GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Parameter estimation, emulator building, model tuning and sensitivity analysis of the ocean carbon cycle using  ML
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work explores neural networks and other ML techniques for Earth Science topics and data sets related to global carbon cycle, ocean circulation, air-sea interactions and climate variability.
Purpose and benefits: Parameter estimation, emulator building, model tuning and sensitivity analysis of the ocean carbon cycle using  ML
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: PIC-ASIC Spectrometer Demonstration for Ultrawideband and Hyperspectral Microwave Sounding
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This family of technologies can provide extremely small multi-sensor capabilities in areas such as ultrawideband and hyperspectral sensing.
Purpose and benefits: PIC-ASIC Spectrometer Demonstration for Ultrawideband and Hyperspectral Microwave Sounding
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Portable flow inferencing device using Fourier Ptychography and Deep Learning for detection of biosignatures
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work is on a portable flow inferencing device using Fourier Ptychography and Deep Learning for detection of biosignatures based on sensors from NASA space probes.
Purpose and benefits: Portable flow inferencing device using Fourier Ptychography and Deep Learning for detection of biosignatures
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning Airport Surface Model: Airport Configuration Prediction
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The ML-airport-configuration software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting airport configurations.
Purpose and benefits: The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: RAMjET: RApid Machine lEarned Triage - AI to classify astrophysical phenomena in photometric lightcurves
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The MAtISSE project seeks to develop a new approach to rapid, real-time extraction and classification of photometric light curves using a modern differencing technique and advanced DL integrated onto a compact graphics processing unit.
Purpose and benefits: MAtISSE will develop this technique, which has the potential to greatly reduce the amount of data transmitted by an observatory, for implementation on a future CubeSat-based science payload with a thorough assessment of power requirements vs. processing and communications bandwidth. This technology will be especially applicable to small, power-limited spacecraft and may enable observations and science return that would be challenging or even impossible otherwise.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Reconstruction of cloud vertical structure
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work explores the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks. We apply the CGAN to generating two-dimensional cloud vertical structures that would be observed by the CloudSat satellite-based radar, using only the collocated Moderate-Resolution Imaging Spectrometer measurements as input.
Purpose and benefits: Reconstruction of cloud vertical structure
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Research in Aritificial Intelligence for Spacecraft Resilience (RAISR)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work is in AI/ML-assisted fault detection to complement human capability for diagnosing issues.
Purpose and benefits: Research in Aritificial Intelligence for Spacecraft Resilience (RAISR)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Science Translation with AI
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work uses natural language processing and bidirectional encoder representations fromm tansformers (BERT) to perform Science Translation on relevant documents or text files.
Purpose and benefits: Science Translation with AI
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Semi-Automatic Landslide Detection (SALaD)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Semi-Automatic Landslide Detection (SALaD). NASA's Semi-Automatic Landslide Detection (SALaD) system combines three leading-edge technologies: open-source Python packages and modules, object-based image analysis (OBIA), and machine learning (ML).
Purpose and benefits: Semi-Automatic Landslide Detection (SALaD)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning Airport Surface Model: Arrival Runway Prediction
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The ML-airport-arrival-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting arrival runway assignments.
Purpose and benefits: The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: SpectraClass: Semi-supervised learning in a Jupyter Notebook
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This is a workbench supporting interactive visual data analysis of sensor data.
Purpose and benefits: It provides an extendable interface and toolsuite which can be used to jumpstart the development of novel methods for addressing a wide range of data analysis challenges in both the earth and space sciences.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SWaP-efficient, fast-wavelength-steering and time-division-multiplexing lidar technology capable of multi-beam ranging and concurrent hyper-spectral imaging
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: We propose to integrate next-generation lidar, compact hyperspectral imaging and Artificial Intelligence (AI) technologies to provide a new remote sensing measurement capability for a broad range of Earth and planetary science objectives.
Purpose and benefits: A groundbreaking lidar module consisting of a high-pulse-rate, fast-wavelength-tuning fiber laser and time-division-multiplexing receiver will make height measurements using 60 steerable beams with drastically increased efficiency compared to the state-of-the-art. Concurrent hyperspectral imaging will greatly enhance science capabilities and real-time, AI neural network analysis of the images will enable optimized data collection and on-board processing. Incorporating these multiple emerging technologies will substantially reduce instrument size, weight and power thereby enabling lower-cost SmallSat-class missions with enhanced scientific return compared to the present day
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning Airport Surface Model: Departure Runway Prediction
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The ML-airport-departure-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting departure runway assignments.
