GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Deep Learning for Flood Mapping (DELTA)
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: DELTA simplifies machine learning for satellite imagery.
Purpose and benefits: Remotely sensed imagery is increasingly used by emergency managers to monitor and map the impact of flood events to support preparedness, response, and critical decision making throughout the flood event lifecycle. To reduce latency in delivery of imagery-derived information, ensure consistent and reliably derived map products, and facilitate processing of an increasing volume of remote sensed data-streams, automated flood mapping workflows are needed. A joint USGS-NASA-Univ. Alabama initiative developed DELTA and applied it to automatic near-real time flood detection, using multiple sources of satellite imagery for use in disaster response.
Topic area: Science
Development stage: Retired
US federal AI use case: cFS High Performance Computing Framework (cFS HPCF)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The core Flight System (cFS) High Performance Computing Framework (HPCF) provides an environment to support a wide variety of Science work, to inlcude AI and ML.
Purpose and benefits: cFS High Performance Computing Framework (cFS HPCF)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: OCO2/3 Bad Pixel Map
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: A machine learning approach is developed to improve the bad pixel map that masks damaged or unusable pixels in the imaging spectrometers of the Orbiting Carbon Observatory-2 and -3.
Purpose and benefits: Improved science results.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SpecTF Clouding Screening for Imaging Spectroscopy
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: A deep learning model for accurate, data-driven cloud detection in imaging spectroscopy data.
Purpose and benefits: Improved science results.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Decision Theoretic Planning
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Algorithms to plan and optimize different outcomes
Purpose and benefits: Increase autonomy and efficiency.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Cloud avoidance for Atmospheric Retrieval Missions
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Methods for atmospheric retrieval of earth science data.
Purpose and benefits: Improved science results.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Dynamic, Intelligent, Tomographic Imaging
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Methods for analyzing 3D imaging data
Purpose and benefits: Improved methods for data understanding.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Adversarial Policy Evolution via AI
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Develop and improve decision-making strategies by having them compete against adversaries, learning and evolving through repeated challenges.
Purpose and benefits: Improved methods.
Topic area: Science
Development stage: Pilot
First used: 2021-09-01
US federal AI use case: Adaptive Problem Solving / Hyperparameter Optimization
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Adaptable algorithms to analyze hyperspectral data
Purpose and benefits: Improved science analysis.
Topic area: Science
Development stage: Pilot
First used: 2024-09-01
US federal AI use case: Onboard Science Instrument Autonomy (OSIA) for OWLS
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Onboard biosignature detection for a suite of life-detection instruments (motility, fluorescence, metabolism indicators) and summarizing the data to overcome bandwidth constraints (e.g., at Enceladus or Europa).
Purpose and benefits: Algorithms successfully identified biosignatures during a field test at Mono Lake, CA and achieved compression ratios of 400-1600x on 3 instruments.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: EMIT Methane Plume Detection
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Machine learning based detection of methane plumes from imaging spectroscopy data.
Purpose and benefits: Machine learning based detection of methane plumes from imaging spectroscopy data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Science Autonomy for NEAScout
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Autonomy software to support on board AI science capabilities for the Near Earth Asteroid Scout Mission.
Purpose and benefits: Autonomy software to support on board AI science capabilities for the Near Earth Asteroid Scout Mission.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Time Series Forecasting, Evaluation and Deployment (Time-FED)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Time-FED is a machine learning system for Time series Forecasting, Evaluation and Deployment.  TimeFED was created in response to the following data realities: 1) data contains significant gaps (sometimes on the order of months or years) due to sensor outages, 2) data are not sampled at uniform rates, 3) time series data can be in stream or track form. JPL has built an infrastructure for time series prediction and forecasting that respects these realities.
Purpose and benefits: Time-FED is a machine learning system for Time series Forecasting, Evaluation and Deployment.  TimeFED was created in response to the following data realities: 1) data contains significant gaps (sometimes on the order of months or years) due to sensor outages, 2) data are not sampled at uniform rates, 3) time series data can be in stream or track form. JPL has built an infrastructure for time series prediction and forecasting that respects these realities.
