Astrophysicist Brice Ménard just used Claude Science to construct the first complete ultraviolet map of the entire sky. Claude orchestrated teams of AI agents to calibrate tens of thousands of satellite observations and inpaint the missing one-third of the sky, predicting unseen UV data within 10% of real measurements. It solves a decades-old observational gap while showing how AI agents can quietly clear researchers' backlogs.
Anthropic’s Claude just helped plan NASA’s first AI-generated Mars rover drive. Engineers at JPL used Claude Code to analyze terrain images and script navigation commands for Perseverance's 400-meter trek. After human operators confirmed the route with minor tweaks, NASA estimated the AI workflow could cut planning time in half.
NASA and IBM just launched Surya, an open-source heliophysics foundation model trained on 9 years of solar observation data. It can visually predict solar flares two hours in advance—beating existing benchmarks by 16%—to help protect satellites and power grids from space weather chaos.
NASA and Microsoft just developed Earth Copilot, an AI prototype powered by Azure OpenAI Service that lets users query over 100 petabytes of geospatial data in plain language. Instead of navigating complex interfaces, researchers can simply ask questions about hurricane damage or air quality. It is currently in internal testing before rolling out to NASA's VEDA platform.
IBM and NASA just released Prithvi WxC, an open-source weather and climate foundation model trained on 40 years of NASA earth observation data. Available on Hugging Face, it can downscale climate data up to 12x resolution and model complex atmospheric gravity waves. Why it matters: it moves weather AI past narrow forecast tools into a flexible backbone for global climate science.
IBM and Hugging Face just open-sourced a geospatial AI foundation model built in partnership with NASA. Trained on a year of satellite data across the continental U.S., the model beats state-of-the-art benchmarks by 15% using half the labeled data. Why it matters: It turns massive satellite archives into ready-to-use tools for tracking floods, fires, and deforestation.
