NASA · NASA-704 · record R0390

Neural Scene Representations for Lunar Terrain Modeling

National Aeronautics and Space Administration · ARC: Ames Research Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

We have developed LunarNRM, a novel neural surface reconstruction algorithm based on Neural Radiance Fields (NeRFs) that incorporates shadow-aware and depth-aware methodologies.

Expected benefits

By integrating multi-sensor data from the Lunar Reconnaissance Orbiter (LRO)—specifically optical data from the Narrow Angle Camera (NAC) and altimeter data from the Lunar Orbiter Laser Altimeter (LOLA)—we have demonstrated that LunarNRM can effectively reconstruct these critical regions, directly supporting NASA’s Artemis campaign.

System outputs

Our LunarNRM generates shadow-controlled Digital Elevation Models (DEMs) of the lunar surface, enabling accurate modeling and relighting of largely shadowed regions such as craters in the lunar south pole.

Technology

AI classification: Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported