• The NASA‑IBM Lunar Foundation Model turns decades of lunar observations into a foundation for discovery, helping scientists surface patterns across data at a scale no single instrument has provided
• The model exceeds widely used methods by up to 23% in identifying key geographic features on the Moon's surface, including potential ice deposits, craters and volcanic formations, to support a sustained return to the Moon

YORKTOWN HEIGHTS, N.Y., Sept. 10, 2026 /PRNewswire/ -- IBM (NYSE: IBM) and NASA today announced the open-source release of the NASA‑IBM Lunar Foundation Model, one of the first publicly available foundation models for scientific exploration of the Moon, now available. Trained on an extensive lunar observation dataset curated by IBM and NASA researchers, the model can help scientists turn decades of complex, multi-instrument data into insights to support the establishment of a sustained human presence on the Moon.

The Moon's topography is ever-changing. Over time, the Moon's surface has formed craters, distributed ice and even experienced volcanic activity. For decades, sensors and instruments have continuously observed the Moon, generating petabytes of data, but to study the Moon's surface, scientists need to either sift through maps and images by hand or use low resolution, task specific machine learning models. These methods can be computationally intensive and can lack the degree of scientific accuracy needed to identify and analyze geographic features. The newly released NASA-IBM Lunar Foundation Model will help researchers accelerate scientific progress by identifying hidden relationships between many different types and resolutions of lunar data.

Researchers could use the NASA-IBM Lunar Foundation Model to investigate multiple lunar phenomena, including:

• Potential Lunar Ice Deposits: Permanently shadowed regions are among the Moon's most difficult environments to observe, yet they may contain lunar ice below the surface. Lunar ice indicates the presence of water and oxygen — resources considered essential for a future Moon base and producing rocket fuel for future missions to Mars. The NASA-IBM model combines multimodal and multi-resolution observations to predict where ice may be present on the lunar surface. A NASA-IBM authored technical paper shows that the NASA-IBM model reduced error (RMSE) in identifying areas with high potential for lunar ice up to 22% compared to the SwinV2-B (ImageNet) model.¹
• Volcanic History: Scientists study lunar volcanic features, called Irregular Mare Patches, to better understand the Moon's volcanic history and thermal evolution. In addition, identifying these changing regions is strategic for future surface operations. Using imperfect labels, the model better captures the extent of the volcanic features than the SwinV2-B (ImageNet model by 3%, bringing comparable accuracy with greater efficiency and lower fine-tuning costs.²
• Crater Detection: Craters are one of the most important and distinguishing features of the Moon and can reveal important clues about its history, such as the age of different terrains, their geology, and the chemical composition of the early lunar interior. Crater mapping also helps NASA select safe landing sites, avoid hazards such as steep slopes and boulders, and plan locations for long-term lunar infrastructure. With the model, researchers can now identify, contextualize, and classify craters at meter-scale resolution with the cited paper showing comparable accuracy as state-of-the-art models like SwinV2-B while offering greater efficiency and lower fine-tuning costs. At context-scale resolution (~100 meters), it outperforms SwinV2-B by nearly 19% using just half the training data.³

"NASA has spent decades building an extraordinary scientific record of the Moon, but collecting data is only part of the job," said Kevin Murphy, chief science data officer and acting chief data and AI officer at NASA Headquarters in Washington. "We also have to make data easier for scientists to explore and use. The NASA-IBM Lunar Foundation Model shows what's possible when we bring AI to NASA's petabytes of scientific data. That's a real opportunity we see with AI: turning large-scale data into new discoveries."

"Uncovering the mysteries of the Moon requires an ability to learn from an extraordinary volume of scientific data," said Juan Bernabe-Moreno, Director of IBM Research Europe, UK and Ireland. "The NASA-IBM Lunar Foundation Model gives scientists a foundation to explore the Moon at scale, connecting observations across instruments, revealing patterns that are difficult to see in isolation, and providing an open platform the global research community can build on."

Despite the wealth of lunar data, no publicly available, unified dataset exists that brings the multi-modal, multi-resolution data into a common framework suitable for modern machine learning. Alongside the model, IBM and NASA scientists built the first open-source lunar dataset of its kind, a unified, machine learning ready lunar dataset aggregating over 30 spatially-aligned layers from nine instruments across four missions. The dataset combines tens of thousands of images and maps showing unique geophysical properties of the lunar surface from NASA's Lunar Reconnaissance Orbiter (LRO) and NASA's GRAIL mission and incorporated complementary lunar data from the Japanese Aerospace Exploration Agency's SELENE/Kaguya for a rich, multi-modal view of the lunar surface and subsurface available for the lunar science community to build on.