Topic area: Science
Development stage: Pilot
First used: 2021-09-01
US federal AI use case: AI Risk-aware task and motion planning for snake-like robots in icy enviroments
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Enables task and navigation planning inextreme ice terrains/environments.
Purpose and benefits: Time-FED provides time-savings for persons wishing to prepare data for and use ML models.  For example, Time-FED has been used on ML applications applied to GNSS data stored in ESDR archives.
Topic area: Science
Development stage: Pilot
First used: 2022-09-01
US federal AI use case: The AQcGAN Air Quality Emulator for GEOS
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: AQcGAN is an air quality emulator for surface O3 and NOx concentrations.
Purpose and benefits: AQcGAN is an air quality emulator for surface O3 and NOx concentrations.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: The AIMFAHR Project
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Artificial Intelligence (AI) techniques, particularly Machine Learning (ML), have undergone significant growth in heliophysics research in recent years. Various ML models have emerged, some outperforming empirical and physics-based models while significantly reducing computational time. The Artificial Intelligence Modeling Framework for Advancing Heliophysics Research (AIMFAHR) project is an initiative aimed at integrating community-wide AI efforts into a unified AI modeling framework, advancing system-of-systems science in Sun-Earth interactions and enhancing the predictability of space weather hazards. We selected data-driven models of the magnetosheath, cusps, auroral precipitation, field-aligned currents (FACs), ionospheric electrodynamics, and thermospheric density as the initial set of AIMFAHR base models. We simulated geomagnetic storms on 4 Jan 2023, 6 May 2023, and 11 May 2024, selected by the Machine Learning-based Geospace Environment Modeling (MLGEM) resource group at the Geospace Environment Modeling (GEM) workshop.
Purpose and benefits: These initial efforts provide valuable insights for future AIMFAHR activities, including ML model coupling, knowledge transfer between models, uncertainty quantification, and research-to-operation transitions.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Qualitative Evaluation of Foundation Models (QEFM)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Objective: Quantify the performance of Foundation Models (FMs) for weather and climate to guide GSFC scientists in effectively integrating AI into their research.
Purpose and benefits: We now have a functioning framework for rapidly evaluating current and future Foundation Models available to our SMEs, which will be available to GSFC scientists over the summer.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: System for Uncertainty, Risk, and Feature Assessment of Surfaces (SURFAS) Holistic Scene Analysis
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: SURFAS, given the above combination of inputs for a particular vehicle, is informed by expertise of mission designers to fuse relevant data together in order to create a holistic map of the scene, combining all information that the agent can perceive about that scene; e.g. the hyperspectral imager may autonomously tag identify an area of geological interest to the mission, Using data from a LiDAR scan, the robot can plan a path to reach the area. That path can be modified to avoid a patch of soil that the ground penetrating radar identifies as soft and loose (and so should be avoided by the robot). SURFAS intends to provide an architecture for uniting these telemetry streams into a single scene map that is made available to onboard planners to account for both risk AND reward in planning.
Purpose and benefits: SURFAS intends to provide an architecture for unifying onboard systems that are often siloed with limited data sharing. Particularly, science instruments can provide relevant traversal information, especially in novel environments encountered on planetary surfaces. We want to avoid leaving data on the table and instead use all available telemetry to inform the motion and mission planning for next-generation robotic explorers.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Explainable Machine Learning Methods for Ocean Worlds Mass Spectrometry Data: Biosignatures and Environmental Characterization
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Future astrobiological and geochemical investigations of ocean worlds (OWs) such as Europa and Enceladus will face challenges that can be addressed through science autonomy. While ML methods are potentially powerful tools for the prediction of geochemistry and biosignatures using IRMS data, many of these models are “black boxes” that reduce trust in predictions, and interpretable ML tools are needed to instill trust. In addition, methods are needed to diagnose false predictions for high-stakes predictions like extraterrestrial life.
Purpose and benefits: We develop and validate an interpretable ML local variable importance tool called Local Nearest-neighbors Projected Distance Regression (local-NPDR) that improves the explainability of ML models for biosignatures and OW chemistry using real IRMS measurements of volatile CO2 from OW analogue brines and simulated data. We hypothesize that false predictions may be identified when the signs and magnitudes of local and global variable importance scores differ. We add local-NPDR false prediction diagnostics to our interpretable ML algorithms that include global-NPDR feature selection and network visualization of globally-important variables. 
Together these NPDR-based tools add interpretability to ML models with the ability to detect biosignatures and characterize the environment with respect to pH, CO2 concentration and salt content. Such interpretable ML methods will be important for the implementation of science autonomy for future missions to study plumes of OWs and evaluate their habitability and geochemistry.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Transiting Exoplanet Survey Satellite (TESS) Neural Network (NN)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The Transiting Exoplanet Survey Satellite (TESS) is a NASA mission focused on exploring and finding exoplanets around nearby stars using the transiting method. TESS telescope covers a large field of view of 96 sq. deg in a single exposure. It has four cameras arranged vertically pointing from the ecliptic plane toward the poles.
Purpose and benefits: Thanks to this configuration and observing schedule, TESS is able to observe asteroids with a high duty cycle. Current techniques to search for asteroid signals on images rely on the shift-and-stack method, which relies on testing all possible combinations of direction and speed an object can move across the image to maximize the detection signal and find the asteroid’s track. This method is computationally expensive, and only attainable when the parameter space (direction-velocity) is constrained, usually to the main direction (e.g. orbits parallel to the ecliptic plane) and low speeds (main belt asteroids). This introduces a bias against fast-moving asteroids and high-inclination orbits (vertical tracks). To solve this, we implemented a rotationally invariant neural network (NN) model that performs semantic segmentation to find moving objects in TESS FFIs.We constructed a custom training set using 64x64x64 cubes of pixel flux time series and truth masks with the tracks of known asteroids from the JPL Horizon Ephemeris system. Our NN model can find known and new asteroids with all kinds of track orientations, showing no bias against objects moving at high inclination orbits, or fast-moving asteroids, or tracks with a change in direction. This NN model detects ~90% of known asteroids down to apparent visual magnitude 20th and has a detection limiting magnitude of ~20.5. This is on par with current implementations of the shift-and-stack method but without the bias introduced by limiting the range of track direction and velocity.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: RAG Chatbots: Enhancing User and Internal Support Through Dual Knowledge Systems
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work presents two complementary RAG-based chatbot systems developed for NASA's Community Coordinated Modeling Center.  These tools represent practical applications of retrieval-augmented generation to enhance both external user support and internal team productivity.
Purpose and benefits: By implementing efficient document processing pipelines and strategic context assembly, both systems demonstrate significant response time improvements while maintaining information boundaries between public and private knowledge domains.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning Techniques for Fast Radiative Transfer
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Radiative transfer models for satellite data assimilation and physical atmospheric retrievals need to be both fast and accurate to fulfill operational constraints. These contradicting requirements have lead to the development of several algorithms specifically for this purpose. Machine Learning offers a new set of tools that can be used for this application and this presentation will discuss implementation and results of deep learning in this context.
Purpose and benefits: Radiative transfer models for satellite data assimilation and physical atmospheric retrievals need to be both fast and accurate to fulfill operational constraints. These contradicting requirements have lead to the development of several algorithms specifically for this purpose. Machine Learning offers a new set of tools that can be used for this application and this presentation will discuss implementation and results of deep learning in this context.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Using ChatGSFC to Streamline Analysis and Reporting
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: A series of 90+ examples of how ChatGSFC or a NASA-focused LLM can be utilized to enhance project planning and controls (PP&C) analysis and streamline project management activities.
Purpose and benefits: Small working groups in various functional areas (EVM, Risk, Resource Management, Schedule) have been experimenting with ChatGSFC and documenting time-savings and efficiencies gained from using the AI/LLM tool.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Ground-based Detection of Martian Dust Devils With a Fine-tuned Fast R-CNN
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: We developed a two-stage pipeline for efficient dust devil detection in Mars rover imagery. Our approach combines preprocessing filters to remove unsuitable images, followed by a Faster R-CNN with ResNet-50 backbone and Feature Pyramid Network, effectively detecting nonrigid, low-opacity dust devils that traditional methods frequently miss. We demonstrated clear advantages over generic object detection models.
