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 initiative, which transforms planetary information into actionable intelligence using foundation models and advanced AI reasoning.
In line with the EarthAI vision, we recently introduced S2Vec, a self-supervised framework designed to learn general-purpose embeddings (i.e., compact, numerical summaries) of the built environment. S2Vec allows AI to understand the character of a neighborhood much like a human does, recognizing patterns in how gas stations, parks, and housing are distributed, and using that knowledge to predict metrics that matter, from population density to environmental impact. In our evaluations, S2Vec demonstrated competitive performance against image-based baselines in socioeconomic prediction tasks, particularly in geographic adaptation (extrapolation), while showing a clear need for improvement in environmental tasks, like tree cover and elevation.
