GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Space Grade Linux
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Current methodologies to deploy edge AI on spacecraft face a critical cost barrier due in part by reliance on traditional real-time operating systems. The solution lies in adopting Linux, the industry standard for edge AI deployments, offering unmatched performance, compatibility, and (AI) software ecosystem. Linux infrastructure for mission adoption however is lacking. Space Grade Linux addresses this by developing a Linux distribution tailor made for spacecraft, improving support for flight software frameworks, and establishing open-source components. In addition, this allows Space Grade Linux to accelerate development, promote code-reuse, and facilitate industry collaboration crucial to remaining competitive in the rapidly evolving field of AI.
Purpose and benefits: Space Grade Linux reduces development cost of spacecraft software by allowing missions to tap into state-of-the-art AI software and hardware that would be otherwise unavailable or cost prohibitive to develop for traditional real-time operating systems. Open-source components in Space Grade Linux will also directly benefit the development of safety critical systems in other industries, such as robotics, medical devices, aerospace, and automotive.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Integrating Explainable Machine Learning with Physics for Enhanced Wildfire Detection in Observation-Constrained Environments
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Satellite-based fire detection provides critical data for fire management, fire spread modeling, air quality forecasts, and assessments of fire impacts on ecosystems and communities. Current fire detection algorithms, whether physics-based or machine learning (ML)-based, frequently fail when wildfires are obscured by dense clouds or smoke, creating data gaps that degrade the quality of air quality and fire emissions estimates. This project will develop an explainable multitask ML model for fire detection that is integrated with cloud and aerosol retrieval to enhance fire detection capabilities under clouds.
Purpose and benefits: The research has the potential to subtantially improve satellite-based fire detection, a key application and societal benefit from NASA's Earth science program, based on more consistent detection of fire activity needed for fire tracking and situational awareness, and improved detection under difficult observing conditions, including on days when extreme fire behavior generates deep injection of smoke (pyrocumulonimbus or PyroCb).
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: The airborne Compact Fire Imager (CFI) for measurements across the entire fire lifecycle
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: CFI is a new pushbroom instrument with six spectral bands between the shortwave infrared (SWIR) and thermal infrared (TIR), including two channels in the mid-wave infrared (MWIR) specifically designed to detect and characterize flaming and smoldering fires. CFI builds on the design and performance of the dual-band Compact Thermal Imager (CTI) that collected more than 15 million images from the International Space Station (ISS) in 2019, including thousands of fires. CFI leverages the stability and proven performance of innovative Strained-Layer Superlattice (SLS) detector technology on CTI with four specific improvements for fire science and applications: 1) a larger format SLS detector array that improves cross-track resolution and swath width, 2) a custom butcher block filter that provides six specific bands for fire science and applications, 3) a custom optical design that leverages the latest infrared glass technology, and 4) an enhanced processor card that supports instrument operation and onboard fire detection using machine learning (ML) algorithms.
Purpose and benefits: Onboard fire detection can accelerate the delivery of life-saving information to first responders and fire managers. The demonstration of lightweight and effective AI/ML tools for onboard processing leverages recent advances in hardware and software needed to advance edge computing for disaster applications such as wildfire detection and characterization.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Natural Language query processor for Common Metadata Repository
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: A chatgpt-like prompt query interface that uses large language models to extract intent from chat query to determine spatial, temporal and science variable filters. These filters are then applied to the Common Metadata Repository (CMR) online catalog API to return relevant earth science datasets.
For example: Water temperature of Lake Michigan since 2021 would extract the spatial constraint associated with Lake Michigan, the  temporal constraint associated with 'since 2021' (2021 -> present) and the remaining variable constraint of 'Water temperature' and apply those constraints to CMR.
