satellite imagery

Ai2 has launched the OlmoEarth Platform, infrastructure built to run its OlmoEarth Earth observation foundation models at continent scale. A North America wildfire-risk map used nearly 20,000 CPUs and 994 GPUs simultaneously, cutting an estimated 4,737 serial-compute hours to about 30.5 hours.

Mistral AI reports that fine-tuning its Pixtral-12B vision model on satellite imagery increased classification accuracy from 56% to 91% on the Aerial Image Dataset. The company used LoRA (Low-Rank Adaptation) to train on 8,000 samples for under $10, reducing hallucinations from 5% to 0.1%.

Allen Institute (AI2) released OlmoEarth v1.1, a family of transformer-based models for satellite imagery processing that reduces compute costs by up to 3x compared to the original OlmoEarth v1. The efficiency gains come from collapsing Sentinel-2 resolution bands into single tokens, cutting sequence lengths by three times while maintaining benchmark performance.