IBM and the European Space Agency (ESA) today launched TerraMind, a new open-source AI model with an “intuitive” understanding of Earth. According to the research team, the system is the best-performing AI model for Earth observation.
In an ESA-led evaluation, TerraMind beat 12 leading AI models on the PANGAEA benchmark — a community standard for Earth observation. The model excelled at various real-world tasks, including land cover classification, change detection, and multi-sensor analysis. On average, it outperformed other models by 8% or more.
“To me, what sets TerraMind apart is its ability to go beyond simply processing earth observations with computer vision algorithms,” said Juan Bernabé-Moreno, director of IBM Research UK and Ireland. “It instead has an intuitive understanding of geospatial data and our planet.”
TerraMind is a generative AI model that can understand different types of data — such as images, text, and time-based sequences (like climate patterns) — and spot connections between these different kinds of information. That’s particularly useful when dealing with an immensely complex system like Earth.
The model was trained on 9 million samples drawn from nine different data types, including satellite images, climate records, terrain features, and vegetation maps. The broad dataset covered every region and biome on Earth. It was designed to minimise bias and ensure the model can be used reliably across the globe, the researchers said.
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ESA and IBM expand AI’s push into climate modelling
TerraMind is built on Prithvi, an open-source family of foundational climate models launched by IBM and NASA in 2023. The Prithvi models require relatively less computational power than traditional climate modelling software, making them potentially more environmentally friendly.
A standout feature of TerraMind is its “Thinking-in-Modalities” (TiM) tuning. Similar to chain-of-thought reasoning in language models, TiM lets TerraMind self-generate extra data to improve its performance.
“TiM tuning boosts data efficiency by self-generating the additional training data relevant to the problem being addressed — for example, by telling the model to ‘think’ about land cover when mapping water bodies,” said Johannes Jakubik, an IBM research scientist based in Zurich.
TerraMind was built in collaboration with Polish spacetech firm KP Labs, the Jülich Supercomputing Centre in Germany, and the German Space Agency (DLR). The model is now available open-source on Hugging Face. Fine-tuned versions will be released in the coming months.
ESA, NASA, and IBM are by no means the only organisations experimenting with AI models for climate forecasting. Another example emerged from Google DeepMind, which recently unveiled an AI weather forecaster that makes faster and more accurate predictions than the best system available today.
The EU has also experimented with the tech. Last year, the union unveiled a comprehensive digital twin of the Earth that uses vast troves of data to improve climate predictions.
