Google Research introduces S2Vec, using self-supervised learning to understand the built environment
Google Research has released S2Vec, a self-supervised framework that converts the built environment into layered images through S2 Geometry tiling and feature rasterization, then learns general-purpose embeddings with a masked autoencoder (MAE). On zero-shot geographic extrapolation for U.S.-wide socioeconomic predictions such as population density and median income, S2Vec is usually the best single model. Multimodal fusion with image embeddings generally performs better than either modality alone, though tasks such as tree canopy and elevation remain challenging.