Purpose and benefits: Our fine-tuned model significantly outperforms general-purpose architectures, highlighting the critical importance of domain-specific training for specialized atmospheric phenomena detection across multiple rover platforms and mission phases. This work has established a foundation for onboard implementation in future Mars missions. Our model could enable intelligent data prioritization, allowing rovers to retain high-resolution imagery of dust devil activity while applying aggressive compression to less scientifically valuable frames, optimizing limited bandwidth resources.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: A Detection and Reporting System for Spacecraft Threats
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Goal is to identify, and potentially predict when an event (e.g., anomaly, interference, etc.) has/will occur. Spacecraft anomalies / interference events can take them out of of mission.
Purpose and benefits: Shorten anomaly/event response times, potentially automating responses; Faster identification of anomalies, and even prediction of events. Keeps spacecraft on mission longer.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Neural Posterior Estimation for X-ray reflection spectroscopy: Training on complex physical models and AGN Observation-Driven Parameter Grids.
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The development of an automated inference tool tailored to extract key physical parameters from obscured AGN (active galactic nuclei) X-ray spectra by means of more complex physical models than ever before with machine learning. For our pilot work, we use the decoupled X-ray reflection MYTorus model with separate direct and scattered ("reflected") continua, as well as narrow Fe K fluorescense.
Purpose and benefits: Such a complex model poses a significant computational challenge for traditional inference techniques. To address this, we construct a physically informed, observation-driven training grid, based on the parameter space spanned by nearby AGN observed with NuSTAR. We use this grid to train a Neural Posterior Estimation (NPE) model within a simulation-based inference (SBI) context. The parameters inferred are the photon index (Γ), the global and line-of-sight equivalent neutral hydrogen column densities (N_Hs and N_Hz), and the reflection scaling factor (A_S), each with associated uncertainties.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Revolutionizing Neutron Star Parameter Inference through Machine Learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Observations of neutron stars provide estimates of their mass and radius—key parameters for constraining their still uncertain equation of state. However, the accuracy of parameter inference is limited by the complexity and computational cost of current models, with more accurate models becoming prohibitively expensive. To make parameter inference feasible, we have developed a transposed convolutional neural network (NN) that serves as a surrogate for the physics-based model within our MCMC algorithm. We test and validate this approach in a simple static vacuum regime using millisecond pulsar PSR J0030+0451 as a case study. The NN achieves a speed-up of over 400× which enables the algorithm to converge on a solution for the first time. We outline our progress towards incorporating more realistic and complex regimes, where the neural network becomes an increasingly vital component of the inference process.
Purpose and benefits: The developed neural network surrogate speeds up the computation of model pulsar X-ray and gamma-ray light curves within Markov chain Monte Carlo and multi-nested suites by a factor of ~400 for vacuum magnetic fields and ~1,000,000 for realistic force-free magnetic fields. This allows the derivation of posterior parameter distributions that otherwise would be impossible.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Curiosities of a Systems Engineer
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: AI has helped enable me as a systems engineer to dive deep into subjects outside my expertise. Helping bridging the gap between specialists and generalists.  This includes helping me identifying what appears to be an optimal 5 node constellation for a GNSS like constellation around the Moon focused on the Lunar South Pole. Which happens to be the most optimal 6 node constellation with one node removed.  Another project has been an auditory "game" demonstrating how the brain builds correlations between different signals played in the separate ears. This demonstration uses an auditory rendition of the GPS gold codes and builds an intuition on phase offset and doppler offsets.  The last "ongoing" side project has been using AI to create a software GNSS receiver leveraging the intuitions gained from auditory demo.  I have learned a few prompting tricks along the way that I would like to share.
Purpose and benefits: TEMPO spectra seem to contain enough information to predict GPP as accurately as the combination of MODIS and MERRA-2 data, despite the absence of infrared information.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Continuing Adventures in the Discovery of Multiple Star Systems with AI/ML
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Early data-driven analyses of ozone chemistry sensitivity primarily relied on "ratio-based" indicators to partially linearize the non-linear aspects of urban ozone chemistry, which are influenced by pollution levels, light, and water vapor. With the development of more sophisticated algorithms, including machine learning techniques capable of fitting high-dimensional non-linear functions, we have shown that a highly effective parameterization of net ozone production rates (PO3) can be achieved. This approach eliminates the need for empirical linearization of ozone chemistry through various indicators and allows for the primary inputs to be accurately constrained using satellite observations.