Purpose and benefits: The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Toward Resilient Spacecraft: Artificially Intelligent Reasoning for Diagnosing Safe Modes
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This research will develop a proof-of-concept prototype for an intelligent and explainable reasoning system on-board, which is capable of diagnosis and decision making in real-time.
Purpose and benefits: We will look specifically at implementing our proof-of-concept as a way to diagnose spacecraft safe-mode events, in an attempt to disambiguate non-urgent anomalous safe mode events from more urgent safe-mode events which require a sunward burn maneuver.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Towards Scientific Autonomy:  Applying Machine Learning to MOMA Science Data
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work progresses NASA toward Scientific Autonomy by Applying Machine Learning to MOMA (Mars Organic Molecule Analyzer) Science Data to help search for signs of life.
Purpose and benefits: Towards Scientific Autonomy:  Applying Machine Learning to MOMA Science Data
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Training Data for Streamflow Estimation
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work uses convolutional neural networks to refine training data for streamflow estimation. The project will implement, test, and operationalize a system to derive effective stream width data using data from ESA's Sentinel-1 C-band radar satellite constellation, archive the data produced, and distribute the data for free and open use to train machine learning models relating to stream flow and effective stream width.
Purpose and benefits: Training Data for Streamflow Estimation
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Unification of laboratory and observational data via learning algorithms for robust models of ice microphysics
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work applies neural networks and variational autoencoders to a variety of weather analysis
Purpose and benefits: Unification of laboratory and observational data via learning algorithms for robust models of ice microphysics
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning Airport Surface Model: Estimated ON Time Prediction
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The ML-airport-estimated-ON software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting landing time.
Purpose and benefits: The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Using Machine Learning to Detect and Build Calibrated CME Datasets
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work uses "you only look once" v.3 Machine Learning techniques to detect and build calibrated CME (coronal mass ejection) datasets based on other solar sensor data.
Purpose and benefits: Using Machine Learning to Detect and Build Calibrated CME Datasets
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Vlasov Informed Super Resolution (VISR)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work uses a physics informed neural network to gain insight into the Vlasov equation (key to plasma physics) based on plasma data from the Magnetospheric Multiscale mission sensors.
Purpose and benefits: Vlasov Informed Super Resolution (VISR)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AEGIS: Autonomous Exploration for Gathering Increased Science
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: AEGIS enables intelligent targeting and data acquisition by planetary rovers. It uses computer vision techniques to identify targets (e.g., rocks) in wide angles images of the rover's surrounding terrain. If targets are found that match scientists specificiations, they are then measured autonomously using remote sensing instruments. AEGIS was first used on the MER Mission. It is currently in use on the MSL Mission to acquire data for the ChemCam instrument. It is planned for use in Spring of 2022 on the M2020 Mission to acquire data for the SuperCam instrument.
Purpose and benefits: AEGIS enables intelligent targeting and data acquisition by planetary rovers. It uses computer vision techniques to identify targets (e.g., rocks) in wide angles images of the rover's surrounding terrain. If targets are found that match scientists specificiations, they are then measured autonomously using remote sensing instruments. AEGIS was first used on the MER Mission. It is currently in use on the MSL Mission to acquire data for the ChemCam instrument. It is planned for use in Spring of 2022 on the M2020 Mission to acquire data for the SuperCam instrument.
Topic area: Science
Development stage: Deployed
First used: 2010-01-01
US federal AI use case: Agile Science
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: This project seeks to enable agile science to be conducted by remote, autonomous spacecraft beyond range of low-latency human control.
Purpose and benefits: Spacecraft systems will have to be able to conduct onboard analysis of sensor data & images to choose scientific targets of opportunity, conduct on-board prioritization, conduct geometric reasoning, and implement planning, scheduling, and execution. Future missions to primitive bodies and deep space exploration may have limited time to explore unknown targets and to react/adapt to new science opportunities.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ASPEN Mission Planner
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Based on AI techniques, ASPEN is a modular, reconfigurable application framework which is capable of supporting a wide variety of planning and scheduling applications. ASPEN provides a set of reusable software components that implement the elements commonly found in complex planning/scheduling systems, including: an expressive modeling language, a resource management system, a temporal reasoning system, and a graphical interface.
Purpose and benefits: ASPEN has been used for many space missions including: Modified Antarctic Mapping Mission, Orbital Express, Earth Observing One, and ESA's Rosetta Orbiter.
Topic area: Science
Development stage: Deployed
First used: 2002-01-01
US federal AI use case: Machine Learning Airport Surface Model: Taxi-in Prediction
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The ML-airport-taxi-in software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi in, 2) unimpeded ramp taxi in, 3) impeded AMA taxi in, and 4) impeded ramp taxi in.
Purpose and benefits: The software provides examples of how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: CLASP Coverage Planning & Scheduling
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: The Compressed Large-scale Activity Scheduling and Planning (CLASP) project is a long-range scheduler for space-based or aerial instruments that can be modeled as pushbrooms 1D line sensors dragged across the surface of the body being observed. It addresses the problem of choosing the orientation and on/off times of a pushbroom instrument or collection of pushbroom instruments such that the schedule covers as many target points as possible, but without oversubscribing memory and energy. Orientation and time of observation is derived from geometric computations that CLASP performs using the SPICE ephemeris toolkit.
Purpose and benefits: CLASP allows mission planning teams to start with a baseline mission concept and simulate the mission's science return using models of science observations, spacecraft operations, downlink, and spacecraft trajectory. This analysis can then be folded back into many aspects of mission design -- including trajectory, spacecraft design, operations concept, and downlink concept. The long planning horizons allow this analysis to span an entire mission. Actively in use for optimized scheduling for the NISAR Mission, ECOSTRESS mission (study of water needs for plant areas), EMIT mission (mineralogy of arid dusty regions), OCO-3 (atmospheric CO2) and more as well as used for numerous missions analysis and studies (e.g. 100+).
Topic area: Science
Development stage: Deployed
First used: 2008-01-01
US federal AI use case: Dynamic Targeting
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: In this approach, called Dynamic Targeting (DT), traditional broad swath instruments are supplemented by more focused instruments with narrow swath and/or limited duty cycle. These instruments use tracking information from other instruments to rapidly and continuously adjust their targeting and configuration to optimize their science.
Purpose and benefits: We aim to develop the onboard technology necessary to allow the instruments to autonomously identify critical areas of interest (e.g. plumes, thermal anomalies) or avoidance (e.g. clouds) and retarget/reconfigure to increase science productivity while accounting for instrument operations constraints such as pointing/slewing, energy, thermal, and setup. With DT, missions could control viewing geometry to extract stereo and smart instruments could autonomously track an event during an overflight to gain a more complete picture of geophysical and other events as they evolve through time and space. DT could even be used with a single instrument to map out the extent of a plume by tracing across the outer edge of the plume distinguishing between plume and non plume signals.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Enhanced AutoNav for Perseverance Rover on Mars
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: AutoNav on the Perseverance Rover autonomously plans a safe path based on stereo navigation camera images, based on multiple technologies including a tree search for decision making, Dijkstra algorithm for global path planning, stereo processing for 3D terrain reconstruction, and Approximate Clearance Evaluation (ACE) for safety checks.
Purpose and benefits: AutoNav on the Perseverance Rover autonomously plans a safe path based on stereo navigation camera images, based on multiple technologies including a tree search for decision making, Dijkstra algorithm for global path planning, stereo processing for 3D terrain reconstruction, and Approximate Clearance Evaluation (ACE) for safety checks.
Topic area: Science
Development stage: Deployed
First used: 2020-07-01
US federal AI use case: Groundwater data interpolation in California’s Central Valley using multimodal data fusion and multivariate sequence-to-sequence transformation models
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: We describe novel distributed Artificial Intelligence/Multi Agent algorithms to allocate observations in a constellation and compare their performance to centralized and highly distributed algorithms using realistic problem and orbit distributions.
Purpose and benefits: We describe novel distributed Artificial Intelligence/Multi Agent algorithms to allocate observations in a constellation and compare their performance to centralized and highly distributed algorithms using realistic problem and orbit distributions.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Hybrid On-Board and Ground-Based Processing of Massive Sensor Data (HyspIRI IPM)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Future space missions will enable unprecedented monitoring of the Earth's environment and will generate immense volumes of science data. Getting this data to ground communications stations, through science processing, and delivered to end users is a tremendous challenge. On the ground, the spacecraft's orbit is projected, and automated mission-planning tools determine which onboard-processing mode the spacecraft should use. The orbit determines the type of terrain that the spacecraft would be overflying—land, ice, coast, or ocean, for instance. Each terrain mask implies a set of requested modes and priorities.
