Pull to refresh
Logo
IBM and NASA release open-source AI model for lunar exploration

IBM and NASA release open-source AI model for lunar exploration

New Capabilities

The Lunar Foundation Model maps ice, craters, and volcanic terrain using decades of data from four Moon missions

Today: IBM and NASA release open-source Lunar Foundation Model

Overview

Updated 27 minutes ago

NASA is targeting 2028 for humans to walk on the Moon again. Before astronauts land, scientists must find ice in shadowed craters and identify safe terrain. On September 11, that mapping work got a new tool: an open-source AI model built by IBM and NASA.

The NASA-IBM Lunar Foundation Model is trained on more than 30 layers of data from nine instruments across four missions, including NASA's Lunar Reconnaissance Orbiter and Japan's SELENE/Kaguya spacecraft. It cuts error in identifying potential ice deposits by 22% versus a leading computer-vision baseline. The model and a co-registered lunar dataset are free to download from Hugging Face.

Why it matters

This shared lunar AI model gives scientists worldwide a free tool for mapping ice and craters, supporting NASA's 2028 crewed return.

Questions about this story

Free account needed to ask — your question is kept and asked for you right after sign-up. Answers are public.

No questions yet — be the first to ask.

Key Indicators

22%
Error reduction in lunar ice identification
Comparison of the NASA-IBM model to the SwinV2-B computer-vision baseline.
4
Missions contributing lunar data
Nine instruments across NASA's LRO and GRAIL missions plus JAXA's SELENE/Kaguya.
30+
Spatially-aligned data layers
Covering surface and subsurface properties of the Moon in a unified framework.
2M+
Co-registered data points in open dataset
Aggregated from tens of thousands of lunar images and maps.

Voices

Curated perspectives — historical figures and your fellow readers.

Ever wondered what historical figures would say about today's headlines?

Sign up to generate historical perspectives on this story.

Play

Exploring all sides of a story is often best achieved with Play.

Most of these play right now — no account needed. Sign up to save scores, keep a streak, and unlock Debate and Predict. Log in Sign Up
Predict 3 ways this could play out. Back the one you believe — contrarian picks score more when a scenario has a resolution date. Log in to play

People Involved

Organizations Involved

Timeline

August 2026 September 2026

2 events Latest: Today
  1. IBM and NASA release open-source Lunar Foundation Model

    Today Announcement

    Model and co-registered lunar dataset published on Hugging Face. Ice detection error cut by 22% versus baseline.

  2. SpaceX Falcon 9 crashes into the Moon

    Incident

    The rocket's upper stage created a new crater. IBM later used the image to validate the model's crater detection.

Historical Context

3 moments from history that rhyme with this story — and how they unfolded.

1961-1972

Apollo-era lunar mapping (1960s-1970s)

NASA scientists and cartographers mapped the Moon's surface manually using telescopic and orbital photography. The process produced charts that guided every Apollo landing, but it was painstaking and the maps had gaps, particularly at the poles.

Then

Apollo missions landed successfully with the maps available at the time.

Now

The lunar community spent decades filling map gaps with later missions like Lunar Prospector and the Lunar Reconnaissance Orbiter.

Why this matters now

The Lunar Foundation Model automates this mapping tradition, covering the entire Moon with co-registered data from four missions in a single unified model.

July 2021

DeepMind's AlphaFold open-source release (2021)

DeepMind released AlphaFold, an AI system that predicts protein structures from amino acid sequences, along with its source code and a database of over 350,000 predicted protein structures. Researchers worldwide adopted it for drug discovery, disease research, and more.

Then

The scientific community embraced it rapidly, with thousands of researchers using it within months.

Now

AlphaFold became the standard tool in structural biology, cited in tens of thousands of papers.

Why this matters now

AlphaFold showed that an open-source AI model can become the shared analytical foundation of an entire scientific field, replacing bespoke task-specific systems.

August 2023

IBM's Prithvi EO Earth observation model (2023)

IBM released Prithvi EO, an open-source geospatial foundation model trained on NASA's Harmonized Landsat-Sentinel data. The model can be fine-tuned for tasks like flood detection and crop classification without retraining from scratch. It was the first geospatial foundation model deployed in orbit.

Then

It established IBM's pattern of building open, reusable scientific AI models and became the basis of the Prithvi family.

Now

The Prithvi family now spans Earth observation, weather, heliophysics, and the Moon.

Why this matters now

The Lunar Foundation Model is the direct descendant of Prithvi EO, using the same foundation-model approach and low-rank adapter fine-tuning techniques.

Sources

(8)