OlmoEarth v1.1: A more efficient family of models

OlmoEarth v1.1 introduces a more efficient family of transformer-based models for processing remote sensing data, significantly reducing compute costs by up to three times compared to its predecessor, OlmoEarth v1, while maintaining similar performance levels. The updated model leverages a novel approach to tokenization by splitting Sentinel-2 satellite imagery into resolution-based patches, which helps in modeling important cross-band relationships without significant performance loss. This efficiency allows for more cost-effective and faster deployments of the model across national, continental, and global scales, making frequent updates of planet-scale maps more feasible for users. By training OlmoEarth v1.1 on the same dataset as its predecessor, the new release isolates the impact of methodological changes, thereby advancing the understanding of scientific principles in pretraining models for remote sensing. The model family is available in various sizes to accommodate different compute budgets, offering significant benefits for both developers and researchers in terms of cost savings and enhanced performance.