The Tessera foundation model enables pixel-wise analysis for satellite imagery. It leverages a masked autoencoder architecture to learn spatial representations from earth observation data. This approach reduces the need for labeled datasets in remote sensing. Practitioners can now fine-tune the model for specific land-cover classification tasks with significantly fewer samples.