Purpose and benefits: Future space missions will enable unprecedented monitoring of the Earth's environment and will generate immense volumes of science data. Getting this data to ground communications stations, through science processing, and delivered to end users is a tremendous challenge. On the ground, the spacecraft's orbit is projected, and automated mission-planning tools determine which onboard-processing mode the spacecraft should use. The orbit determines the type of terrain that the spacecraft would be overflying—land, ice, coast, or ocean, for instance. Each terrain mask implies a set of requested modes and priorities.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning Airport Surface Model: Taxi-out Prediction
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting impeded and unimpeded taxi out duration.
Purpose and benefits: The software provides examples of how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Mars2020 Rover (Perseverance)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Research, experiments, and engineering to empower future rovers with onboard autonomy; planning, scheduling & execution; path planning; onboard science; image processing; terrain classification; fault diagnosis; and location estimation.
Purpose and benefits: Meta Search: Because the onboard scheduler will be invoked many times in a given sol (Martian Day) with a range of possible contexts (due to execution variations), its non backtracking nature leaves its vulnerable to brittleness. In order to mitigate this potential brittleness, the Copilot systems perform a monte carlo based stochastic analysis to set meta parameters of the scheduler - primarily activity priority but also potentially preferred time and temporal constraints.
Topic area: Science
Development stage: Deployed
First used: 2020-07-01
US federal AI use case: MLNav (Machine Learning Navigation)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Accelerates path planning of rovers and other types of vehicles through ML-based heuristics, while guaranteeing safety through conventional, model-based collision checking.
Purpose and benefits: Accelerates path planning of rovers and other types of vehicles through ML-based heuristics, while guaranteeing safety through conventional, model-based collision checking.
Topic area: Science
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Low-speed engagements with terrain features on Mars; part of core mission parameters. Mars Rover cannot harm humans or impact rights.
First used: 2020-07-01
US federal AI use case: Neural network accelerated radiative transfer modeling
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Neural network accelerated radiative transfer modeling is intended to enhance efforts in the Earth Science domain.
Purpose and benefits: Specifically, JPL constructed a flexible radiative transfer model (RTM) that combines physics-based models and artificial neural networks with the intent of providing fast radiative transfer modeling for global imaging spectroscopy missions, as well as large-scale airborne campaigns (ABoVE, Western Diversity Time Series, FIREX-AQ, etc.)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Perseverance Rover on Mars - Terrain Relative Navigation
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: 3D machine vision via dual cameras to inform convolutional neural networks for rover navigation path planning.
Purpose and benefits: 3D machine vision via dual cameras to inform convolutional neural networks for rover navigation path planning.
Topic area: Science
Development stage: Deployed
First used: 2021-02-18
US federal AI use case: Providing visualization tools and streamlining the detection and tracking of wildfire-induced smoke plumes during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) mission
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Providing visualization tools and streamlining the detection and tracking of wildfire-induced smoke plumes during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) mission is intended to enhance efforts in the Earth Science domain.
Purpose and benefits: Specifically by providing a hybrid unsupervised/supervised data processing pipeline for data fusion and wildfire/smoke identification with unique classification products from multiple instruments for further structural understanding smoke/fire dynamics.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SCOTI (Scientific Captioning of Terrain Images)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: SCOTI (Scientific Captioning of Terrain Images) automatically generates natural language explanations of geological images taken by rovers. It uses "show-attend-tell" model consisting of CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory), trained by scientist-genarated labels on MSL images.
Purpose and benefits: SCOTI provides onboard data summarization thar would help the ground operation to selectively downlink high priority data under data bandwidth constraint.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SPOC (Soil Property and Object Classification)
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Using a convolutional neural network (CNN), SPOC (Soil Property and Object Classification) takes rover images and classifies the terrain type (e.g., sand, soil) from visual appearance.
Purpose and benefits: This ability enables rover to drive more safely.
Topic area: Science
Development stage: Deployed
First used: 2020-07-01
US federal AI use case: Trusted and exPlainable Artificial Intelligence for Saving Lives (TruePAL) Technology for First Responder Safety
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Problem addressed: Fatalities caused by emergency vehicle collisions are 4.8 times higher for emergency responders than the national average. The Trusted and exPlainable Artificial Intelligence for Saving Lives (TruePAL) project was funded by DOT to study the AI technology for saving first responders and roadside crews lives in and around active traffic. The TruePAL, an AI assistant, provides real-time warning of risks by analyzing the environment and traffic patterns to generate timely warning to drivers and roadside crews to avoid crashes. A deep neural network (DNN) is developed to detect and track vehicles, pedestrians, traffics signs, etc., and a Non-Axiomatic Reasoning System (NARS) to analyze the risk and provide prioritized warning messages. A mobile app with AI interface is developed to perform verbal communication with the first responders. The TruePAL AI assistant demonstrated real-time crash warning and human factor design capabilities using a CARLA simulator. The TruePAL project has developed cutting-edge AI tools: a Human-machine AI interface has potential to save lives for first responders and roadside crews.