This functionality is now in our user acceptance testing environment at https://cmr.uat.earthdata.nasa.gov/search/nlp/
Purpose and benefits: Intuitive user experience through prompt-based interface
Improved accuracy of search results
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: PIX4DCloud
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Geostationary spectrometer based foundation model (ABI-FM) and evaluation on benefits for 3D cloud and convection related downstream tasks
Purpose and benefits: A new all-sky EO-FM model. A thorough evaluation for downstream task benefits. A suite of 3D cloud prediction model from spetrometers.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Lunar Foundation Model
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Funded by the Office of the Chief Science Data Officer, the Lunar Foundation Model (LFM) is a joint effort between GSFC, MSFC, and IBM that will harness a large and diverse array of multi-modal datasets from recent missions with the goals to 1) Create a working example of a FM that demonstrates the construction and scientific use of FMs in planetary science, 2) Expand the community of machine learning practitioners within the lunar community and across planetary science, and 3) Pilot the use of FMs to lunar science and/or lunar exploration applications.
Purpose and benefits: 1) Create a working example of a FM that demonstrates the construction and scientific use of FMs in planetary science, 2) Expand the community of machine learning practitioners within the lunar community and across planetary science, and 3) Pilot the use of FMs to lunar science and/or lunar exploration applications.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: MADI - Modular AI for Design and Innovation
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: MADI (Modular AI for Design and Innovation) is a decentralized, open-source AI platform that identifies unexplored research "whitespace" between scientific disciplines through secure plugin architecture and interactive visualization. Unlike traditional chatbots, MADI enables collaborative human-AI partnerships where researchers can access proprietary data through authenticated plugins while maintaining security protocols, visualize knowledge relationships through 3D network graphs, and discover cross-disciplinary innovation opportunities that conventional approaches might miss. The platform demonstrates substantial efficiency gains, including 50% reduction in brainstorming time and 1,200 FTE hours saved annually, while operating cost-effectively at $500-600 per month. Currently transitioning from NASA-internal tool to Apache 2.0 open-source release, MADI represents an attempt at "democratic cognitive symbiosis" designed to prevent AI power concentration while advancing scientific discovery through transparent, community-driven development that serves public benefit for all.
Purpose and benefits: Improved innovation and design methods demonstrating 50% reduction in brainstorming session duration, saving approximately 1,200 FTE hours annually for ten-person teams. Cost-effective operations at $500-600/month with $0.05 per conversation variable costs. Accelerates cross-disciplinary research discovery by identifying unexplored "whitespace" between scientific domains. Enables secure collaboration across NASA centers without duplicating AI infrastructure investments.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Developing an ML-based subcolumn generator
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: ML generator for cloudy and precipitating subcolumns to be used in global climate models.
Purpose and benefits: Improvement of cloud and precipitation processes in global climate models for more realism and better performance.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: CHESS: Coronal Hole Extraction with Semantic Segmentation
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project aims at expanding the training of two Convolutional Neural Networks (CNNs) that we have already developed to obtain a more efficient, more accurate, and least-biased CNN model for segmenting coronal holes (CHs). 
Our two CNNs are based on (i) a U-Net and (ii) a Res-U-Net architecture for Coronal Hole Image Segmentation,with model (ii) currently being more accurate than model (i). These two CNNs have been pre-trained with the coronal hole (CH) boundary data from the Heliophysics Events Knowledgebase (HEK). These initial, pre-training data of the CH boundaries are obtained by the Spatial Possibilistic Clustering Algorithm (SPoCA) applied to images of the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), using the extreme ultraviolet (EUV) 193-Å filter. In many instances, this algorithm cannot differentiate between a CH and another solar structure called ""filament"". Our project will overcome this limitation by adding ground-based observations of the He I 10830 Å spectral line, which is able to provide such disambiguation.
Purpose and benefits: Improved segmentation of coronal holes with (i) higher accuracy, (ii) better performance, (iii) ready to be implemented in research-to-operations pipelines.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine learning for X-ray astronomical spectroscopy
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: We use Simulation Based Inference to construct 34000 artificial spectra that are representative of observed Active Galactic Nuclei X-ray spectra with NASA's NuSTAR X-ray telescope. Based on previous literature results for these spectra, we train, validate, and test a new neural network model, ML-mytorus, which predicts best values for key physical parameters fast, automatically, and with very high degree of accuracy, including error estimates. We make the code publicly available, and set up a dedicated webpage for the community to upload any spectrum and use the NN model. A publication is about to be submitted for review.
Purpose and benefits: Reproducibility: Standard spectral fitting is done interactively with human decision making. Automation and speed: Using the trained neural network model is completely automated, and takes just seconds for a given spectrum (compare to hours/days for interactive fitting). Openly available: Anyone can go to our webpage, load X-ray spectra in 2-column format and obtain results in seconds by clicking on a single button. Extensibility: The methodology can be extended for other X-ray (and non-X-ray) telescopes producing spectra, including for other types of astronomical observations, provided telescope and physical model characteristics are known. Training only needs a modest number (tens...) of existing previous fitted observed spectra, which are statistically perturbed to produce literally thousands of simulated spectra for training. Simulations, training and validation take no more than a few days with modest high-performance computing use.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Using MML code generation to create new high-energy astrophysics science software
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: We plan to train an LLM on previous large code bases for high-energy astrophysics science pipeline and analysis software.  HEASARC is under a mandate to work on next-generation science software to modernize the existing code base and make it easier for new high-energy astrophysics missions to develop data plans at lower cost.  By training LLM(s) on existing code the code assistants should be more effective in reducing the FTEs required to implement new systems.
Purpose and benefits: Modernizing the software that is maintained by HEASARC and reducing the FTEs for new development
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Diffusion Modeling of the Solar Corona
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Diffusion models such as DeepMind’s GenCast have demonstrated powerful performance in terrestrial weather forecasting, achieving results on-par and surpassing leading medium-range numerical weather simulations. We present our initial results to train a Denoising Diffusion Probabilistic Model (DDPM) of the solar corona, aimed as a first step at generating synthetic solar magnetic fields based on conditioning inputs. Conditioning inputs include multi-spectral imagery, magnetograms, and other measurements commonly used to frame coronal inverse problems.

Our initial experiment targets the generation of synthetic global magnetic field configurations of the solar coronal basedon conditioning with the solar cycle phase. The model is trained on 10 years of WSA potential field source surface(PFSS) model runs, augmented with 15° to 345° azimuthal rotations to increase data diversity. Training is conducted inthe spherical harmonics domain, leveraging concepts from Fourier Neural Operators (FNOs) and Spherical Fourier Neural Operators (SFNOs). A physics-informed loss function, built on a differentiable spherical harmonic expansion, isused to maximize generation of realistic 3D magnetic potentials.
Purpose and benefits: First step towards probabilistic modeling of the solar corona
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Data-Mining Similar Scenarios for Uncertainty Quantification of Solar Wind Predictions at L1
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Accurate Uncertainty Quantification (UQ) for space weather forecasts is an ever-important supplementary variable to enable accurate risk response. Modeling uncertainty is itself a “model of a model”, and one of the best datasets to describe a model’s performance is a past database of it’s predictions and after-the-fact observations. 

In this work, we develop a method based on k-NN and kernel regression to quantify uncertainty in the WSA solar wind model and it’s predictions of the solar wind speed at L1. By constructing state vectors that describe the current forecasting context— recent observations, recent predictions, and future predictions, we build a catalog of “similar scenarios” from past data. With a set of similar scenarios at each timestep, we can base our uncertainty on the performance in those cases. This approach—suitable for low-dimensional datasets such as time series—is extremely fast and interpretable. We find that the resulting uncertainty estimates naturally capture structured patterns in forecast error, such as shifts between solar minimum and maximum, and periodic features on the scale of half a solar rotation.
Purpose and benefits: Being integrated into WSA software for operational use in Moon 2 Mars and NOAA Space Weather Prediction Center
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Flarenet
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Developing a CNN model to identify stellar flares in the 20-second cadence TESS data product.
Purpose and benefits: Quickly and uniformly identify flares for a large number of stars, which saves time and allows for larger flare samples.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Uniting Physics and Machine Learning for Enhanced Heliophysics Insights
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The Solar Neutron TRACking (SONTRAC) instrument is designed to detect incident solar neutrons in an energy range that fills a key gap in understanding flare ion acceleration. SONTRAC tracks recoil protons generated by neutron interactions as they traverse a fiber bundle volume, depositing ionization energy along their paths. Current reconstruction of proton tracks—determining energy deposition and momentum vectors—is labor-intensive and ambiguous
Purpose and benefits: Autonomous neutron event reconstruction in SONTRAC will significantly reduce manual analysis time, increase the fraction of usable neutron interaction events, and improve instrument efficiency. For NASA, this translates to higher-fidelity science return from the same mission resources, enabling better understanding of solar flare particle acceleration. For the broader public, improved solar particle monitoring enhances space weather forecasting, which protects satellite operations, communications, and astronaut safety.

