Google Research introduces S2Vec for general-purpose geospatial embeddings

What happened

Google Research introduced S2Vec on March 24, 2026 as a new self-supervised framework for geospatial AI. The idea is to convert built-environment data such as roads, buildings, businesses, parks, and other infrastructure into reusable embeddings that can support socioeconomic and environmental prediction tasks at global scale.

That is a meaningful shift from older geospatial workflows, which often required researchers to hand-craft features for every downstream problem. S2Vec is designed to learn a general representation of place, so the same embedding can help model questions such as population density, median income, and broader urban-development patterns.

How it works

The system uses Google's S2 Geometry framework to partition the Earth's surface into cells at multiple resolutions.

Features inside those cells are rasterized into multi-layer images so geospatial structure can be processed more like computer-vision input.

S2Vec then applies masked autoencoding, hiding parts of the map representation and training the model to reconstruct them from context.

The output is a general-purpose embedding that captures the character of a location without depending on hand-written labels.

In Google's evaluation, S2Vec performed especially well on socioeconomic prediction tasks that required geographic adaptation, meaning it could generalize to unseen regions rather than just interpolate within familiar ones. The paper also found that environmental prediction tasks such as tree cover, elevation, and carbon emissions improved when S2Vec was fused with satellite-imagery embeddings. That result suggests built-environment data is powerful on its own, but strongest when paired with remote sensing.

Why it matters

S2Vec points toward a broader category of foundation-style models for geography. Instead of building one narrow model for one urban-planning question, teams could build reusable location embeddings and adapt them across planning, infrastructure, climate, and public-policy workflows.

For Insights readers, the larger takeaway is that AI competition is spreading into location intelligence. As geospatial data, satellite imagery, and built-environment records become easier to fuse, the next wave of practical AI may include stronger tools for urban planning, environmental analysis, and measuring how cities change over time.