The model extends an established IBM and NASA collaboration that transforms valuable scientific data into an openly available foundation for discovery for the entire scientific community. By open sourcing the model, scientists and researchers have access to cutting edge AI systems to accelerate progress in lunar exploration. It joins the Prithvi family of open foundation models, spanning geospatial, weather, heliophysics and now the Moon. Together, these models advance a broader vision: instead of building a new algorithmic system for every scientific question, researchers can start from a shared model and adapt it to new tasks to accelerate discovery across domains.

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Thousands of government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting delivers open and flexible options to our clients. All of this is backed by IBM's long-standing commitment to trust, transparency, responsibility, inclusivity and service.

Media Contacts:



Ashley Peterson

IBM Research Communications

Ashley.peterson@ibm.com 

Maury Chasteau-Simien

IBM Research Communications

Maury@ibm.com 

¹ "Multimodal-Multiresolution Foundation Model for Lunar Remote Sensing", pg 17 (authored by IBM & NASA)

² "Multimodal-Multiresolution Foundation Model for Lunar Remote Sensing", pg 17 (authored by IBM & NASA)

³ "Multimodal-Multiresolution Foundation Model for Lunar Remote Sensing", pg 15 (authored by IBM & NASA)

View original content to download multimedia:https://www.prnewswire.co.uk/news-releases/ibm-and-nasa-release-open-source-ai-model-to-support-lunar-exploration-302874851.html

Eine anonyme E-Mail an die US-Botschaft in Bern hat kurz vor dem Auftritt von US-Präsident Donald Trump beim Weltwirtschaftsforum in Davos ein Ermittlungsverfahren der Schweizer Bundesanwaltschaft ausgelöst – und zur Festnahme einer WEF-Mitarbeiterin geführt. Inzwischen ist klar: Die gegen die IT-Expertin erhobenen Vorwürfe eines geplanten Attentats waren nicht zu belegen. Die Bundesanwaltschaft teilte mit, die in der Sache eröffneten Ermittlungen wegen strafbarer Vorbereitungshandlungen zu einem Mord seien eingestellt worden.

Die Warnmail war laut Medienberichten der Tamedia-Gruppe wenige Stunden vor Trumps Rede eingegangen. Der anonyme Absender schilderte in fehlerhaftem Englisch eine angeblich „dringliche Bedrohung“ für das Leben des US-Präsidenten in Davos und bezeichnete die später festgenommene Frau iranischer Herkunft als Agentin, die eine entsprechende Operation koordiniert habe. Die US-Botschaft leitete den Hinweis an die Schweizer Behörden weiter; diese erklärten, sie hätten die Warnung „sehr ernst“ genommen und seien „sofort aktiv“ geworden.

Tatsächlich arbeitete die Beschuldigte nicht am Tagungsort in Davos, sondern in der WEF-Zentrale im Genfer Vorort Cologny. Dort wurde sie nach Angaben der Bundesanwaltschaft festgenommen, mehrere Stunden lang bis tief in die Nacht vernommen und ihre Telefone sowie ihr Computer wurden beschlagnahmt und ausgewertet. Im Zuge dieser Maßnahmen habe sich jedoch gezeigt, dass sich keiner der in der anonymen E-Mail erhobenen Punkte erhärten ließ. Für alle Aspekte der Anschuldigungen seien keine Beweise gefunden worden, hieß es zur Begründung für die Einstellung des Verfahrens.

Die betroffene Mitarbeiterin, eine gebürtige Iranerin, war nach den Medienberichten kurz nach dem Vorfall beim WEF nicht mehr beschäftigt. Zu den Gründen äußerte sich die Organisation nicht; auf entsprechende Anfragen gab es zunächst keine Stellungnahme. Damit bleiben nach dem spektakulären Fehlalarm vorerst Fragen offen – etwa zur Herkunft und Glaubwürdigkeit des anonymen Hinweises –, während die Schweizer Strafverfolger den Fall strafrechtlich abgeschlossen haben.