Key performance indicators (KPIs):
•Increased percentage of reconstructed neutron events (higher detection efficiency)
•Reduction in manual reconstruction effort (time savings for analysts)
•Improved accuracy and reliability of neutron track reconstruction (science quality ROI)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Cloud translator for large-scale models
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Simulates frequency of occurrence of cloud types as seen from a space-based imager from radiative fluxes.
Purpose and benefits: Evaluate realism of cloudiness in global climate models
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI/ML for Anomaly Detection and Health Monitoring for the NSN
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The project develops ML/AI methods/tools to enhance the operation and sustainment of GSFC Space Network (ACCESS-managed) assets. We analyze telemetry from TDRS and STPSat-6 to assess state of health, detect anomalies, and predict remaining useful life (RUL) of spacecraft components. The ML/AI Algorithms are implemented in Python and MATLAB and are reported and validated with engineering teams during Sustaining ML meeting. Validated models are being integrated into a GUI to provide an intuitive platform for engineers to analyze spacecraft telemetry and support real-time monitoring. Documentation and results are summarized and posted to SharePoint.
Purpose and benefits: Automated telemetry anomaly detection and forecasting reduce manual review and improve Space Network availability. In internal tests, the TDRS-8/10 Bus Voltage Limiter (BVL) detector achieved ~98% true-positive rate with very few false alarms and processed ~10 years of telemetry in ~5 minutes. Early, prioritized alerts (hours–days sooner) have surfaced incipient battery issues—e.g., two early-warning signals of failing cells—and the battery tool identified diverging cells on TDRS-9/10. Remaining-useful-life (RUL) and short-term forecasts enable condition-based maintenance and proactive scheduling, reducing unplanned downtime and associated costs.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Solar Neutron Tracking Spectrometer (SONTRAC)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: To develop new instrumentation that will be capable of measuring winds in planetary atmospheres.
Purpose and benefits: Will add understanding of the mechanisms behind solar activity and its effects on space weather.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Text-to-Spaceship
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Text-to-Spaceship is a NASA-led initiative exploring how artificial intelligence can transform mission development. The approach transforms traditional slow, linear development into a faster, iterative process, where engineers guide intent and AI rapidly handles requirements generation, design creation, and manufacturability assessment. The result is a future where missions can be conceived, iterated, and realized with unprecedented speed and rigor, expanding the boundaries of science and exploration. This initiative also strengthens NASA’s role in pioneering AI-enabled engineering, ensuring that emerging AI-for-hardware technologies are safe, rigorous, and aligned with national priorities.
Purpose and benefits: This AI-powered workflow transforms traditional design processes into an integrated computational pipeline that dramatically reduces iteration time, accelerates mission development, lowers costs and risks, and empowers teams to explore breakthrough mission concepts. By automating routine engineering tasks, Text-to-Spaceship frees human experts to focus on creative problem-solving, mission-critical decisions, and technology development where their expertise provides maximum value.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Creating U-Net + LSTM Hybrid Architecture for Image Series Analysis
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work focuses on creating U-Net + long short-term memory Hybrid analysis architectures for image series analysis on solar image data.
Purpose and benefits: Creating U-Net + LSTM Hybrid Architecture for Image Series Analysis
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Roman Space Telecope WFI Pupil alignment verification
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Using Machine Learning (ML) to determine the pupil alignment of the Wide Field Instrument of the Roman Space Telescope during the spacecraft testing in thermal vacuum conditions. The ML algorithm trained on a large data set of possible misalignments to estimate the actual misalignment from the measured data.
Purpose and benefits: This is a backup technique to verify the RST WFI pupil alignment. Because it is a backup it crates redundancy in the process making it more reliable. A more reliable approach can save the project time and money by completing the verification in a more expedited way. This can ultimately help keep the project on schedule and within cost. This benefits NASA and the public.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: A hybrid machine learning approach for calibration and regionalization of LSM soil and vegetation parameters
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work explores the combined use of machine learning and traditional model calibration methods to develop a high resolution (1 km) soil and vegetation parameter dataset for North America and Central America. First, we use a traditional calibration method (i.e., genetic algorithm (GA)) to calibrate soil and vegetation parameters to Soil Moisture Active Passive (SMAP) soil moisture at 25 km spatial resolution over the CONUS, Asia, and Europe. Next, we use a series of machine learning algorithms, including Neural Network, Random Forest, and XGBoost, to downscale and regionalize the optimized parameters for the National Land Data Assimilation System - Phase 3 (NLDAS-3) domain (i.e., North and Central America). The goal is to develop high resolution parameters that produce a land surface model (i.e., Noah-MP) soil moisture climatology that is more in-line with SMAP’s SM climatology.
Purpose and benefits: The ultimate benefit of this work is that it could improve land surface model simulations of the water cycle through more efficient remote sensing data assimilation (DA). Current DA methods rely on bias correction and CDF matching to translate observations (SMAP) into the same climatology as the model. However, this often filters out (as noise) useful signal about the human component of the water cycle, such as irrigation, in the observations. The ultimate goal of our work is to develop parameters that generate a model soil moisture climatology similar to SMAP, possibly bypassing the need for bias correction. As such, it may allow us to more readily incorporate information that SMAP provides about irrigation and other non-geophysical activities that are difficult to model, through DA of NASA remote sensing observations.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Mapping anthropogenic water cycle impacts in a future climate: A global digital twin for scenario-driven exploration
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project develops an emergent constraint emulator for future changes in water storage estimates based on the available historical record of GRACE and GRACE-FO measurements.
Purpose and benefits: An emulator to predict groundwater will reduce computation time relative to larger physical models. Additionally, predicting future changes in water storage estimates will better inform water managers for upcoming periods of water surplus or scarcity. Current results suggest we can predict 2-3 months into the future, and future work intends to identify the maximum number of months we can predict.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Nominal Forecast Sub System for the NASA Coastal Zone Digital Twin
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Development of ML based emulator for enabling what-now, what-next, and what-if analysis for flood and water quality indicators in the Chesapeake Bay, trained using Land Information System-modeled land surface variables
Purpose and benefits: Daily to subdaily forecasts of flooding and water quality indicators along the Chesapeake Bay and other US coasts
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI assisted Image recognition for EEE parts kitting and auditing
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Due to the limits in personnel capacity, warehouse inventory contract (TRAX), and the limits in the Goddard Material Management System (MMS), EEE parts kitting and auditing is labor intensive, and error prone. This project aimed to utilize the image recognition potential to help reduce partial man-hour involved in the kitting and auditing process.
Purpose and benefits: The outcome of this project will benefit the Goddard EEE parts engineer, the Printed Wiring Assembly card lead, the EEE parts technician, the vendor who receives the kit, and the Quality Assurance (QA). The measurements KPI include: 1) The man-hour saved in generating the correct parts kitting list with all required information; 2) The accuracy of with the assistance from AI, comparing the old method. The overall benefit should consider a combination of both factors, not just focused on accuracy improvement.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Development of a next-generation snow and ice product (SNIP) for operational applications
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project aims to develop interpretable AI/ML models to improve global snow depth retrieval from AMSR2 brightness temperature observations.
