In this episode I caught up with Shravya Shetty, who leads research and engineering for Earth AI at Google Research, to discuss how AI agents are changing geospatial analysis and predictive modelling. We begin by unpacking the difference between language models, fixed workflows, and agents that can plan, select tools, and iterate on results. Shravya explains how Earth AI brings together foundation models for remote sensing, population dynamics, and weather, and how its agentic capabilities can combine these with other geospatial datasets and users’ own data.
We explore the Planetary Prediction Engine, which automates data discovery, preparation, feature engineering, model training, and evaluation from natural-language requests. Shravya also shares examples from work with WHO AFRO and partners in the Democratic Republic of the Congo to analyse Ebola risk and predict potential spread. Finally, we discuss how these systems are evaluated, why domain expertise remains essential, and how interested organisations can apply for early access.
Bio: Shravya Shetty is a Distinguished Engineer and Research Lead for Google’s Earth AI—driving research at the intersection of AI and geospatial understanding to build foundation models and agentic systems across satellite imagery, climate, and population dynamics to address critical global challenges. Sitting at the nexus of planetary and human well-being, she also leads research in global public health—spanning climate and health, infectious diseases, and open-source initiatives such as MedGemma and Open Health Stack to advance global health equity.
Over her 18+ years leading large-scale machine learning at Google, Shravya previously spearheaded open-domain Question Answering for Google Search, a foundational precursor to modern LLM-driven search experiences. Prior to joining Google, she was a researcher at the Center of Excellence for Document Analysis and Recognition (CEDAR), where she developed machine learning algorithms for handwriting and fingerprint recognition.
