The emergence of synthetic pigments in the 19th century had an immense impact on the art world, particularly the availability of emerald-green pigments, prized for their intense brilliance by such masters as Paul Cézanne, Edvard Munch, Vincent van Gogh, and Claude Monet. The downside was that these pigments often degraded over time, resulting in cracks and uneven surfaces and the formation of dark copper oxides—even the release of arsenic compounds.
Naturally, it’s a major concern for conservationists of such masterpieces. So it should be welcome news that European researchers have used synchrotron radiation and various other analytical tools to determine whether light and/or humidity are the culprits behind that degradation and how, specifically, it occurs, according to a paper published in the journal Science Advances.
Science has become a valuable tool for art conservationists, especially various X-ray imaging methods. For instance, in 2019, we reported on how many of the oil paintings at the Georgia O’Keeffe Museum in Santa Fe, New Mexico, had been developing tiny, pin-sized blisters, almost like acne, for decades. Chemists concluded that the blisters are actually metal carboxylate soaps, the result of a chemical reaction between metal ions in the lead and zinc pigments and fatty acids in the binding medium used in the paint. The soaps start to clump together to form the blisters and migrate through the paint film.
OlmoEarth Studio, the Allen Institute for AI’s new workspace for building and fine-tuning environmental AI models. The interface lets users choose base maps, tag locations, and manage field data for projects such as wildfire fuel monitoring. (Ai2 Screenshot)
A new platform from the Allen Institute for AI promises to deliver insights into the state of the planet, in near real-time, by giving organizations without deep AI expertise the ability to monitor deforestation, assess crop health, and predict wildfire risk, among other capabilities.
OlmoEarth, announced Tuesday by the Seattle-based nonprofit AI institute, is an open, end-to-end system that uses AI to analyze current and historical satellite and sensor data.
It runs on a new family of AI models, which Ai2 says it trained on millions of Earth observations totaling roughly 10 terabytes of data. The idea is to give anyone free access to the kinds of capabilities typically restricted to proprietary systems or well-resourced AI labs.
The platform includes tools such as OlmoEarth Studio, a workspace for creating datasets and fine-tuning models, and OlmoEarth Viewer, a web app for exploring AI-generated maps.
The initiative is “making Earth AI accessible to those working on the front lines,” said Ali Farhadi, Ai2 CEO and University of Washington professor, in a press release announcing OlmoEarth.
Patrick Beukema, lead researcher on the OlmoEarth team, added that the project is meant to encourage collaboration across scientific and technical fields, helping different groups work together on shared data and tools to better understand and respond to environmental challenges.
Ai2 said early adopters of OlmoEarth are already showing the potential, using it to update global mangrove maps twice as fast with 97% accuracy, detect deforestation across the Amazon, and map vegetation dryness in Oregon to improve wildfire prediction and prevention, for example.
It’s the latest example of Ai2’s push for “true openness” in AI, extending the philosophy behind its open-weight language and multimodal models into climate science and conservation.
Geospatial analysis has long been dominated by major tech and research organizations. Platforms like Google Earth Engine and Microsoft’s Planetary Computer provide cloud access to petabytes of satellite data, but often require significant technical expertise for analysis and are not fully open-source.
Ai2 is positioning OlmoEarth as an end-to-end open alternative, providing not just data access but a complete, usable system for model fine-tuning and deployment.
At the model level, Ai2 is competing with AI-specific tools from other major labs. In its research, Ai2 contrasts OlmoEarth with Google’s AlphaEarth Foundations, noting that Google released “embeddings” rather than the open model itself. Ai2 says a fine-tuned OlmoEarth “outperformed AEF substantially,” and also did well against models from Meta, IBM, and NASA.
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