AI for lunar exploration
Over the past 7 years, NASA, The Luxembourg Space Agency / Space Resources and FDL.AI have had a hugely impactful research partnership leveraging AI for lunar exploration and resources, with notable firsts in advance of the Artemis mission.
The formula of data science specialists working closely with subject matter researchers has been remarkably consistent, with many peer reviewed research outcomes and data products, from rover localization without GPS to co-operative robotics and crater-counting for impact age determination. Some of our most impactful work for Artemis is shared below.
Notable firsts:
Lunarlab’s research used AI to peer into the permanently shadowed regions (PSRs) of the Moon to give us a clear view into the permanent darkness for the very first time. HORUS continues to be used for traverse planning.
Lunarlab’s work on thermal anomaly detection revealed the location of the Moon’s metallic resources using a breakthrough application of Physics Informed Neural Nets called a PINN. This AI allows us to see the Moon’s resources clearly.
Lunar South Pole PSRs
FDL.AI pipelines for Lunar exploration
CHANGE DETECTION WITH LRO DIVINER
We can use ML to identify the faint and transient signatures of features such as fresh craters, landslides, and tectonic activity on the whole Moon. Protecting the settlers and infrastructure of our off planet outpost.
UPSCALING LUNAR RESOURCES
We can use AI super-resolution to improve maps of the lunar poles upscaling intermediate (low res) imagery to high resolution images. The improved images have substantial value for lunar science, greatly aiding the traverse planning for the imminent exploration of the Moon.
MOON FOR GOOD (PHASE II)
ML can learn to enhance the light in faint images of permanently shadowed regions of craters, to aid sustainable missions on the Moon that depend on ice deposits.
LUNAR RESOURCE MAPPING / SUPER RESOLUTION
This team produced a global data stack of lunar orbiter measurements by fusing 42 layers of multi-sensor satellite data. Revealing the location of high value metallic deposits on the moon, enabling the construction of a sustainable off planet outpost on the moon.
Illuminating shadowed regions on the lunar poles.
LOW-LIGHT IMAGE ENHANCEMENT OF PERMANENTLY SHADOWED LUNAR REGIONS WITH PHYSICS-BASED MACHINE LEARNING
LRO NAC images of the Apollo 16 landing site (LM in the center of the figure, astronaut tracks are visible), top: 0.94 m/pixel; bottom: 0.47 m/pixel. The higher resolution image reveals more of the small-scale geomorphology, such as small craters, etc., which are most relevant for ground-based exploration. The LM has a diameter of 4.22 m without the landing gear. Image credits to LROC/GSFC/ASU
Automatically highlight all the potential new cold spots.
Give the probability of it being new impact.
Autogenerate all relevant information of the area of interest. i.e. thermal images, optical images, statistics, etc.