Purpose and benefits: This project establishes a benchmark for AI/ML applications in passive microwave remote sensing and demonstrates the potential for AI/ML to substantially advance snow depth estimation capabilities. The near-real-time global snow depth product supports critical operational applications like transportation safety, weather services, and seasonal water supply planning. It also opens the possibility for improved snow depth retrievals across the entire multi-decadal passive microwave satellite era – the longest continuous dataset available for global snow monitoring.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Simultaneous emulation and downscaling of modeled soil state variables with machine learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: We propose a lightweight, computationally efficient machine learning (ML) model capable of emulating the LIS-based soil moisture and soil temperature and downscaling them from a native 10 km resolution to 1 km resolution. Our approach is extendable to other variables as long as a non-linear relationship between meteorological forcing and the variable of interest can be conceptualized as modulated by local conditions (elevation, soil type, land cover, vegetation). Then, a branched neural network (NN) architecture structurally represents this relationship. As a part of the project, different NN architectures and input combinations have been tested and assessed using SHapley Additive exPlanations (SHAP) values and ablation analysis. Currently, the downscaled product is being validated and compared to other high-resolution products.
Purpose and benefits: Using the proposed method, it is possible to obtain LIS-like quality predictions for soil state variables in seconds, as well as provide unprecedented for LIS downscaled to 1 km data relevant to a wide variety of applications. The low computational cost of the inference and the ability to resolve fine-resolution features expedite obtaining the crucial information for decision-making.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Hydrology Copilot
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: We are building a multi-agent AI copilot that lets users explore and apply the new 1-km, hourly North American Land Data Assimilation System (NLDAS) version 3 dataset through natural-language queries. The copilot—built on NASA Earth Copilot with an agentic Retrieval-Augmented Generation (RAG) stack (Azure AI Search/Foundry + Synapse)—explains variables, retrieves relevant data and workflows, and guides users from question to analysis. It lowers the barrier for scientists, planners, and decision-makers to use NLDAS-3 for drought monitoring, flood assessment, and agricultural risk forecasting.
Purpose and benefits: •Time-to-insight : converts plain-language questions into data queries, plots, and subsets in minutes.
•Accessibility: non-experts can discover variables/units and relevant documentation without deep tooling knowledge.
•Decision support: speeds drought/flood/ag risk assessments by surfacing the right NLDAS-3 variables and spatial/temporal subsets.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Developing high-resolution multidecadal satellite remote sensing-based snow lifecycle reanalysis products over the Northern Hemisphere
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: A deep learning model is being trained using a subset of meteorological forcings and remote sensing observations of snow cover to reconstruct seasonal snow water content globally. The relatively small quantity and location-independent model inputs mean that the model is transferrable globally with little computational cost.
Purpose and benefits: Deep learning model outputs are being assimilated with NASA process based hydrologic models. This will provide the capability to bias correct for common model errors that are driving snow-fed hydrologic biases in operationally used NASA products.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Distributed Spacecraft Autonomy
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems through the development of three capabilities: scalable communication, distributed coordination and planning, and human-swarm interaction. DSA will demonstrate these capabilities in two contexts. The first context is a flight demonstration consisting of a software payload hosted on the Starling-1 small-spacecraft mission. This software payload will use the on-board GPS receiver to perform in-situ, swarm-level reconfiguration in response to observed features in the Topside Ionosphere. The second context is a scalability study, which shows how the technologies developed in the flight demonstration can scale to a large number of spacecraft (≈ 100). The scalability demonstration applies the tools developed for the flight mission to a hardware-in-the-loop simulation of the flight software payload.
Purpose and benefits: Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems through the development of three capabilities: scalable communication, distributed coordination and planning, and human-swarm interaction. DSA will demonstrate these capabilities in two contexts. The first context is a flight demonstration consisting of a software payload hosted on the Starling-1 small-spacecraft mission. This software payload will use the on-board GPS receiver to perform in-situ, swarm-level reconfiguration in response to observed features in the Topside Ionosphere. The second context is a scalability study, which shows how the technologies developed in the flight demonstration can scale to a large number of spacecraft (≈ 100). The scalability demonstration applies the tools developed for the flight mission to a hardware-in-the-loop simulation of the flight software payload. Distributed Spacecraft Systems are a type of multi-spacecraft mission architecture that can not only provide improved resolution, coverage, and availability of existing missions, but also enable missions that would be previously infeasible using traditional approaches. Autonomy is a critical need for these systems, since the cost associated with applying conventional approaches for command and control does not scale with the number of spacecraft.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: GLOBE Program - Image processing
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Processing photo images submitted to the GLOBE Program through the GLOBE Observer app. These photos relate to submissions of Clouds, Mosquito Habitat Mapper, Trees, and Land Cover observations. GLOBE data is processed through Amazon Rekognition SAAS (software as a service) tool to automate the image approval process (Amazon 2024).