Purpose and benefits: We need a better understanding of the worldwide spatiotemporal variability of ozone production rates. This is mainly due to the limited information we can gain from supersites or aircraft data by which we can generate observationally constrained PO3. However, we offer a significantly enhanced algorithm to parameterize PO3 using retrospective aircraft observations and a handful of variables that can be primarily informed by satellite observations that provide high spatial coverage. Our work shows the long-term maps of PO3 worldwide. This work has important implications for pollution exposure and regulations and can promote the use of satellite observations as an essential component for informing emission regulations beyond the current capabilities.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Autonomous Science and Technology for Responsive Adaptation (ASTRA)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The ASTRA team is working to develop and mature capabilities for extensibility and science autonomy. Extensibility would give us the ability to add and collaborate among multi-organizational assets to form a new, distributed and disaggregated mission. Our onboard Science AI would allow missions to make decisions for future action onboard using real-time data, enabling adaptable and responsive exploration and discovery.
Purpose and benefits: Intelligent and interoperable extensible architectures for space developed and deployed under ASTRA are poised to become a “get with it or get out” technology, similar to the protocols for the internet, bluetooth, etc. We utilize two key capabilities developed by the ASTRA team in FY25: 1) Intelligent Extensible Mission Architectures (IEMA) and 2) Objective-Based Artificial Intelligence (OBAI). IEMA code includes the ability to broadcast capabilities and requests, and to join in a coordinated effort with assets that were previously unknown. OBAI code includes the ability to use established observational priorities to analyze data in real-time, make recommendations and decisions for future action based on the perceived importance of observation(s), and carry out actions in a new, coordinated multi-asset effort.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML):  A Case Study in Canopy Height Model and Canopy Height Change Prediction
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Here we introduce an updated version of Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), an end-to-end data fusion and artificial intelligence (AI) and machine learning (ML) framework tailored for Earth Observation (EO). MEOW-ML supports the full AI/ML lifecycle, from data preparation to model training and evaluation.
Purpose and benefits: It speeds up the iterative loops of data processing and model architecture development, and, critically, enables the integration of heterogeneous data sources.
A primary intent for MEOW-ML is its application in the design of New Observing Strategies (NOS) which require very large data sets with data of diverse types acquired by a variety of sensors potentially aboard multiple sub-orbital and orbital platforms.  MEOW-ML can be used to test various combinations of data types, qualities and resolutions to optimize the design of an NOS.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Sub-Saharan West Africa  Land Cover Change
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: In recent decades, Sub-Saharan West Africa has seen rapid and ongoing land cover change fueled by population growth and subsequent agricultural expansion and intensification. These changes have led to negative impacts including a decrease in land productivity, loss of local biodiversity, and a general degradation of ecosystem services, resulting in debate over whether policies to discourage this type of transformation should be introduced. However, moderate resolution satellite data and traditional remote sensing methods are insufficient at resolving land cover land use change in this region, which is dominated by small, dispersed patches of savanna-woodlands and smallholder agriculture systems (< 3 ha.) that consist of highly dynamic and often ill-defined field boundaries. In Senegal, extreme latitudinal gradients in phenology, limited availability of cloud-free wet season imagery, and widespread burnt area during the dry season add further complexity in identifying sub-hectare land cover and change.
