In this episode I sat down with Joe Redmond from the Allen Institute for AI (AI2) to discuss OlmoEarth, AI2's open geospatial foundation model for Earth observation. Joe explains how the project emerged from AI2's environmental and climate initiatives, where partners needed practical tools for analysing satellite imagery across applications such as agriculture, wildfire risk, ecosystem mapping, and conservation. We discuss the unique challenges of remote sensing data, including its temporal and multispectral nature, why geospatial machine learning differs from traditional computer vision, and AI2's philosophy of building open models and tools that can be adapted to real-world environmental problems.
A major focus of the conversation is Latent MIM Lite, OlmoEarth's self-supervised pretraining approach. Joe explains how the method strikes a balance between masked autoencoders, which reconstruct pixels and train reliably but often learn weaker representations, and latent-space methods such as I-JEPA and Latent MIM, which can produce stronger features but are notoriously unstable. By replacing the target encoder with a frozen random linear projection in token space, Latent MIM Lite achieves stable training while preserving the benefits of latent-space prediction. We also discuss the broader challenges of evaluating geospatial foundation models, the trade-offs between embeddings and fine-tuning, and why practical performance on partner applications often matters more than leaderboard results.
Bio: Joseph Redmon is a research scientist at Ai2 building multimodal foundation models for geospatial data. As part of the OlmoEarth team he’s working to bring cutting edge AI research to non profits and NGOs working on conservation, ecological, and environmental problems.