Purpose and benefits: The AI photo review system has dramatically reduced staff time needed to screen photos and therefore enabled NASA to process these crowdsourced data at a much faster speed. This leads to significantly more surface-based Earth system observations to enable science and societally relevant applications that would not be possible otherwise.
Topic area: Science
Development stage: Deployed
First used: 2022-03-29
US federal AI use case: Cloud Account Allocation Plan (CAAP) Cost Analytics Support
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Utilize machine learning to predict and allocate Cloud Account Allocation Plan (CAAP) cost and egress limits based on past actuals.
Purpose and benefits: Automates predictions based on past performance figures, saving human time and effort and enabling more accurate cost estimates.
Topic area: Administrative functions
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: ML-Driven Wide Field Instrument (WFI) Image Calibration
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This machine learning-driven project aims to expedite the image calibration process for Roman Wide Field Instrument (WFI) data by developing an automated calibration system. Leveraging advanced AI algorithms, we aim to increase the speed and efficiency of image calibration, particularly for large-scale astronomical surveys and observations.
Purpose and benefits: Faster calibration processes across all detector outputs
Streamlined calibration workflows enable faster processing times, accelerating scientific discoveries.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: HEASARC Bibliography
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Use an LLM to analyze existing publications and extract references to observations archived by the HEASARC
Purpose and benefits: Maintain current status of the impact of the HEASARC on the advancement of the science.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Bump in the Wire (BITW)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: BITW is a software technology designed to detect anomalous commands and trends in telemetry beyond traditional detection methods such as command parsers and telemetry limit checkers. It provides an extensible architecture that enables a pipeline of detection plugins based on Machine Learning (ML) models and signature-based algorithms
Purpose and benefits: Intended benefit for this technology is to improve upon the detection of anomalous space system commands and telemetry beyond traditional command parsers and telemetry limit checkers, and to improve the time to detect anomalies by embedding the detection software on the edge computing platform such as a spacecraft.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Point spread function (PSF) calibration using machine learning (ML) techniques.
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The Habitable Worlds Observatory (HWO) aims to image and thoroughly characterize exoEarths and is the highest priority of NASA as recommended by the Astro2020 Decadal Survey. With this goal in mind, NASA HQ has directed NASA GSFC to open a program office to facilitate advancing technologies related to HWO.
To achieve the target of imaging dim exoplanets (to the order of 10^10 as compared to host star) it is necessary to create and maintain high-contrast zones in the science camera. This imposes stringent constraints on the telescope requirements. At GSFC, we are working on point spread function (PSF) calibration using machine learning (ML) techniques. oUse wavefront sensor camera images as input and science image to train various ML methods.
Purpose and benefits: Cost and time saving.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine learning for low-order wavefront sensing
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Use machine learning to increase the accuracy and range of low-order wavefront sensor.
Purpose and benefits: Improve sensors used for imaging planets that could harbor life.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: IV&V Requirements Quality & Traceability Analysis
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project develops software to generate pre-computed, draft analysis results for text-based software requirements artifacts on NASA programs within the scope of Independent Verification & Validation (IV&V). The software provides a web-based interface that enables users to filter pre-computed findings by issue severity, evaluate generative AI–produced results, and auto-draft IV&V issues. Its analysis capabilities include assessment of requirement quality attributes, decomposition of functionality, and evaluation of both upward and backward traceability. Initial development and testing leverage synthetic data hosted on AWS (Cloud – Other). Beginning in FY26, the project will transition to beta and production phases, operating on local on-premises HPC infrastructure with access to real ITAR-level NASA mission data.