Purpose and benefits: Thanks to the growing availability of very high resolution (VHR) imagery (< 3 m GSD) through commercial vendors and the increased accessibility of high-performance computing resources such as GPUs, we are now able to perform computationally-expensive, deep learning-based predictions on thousands of VHR observations for mapping fine-scale land cover over large areas. We have leveraged these enhanced capabilities by developing a series of deep learning models for land cover classification with WorldView 8-band imagery (2 m GSD), and have performed inference on all data available over the study domain. To assess the cost-benefit of this effort, we implemented a simple spatial resolution experiment at select locations in Senegal by pansharpening and resampling 2 m WorldView multispectral imagery and training data to alternate spatial resolutions (0.5 m, 5 m, 10 m, and 30 m) for training and inference using our deep learning models.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Near-real-time aerosol retrievals from OMPS Limb Profiler measurements
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Rapid aerosol retrievals from OMPS Limb Profiler are important to monitor large wildfires and volcanic eruptions that reach the stratosphere.  These near-real-time (NRT) retrievals can inform aviation safety and help coordinate ground-based and in situ measurements of these disruptive events.  To enable NRT retrievals from OMPS Limb Profiler, we utilize neural networks trained on the operational physics-based data product, reducing the runtime to retrieve these aerosols from ~2 hours to ~2 minutes.
Purpose and benefits: Sped up aerosol retrievals by ~60 times to enable rapid retrievals within 3 hours from acquisition, integrated imagery into NASA Worldview for quick assessment of disruptive events (volcanic eruptions, major wildfires) alongside related data products (e.g., OMPS SO2)
Topic area: Science
Development stage: Deployed
First used: 2024-06-14
US federal AI use case: Retrieving stratospheric water vapor from OMPS Limb Profiler measurements
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Stratospheric water vapor (SWV) plays an important role in atmospheric chemistry, dynamics, and radiative forcing.  OMPS Limb Profiler (LP) provides daily near-global coverage and currently operates on two satellites, with plans to launch two additional platforms in the coming years.  Developing a method to retrieve SWV from OMPS LP measurements would provide measurements of SWV into the 2030s.  However, OMPS LP has low sensitivity to SWV which inhibits the application of traditional retrieval methods.  We explored using neural networks to perform these retrievals.
Purpose and benefits: With the impending loss of Aura Microwave Limb Sounder (MLS) which has provided SWV measurements for two decades, our OMPS LP product will serve as a low-cost continuation of the MLS water vapor record until a successor instruments can be developed and launched.
Topic area: Science
Development stage: Deployed
First used: 2025-09-30
US federal AI use case: Retrieving stratospheric NO2 profiles from OMPS Limb Profiler measurements
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Stratospheric NO2 plays an important role in ozone photochemistry.  The OMPS Limb Profiler (LP) instrument provides daily near-global coverage; it is currently operating on two satellites and is planned for two additional platforms in the coming years.  However, LP is only weakly sensitive to NO2 due to coarse spectral resolution at wavelengths where NO2 absorbs, which prohibits the use of traditional retrieval techniques.
Purpose and benefits: Additional observations of stratospheric NO2 abundances can improve our understanding of stratospheric chemistry and dynamics.  Validation of this work is complicated due to limited availability of observations, but the retrieved profiles show good agreement with state-of-the-art model simulations and the retrieved integrated column shows good agreement with space-borne measurements from Aura OMI.
Topic area: Science
Development stage: Pilot
First used: 2025-03-14
US federal AI use case: Automated identification of volcanic plumes
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Use ML to reduce noise in image of satellite SO2 retrievals, and automatically identify volcanic SO2 plumes in the images.
Purpose and benefits: Automated identification and tracking of volcanic plumes will be useful in monitoring and mitigating volcanic hazards.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Correcting NASA NPOL weather radar beam blockage using machine learning approaches
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Our objective is to fill in blocked radar data using Convolutional Neural Networks (CNN). This project is in the early stages in preparing the data and model for training.
Purpose and benefits: This project will help fill in NASA NPOL blocked radar data from the Global Precipitation Measurement (GPM) mission Ground Validation (GV) field campaigns to better validate rainfall from space.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Application of ML to Detection of Anomalies in Spacecraft Health and Status Data
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: We are collaborating with the Magnetospheric Multiscale (MMS) mission to research Machine Learning (ML) techniques capable of predicting and detecting anomalies in spacecraft health and status data. We are combining historical MMS telemetry data, with known mission events and anomalies, to perform unsupervised ML techniques. We have primarily used Temporal Convolutional Networks (TCN) as they preserve the temporal nature of our data while detecting long term and short term trends in the data.
