OlmoEarth v1.1: 3x Compute Reduction for Satellite Imagery Models

Allen Institute for AI (AI2) released OlmoEarth v1.1 on May 19, 2026, a family of transformer-based models that cuts compute costs by up to 3x compared to OlmoEarth v1 while maintaining performance on remote sensing benchmarks.

Technical Implementation

The efficiency gains stem from a fundamental redesign of how the model tokenizes Sentinel-2 satellite imagery. OlmoEarth v1 created separate tokens for each of Sentinel-2's three resolutions (10m, 20m, and 60m), generating 6 tokens per spatial patch per 2-timestep input. OlmoEarth v1.1 collapses these into single tokens, reducing token counts by three times.

This approach wasn't straightforward. AI2 reports that naive token merging caused a 10 percentage point drop on m-eurosat kNN, a standard remote sensing benchmark. The team modified their pretraining regimen to maintain cross-band relationship modeling, detailed in their technical report.

Model Family

AI2 released three model sizes:

• Base
• Tiny
• Nano

All variants process Sentinel-2 data with tensors formatted as [H, W, T, D=12], where H and W represent latitudinal and longitudinal pixels, T is the temporal dimension, and D covers 12 Sentinel-2 channels.

Performance Trade-offs

AI2 states OlmoEarth v1.1 maintains similar performance to v1 on their benchmark mix and partner-constructed tasks, though the technical report notes "some regressions." Because both versions train on identical datasets, performance differences isolate methodological changes.

Compute costs in transformer models scale quadratically with token sequence length, making the 3x reduction in tokens significant for inference and fine-tuning. AI2 measured efficiency using MACs (multiply-accumulate operations per forward pass).

Deployment Context

Since OlmoEarth v1's November 2025 release, partners have deployed it for mangrove tracking, forest loss classification, and country-scale crop mapping. AI2 reports deployments now scale to national, continental, and global areas, with data export, preprocessing, inference, and post-processing dominated by compute costs.

What This Means

OlmoEarth v1.1 addresses the practical bottleneck in satellite imagery AI: compute cost at scale. A 3x reduction enables more frequent planet-scale map refreshes and lowers barriers for organizations without large compute budgets. For researchers, training on identical datasets to v1 creates a controlled comparison for studying pretraining methodologies in remote sensing.

The model family is available on Hugging Face with full training code. AI2 recommends existing OlmoEarth v1 users test v1.1 for their specific tasks given the documented performance regressions.