downstream inference. Together, these examples demonstrate how combining machine learning with principled use of Earth observation modalities can yield scalable, reliable insights about human and environmental systems.   Bio: Sherrie Wang is an Assistant Professor at MIT in the Department of Mechanical Engineering and the Institute of Data, Systems, and Society. Her research spans Earth observation data, machine learning, and statistical inference, with the goal of enabling reliable understanding of land and atmospheric systems at scale. Her work spans developing and evaluating geospatial data products, designing machine learning algorithms that generalize under data scarcity and domain shift, and performing downstream inference with principled uncertainty quantification. A central theme of her work is understanding how different sensing modalities, such as satellite imagery and LiDAR, interact with learning algorithms to produce representations that transfer across geographic and temporal scales. Her research supports applications in agriculture, greenhouse gas monitoring, and localized weather inference, particularly in settings where ground-based measurements are limited.