Purpose and benefits: This research provides new insights in telemetry data patterns and may reduce the time it takes to identify anomalies, allowing operators to focus on finding resolutions to ensure spacecraft health and safety.
Topic area: Science
Development stage: Deployed
First used: 2025-10-01
US federal AI use case: AI: Logic Design and Verification
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: There has been much discussion on the use of Artificial Intelligence (AI) in many fields.  This study is a first look at the use of AI in the field of digital electronics, primarily generating VHDL designs as well as self-checking test benches.  A series of experiments were conducted, giving the AI design and verification tasks for standard logic constructions as well as modest-sized applications.  The AI’s performance was analyzed in detail.
Purpose and benefits: The key benefit of using AI for these applications is the huge speed advantage for generating designs and test benches, when compared to the human.  This is for NASA and organizations that do similar engineering; not for the general public.
In the course of this study, some cases that would take a human a number of hours to complete, would be done in less than a minute by the AI.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SOLARIS-AI: Analyze Planetary Modulation in Solar Activity Cycles (Prediction of solar storms possible?)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This AI-driven project analyzes multi-decadal solar activity datasets to identify and quantify periodic signals that correlate with planetary orbital mechanics. The project processes large volumes of solar observation data (sunspot numbers, solar area measurements, magnetic field data) to detect cyclic patterns and their relationship to Jupiter's orbital period, planetary conjunctions, and synodic cycles. The project validates the hypothesis that solar activity (include magnetic activity and solar storms) is modulated by gravitational influences from the planetary system, particularly Jupiter's 11.86-year orbit.
This project leverages AI to transform decades of solar observations into actionable insights for space exploration safety and scientific understanding of solar-planetary interactions.
Purpose and benefits: Scientific Validation:
Quantitative verification of planetary influence on solar activity hypothesis
Enhanced understanding of solar-planetary field interactions
Improved solar cycle prediction accuracy through planetary position integration
Prediction of solar storms and theirs impact on Earth and on astronauts traveling to Moon and Mars?

NASA/Public Benefits:
Cost Savings: $2-5M annually in satellite protection through better space weather prediction
Time Savings: 90% reduction in manual solar cycle analysis (from months to days)
Mission Safety: Enhanced crew and equipment protection during solar maximum periods
Communication Systems: Improved reliability of satellite communications and GPS
ROI: 300-500% return through prevented satellite damage and improved mission planning
KPIs: 95% accuracy in 11-year cycle detection, 85% accuracy in solar maximum timing prediction
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Specifying Properties Of Dayside Magnetopause Reconnection From A Machine-Learning Model For The Earth'S Cusps
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Implementation of models of the magnetospheric CUSP using in-situ ion flux data from ESA's Cluster mission and DMSP spacecraft. The models produce 3-D ion flux distributions in response to solar wind parameters such as density, velocity and magnetic field.
Purpose and benefits: Can provide information regarding dayside magnetic reconnection which is the main process that describes the energy transfer between the Sun and Earth.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Strategic Text Augmentation & Research Synthesis for Physics Innovation Narratives & Evaluations"
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project is an AI-assisted daily workflow solution designed to streamline the scientific documentation process for physics researchers. The system leverages ChatGSFC's advanced language processing capabilities to transform Vilem Mikula's research notes, experimental data, and scientific observations into polished monthly reports, publication drafts, and research proposals. The platform specializes in physics domain knowledge. The project automates the time-consuming documentation aspects of scientific work, allowing researchers to focus on discovery and analysis rather than report formatting and literature synthesis.
Purpose and benefits: Efficiency Improvements:
Time Savings: 70% reduction in report preparation time 
Documentation Quality: 85% increase in consistency and completeness of research documentation
Publication Output: 30% increase in publication submission rate through streamlined drafting
Grant Success: 25% improvement in proposal acceptance through AI-enhanced presentation
Knowledge Transfer: Enhanced collaboration through better-structured communication
ROI Metrics:
Estimated 200+ hours saved annually per researcher on documentation tasks
$30,000-$50,000 value creation per researcher annually through increased research productivity
40% reduction in administrative support needs for report generation
KPI: 90% researcher satisfaction with AI-generated content quality
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AI Based VoIP Outage Prevention
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This project is to collect the live data from Voice Over Internet Protocol (VoIP) currently resides in NASA network. Then to apply an appropriate Machine Learning technique to learn and train an AI model that can predict and prevent network outages occurring.
