Google Research· Research

Mapping the modern world: How S2Vec learns the language of our cities

Shushman Choudhury, Research Scientist, Google Research We introduce S2Vec, a self-supervised framework that transforms complex geospatial data into general-purpose embeddings to predict socioeconomic and environmental patterns across the globe. Quick links Paper Share Copy link × When we think about artificial intelligence and geography, we often focus on navigation, or getting from point A to point B. However, the built environment — the complex web of roads, buildings, businesses, and infrastructure that defines our world — contains far more information than just coordinates on a map. These features tell a story about socioeconomic health, environmental patterns, and urban development. Until recently, translating these diverse geospatial features into formats that machine learning (ML) models can understand had been a manual and labor-intensive process. Researchers often had to hand-craft specific indicators for every new problem they wanted to solve. At Google Research, we’ve developed a new way to bridge this gap as part of the Google Earth AI …

Announced

24 Mar 2026 17:42 UTC