Purpose and benefits: Implementation of the local on-premises HPC environment and subsequent execution of AI-enhanced IV&V analysis activities will begin in FY26. These activities will be complemented by expert human engineering judgement and further analysis. By integrating AI-generated content into NASA IV&V's current processes, analysis results and assessments can be produced significantly faster than with traditional methods. This accelerated capability is expected to positively impact the mission projects supported by NASA IV&V by enabling earlier identification of issues, more thorough characterization of residual risk and impact of any open identified issues, and delivery of improved, risk-informed decision-making support to programs.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: IV&V Static Code & Implementation Analysis
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project develops software to generate pre-computed, draft analysis results for static code and code implementation artifacts on NASA programs within the scope of Independent Verification & Validation (IV&V). The tool provides a web-based interface and plug-ins for Code IDEs such as Visual Studio that enables users to filter pre-computed static code findings, review generative AI–assisted assessments, and auto-draft IV&V issues. Pending enhancements include secure coding evaluations, assessments of code traceability to other artifacts (requirements, design, test, etc.) for identifying functional gaps. Initial development and testing utilize synthetic data hosted on AWS (Cloud – Other). Beginning in FY26, the project will transition to beta and production phases, operating on local on-premises HPC infrastructure with access to real ITAR-level NASA mission data.
Purpose and benefits: Implementation of the local on-premises HPC environment and subsequent execution of AI-enhanced IV&V analysis activities will begin in FY26. These activities will be complemented by expert human engineering judgement and further analysis. By integrating AI-generated content into NASA IV&V's current processes, analysis results and assessments can be produced significantly faster than with traditional methods. This accelerated capability is expected to positively impact the mission projects supported by NASA IV&V by enabling earlier identification of issues, more thorough characterization of residual risk and impact of any open identified issues, and delivery of improved, risk-informed decision-making support to programs.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: IV&V Test Analysis
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project develops software to generate pre-computed, draft analysis results for text-based test artifacts on NASA programs within the scope of Independent Verification & Validation (IV&V). The software provides a web-based interface that enables users to filter pre-computed findings by issue severity, evaluate and provide feedback for generative AI–produced results, and auto-draft IV&V issues. Its analysis capabilities include assessment of tests for completeness and consistency. Initial development and testing will utilize limited synthetic data or open source data hosted on AWS (Cloud – Other). Beginning in FY26, the project will transition to beta and production phases, operating on local on-premises HPC infrastructure with access to real ITAR-level NASA mission data.
Purpose and benefits: Implementation of the local on-premises HPC environment and subsequent execution of AI-enhanced IV&V analysis activities will begin in FY26. These activities will be complemented by expert human engineering judgement and further analysis. By integrating AI-generated content into NASA IV&V's current processes, analysis results and assessments can be produced significantly faster than with traditional methods. This accelerated capability is expected to positively impact the mission projects supported by NASA IV&V by enabling earlier identification of issues, more thorough characterization of residual risk and impact of any open identified issues, and delivery of improved, risk-informed decision-making support to programs.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: IV&V AI Assistant
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project delivers a NASA IV&V AI Assistant powered by LibreChat with Retrieval-Augmented Generation (RAG) to provide a secure, conversational interface for NASA IV&V engineers. The Assistant enables users to interact directly with NASA program documents, asking questions that span subject matter expertise and program-specific content relevant to software assurance activities, including accelerated system understanding, analysis process planning, and context specific lessons learned and potential risk considerations. Initial development and evaluation are conducted with synthetic data hosted on AWS (Cloud – Other). Beginning in FY26, the project will transition to beta and production phases, operating on local on-premises HPC infrastructure with access to real ITAR-level NASA mission data, IV&V process data, and lessons learned.
Purpose and benefits: The initial deployment of the NASA IV&V AI Assistant will leverage the LibreChat platform with automated embedding and Retrieval Augmented Generation (RAG) to provide end-users with efficient access to relevant, necessary knowledge to complete analysis activities and generalized inference capabilities. More advanced iterations are anticipated to include fine-tuning on IV&V-specific datasets such as historical issues, risks, and lessons learned. By integrating GenAI-powered search and chain-of-thought reasoning into IV&V workflows, the Assistant will enable engineers to rapidly locate critical information, streamline analysis activities, and enhance overall productivity. This accelerated capability is expected to positively impact the mission projects supported by NASA IV&V by enabling earlier identification of issues and delivery of analysis assurance conclusions.
Topic area: Science
Development stage: Pre-deployment