Purpose and benefits: Saving time, labor, and money on fixing outage issues.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Quantification of Uncertainty Analysis Toolkit (QUAnT)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The Quantification of Uncertainty Analysis Toolkit (QUAnT) is a digital-twin framework that informs and guides the design process of complex, large-scale, multidisciplinary systems throughout their life cycle, while maximizing resources (e.g., size, weight, power, cost, schedule).
QUAnT enables: 1) orders-of-magnitude reductions in computational cost through a multi-fidelity simulation approach and the most efficient sampling techniques, 2) maximization of project resources through optimal allocation and task automation, 3) model predictive capabilities through data-driven learning (digital twins), 4) quantification of uncertainty to the maximum extent possible to efficiently identify risk drivers, 5) reliability analyses for rare events through advanced statistical methods.
QUAnT lays its foundations on state-of-the-art methodologies described in peer-reviewed literature and leverages artificial intelligence and machine learning to automate and facilitate several tasks. It has successfully been applied to several engineering problems including flown NASA missions such as the James Webb Space Telescope and the ongoing Mars Sample Return, where it demonstrably brought notable savings in terms of time, cost, technical quality and efficiency.
Purpose and benefits: QUAnT is expected to have a strategic, long-term, high payoff especially when used early in a project life cycle. This is advantageous as it can increase system knowledge and inform decisions when the design freedom is higher and cheaper (i.e., well before PDR, when 85% of the project’s total life cycle cost is locked in).
The anticipated ROI is tied to QUAnT’s ability to guide the mission development process while cutting down on computational cost, thus bringing notable savings in terms of time, cost and efficiency. Namely, QUAnT yields better (10%-50%) margin estimates, leading to time (<1+ year) and cost (<$200M+) savings; efficiencies in analysis cycles yield time and cost savings also thanks to the elimination of obsolete tasks and the workforce needed to perform them (as an example from a real-life case: 2.5 months, $300K for a 5-person team vs. 2 weeks, $5K for 1 person applying this technology). Finally, QUAnT is mathematically proven to provide the highest-quality results, which ensures having the best information available at hand when making decisions under uncertainty.
Note, the ROI estimates provided in here were derived from specific cases and can vary but do represent the correct order of magnitude.
Topic area: Science
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: Machine Learning effort to calculate Parker Solar Probe magnetometer offsets
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Testing various ML algorithms to model the magnetometer offset values at points in the orbit where the traditional methods are not available.
Purpose and benefits: The previous methods of offset calculations for the magnetometer are not applicable as the probe passes through its closest approach to the sun each orbit. This is also the primary science period of the mission. We are trying to find an alternative method based on housekeeping information from the spacecraft itself since that is the source of the offsets affecting the magnetometer: the spacecraft contribution to the magnetic field readings.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Identifying global dust and smoke over ocean using MODIS sensor
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Using ML RF/CNN to detect dust in MODIS images.
Purpose and benefits: Improving the dust detection than traditional physics based dust/smoke detection to provide prior knowledge for downstream retrievals and can be used for aerosol related research and public health studies.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: A Forecasting Scheme For Accelerated Harmful Algal Bloom Monitoring (FASTHAB)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: To develop an AI/ML forecast water quality model initial targeted for the Chesapeake bay.
Purpose and benefits: It will enhance the STREAM water quality nowcast system with foreacast capabilities.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Instantaneous photosynthetically available radiation models for ocean waters using neural networks
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Neural network forward models were developed to predict subsurface vertical profile of instantaneous photosynthetically available radiation (IPAR) for both open ocean and coastal waters.
Purpose and benefits: The models can be used to estimate IPAR profiles on open ocean and coastal waters efficiently. The models are integrated into joint retrieval algorithm (FastMAPOL/component) for satellite retrieval of IPAR using polarimetric measurments.
Topic area: Science
Development stage: Pre-deployment