Purpose and benefits: Fatalities caused by emergency vehicle collisions are 4.8 times higher for emergency responders than the national average. The Trusted and exPlainable Artificial Intelligence for Saving Lives (TruePAL) project was funded by DOT to study the AI technology for saving first responders and roadside crews lives in and around active traffic. The TruePAL, an AI assistant, provides real-time warning of risks by analyzing the environment and traffic patterns to generate timely warning to drivers and roadside crews to avoid crashes. A deep neural network (DNN) is developed to detect and track vehicles, pedestrians, traffics signs, etc., and a Non-Axiomatic Reasoning System (NARS) to analyze the risk and provide prioritized warning messages. A mobile app with AI interface is developed to perform verbal communication with the first responders. The TruePAL AI assistant demonstrated real-time crash warning and human factor design capabilities using a CARLA simulator. The TruePAL project has developed cutting-edge AI tools: a Human-machine AI interface has potential to save lives for first responders and roadside crews.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Volcano SensorWeb
Agency: National Aeronautics and Space Administration - JPL: Jet Propulsion Laboratory
Development stage: Retired
US federal AI use case: NextGen Advanced Methods: ATCSCC Webinar Speech2Text and Analysis
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: The Advanced Methods project explores the use of innovative and emerging technologies to drive post operational analysis of Traffic Management for aircraft. Technologies such as machine learning, (ML), artificial intelligence (AI), and advanced data analytics for use in improving the FAA’s traffic flow management. In this specific use case, our aim is to use deep learning to convert live ATCSCC webinar meeting conversation to text, and then apply natural language processing to the converted text data for later analysis and review.
Purpose and benefits: The Advanced Methods project explores the use of innovative and emerging technologies to drive post operational analysis of Traffic Management for aircraft. Technologies such as machine learning, (ML), artificial intelligence (AI), and advanced data analytics for use in improving the FAA’s traffic flow management. In this specific use case, our aim is to use deep learning to convert live ATCSCC webinar meeting conversation to text, and then apply natural language processing to the converted text data for later analysis and review.
Topic area: Transportation
Development stage: Deployed
US federal AI use case: Classification of features in Crew Earth Observations imagery
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Feature classification in Crew Earth Observations imagery of the following classes: Earth limb, lightning, the Moon, aurora, cities (64 cities, day/night), ISS equipment. For deployment on the Gateway to Astronaut Photography
Purpose and benefits: Feature classification in Crew Earth Observations imagery of the following classes: Earth limb, lightning, the Moon, aurora, cities (64 cities, day/night), ISS equipment. For deployment on the Gateway to Astronaut Photography
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Cloud masks from Crew Earth Observations imagery
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Generation of cloud masks via segmentation in Crew Earth Observations (Crew Earth Observations) images. For deployment on the Gateway to Astronaut Photography to supplement search tags.
Purpose and benefits: Generation of cloud masks via segmentation in Crew Earth Observations (Crew Earth Observations) images. For deployment on the Gateway to Astronaut Photography to supplement search tags.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Comment Analytics Dashboard
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Developed using Python and R, the Comment Analysis Dashboard is designed to visualize sentiment scored data provided by SF to better understand how crew members feel on a day-to-day basis, allowing for improved decision making when determining how to meet crew member needs. The data visualization comes with a data handling notebook designed to give SF insights into the analytics portion of the application, i.e. using comparative analysis to select a sentiment scoring model.
Purpose and benefits: Developed using Python and R, the Comment Analysis Dashboard is designed to visualize sentiment scored data provided by SF to better understand how crew members feel on a day-to-day basis, allowing for improved decision making when determining how to meet crew member needs. The data visualization comes with a data handling notebook designed to give SF insights into the analytics portion of the application, i.e. using comparative analysis to select a sentiment scoring model.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NextGen Data Analytics: Letters of Agreement
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Today, operation constraints are documented via Standard Operating Procedures (SOP) and Letters of Agreement (LOA) and are not made available to the public in a consistent manner. SOPs are specific to an air traffic control facility and specify the procedures necessary for safe operation in the sector. LOA outline agreements establishing procedures and responsibilities between two parties (including crossing restrictions, holding patters, emergency procedure coordination, etc.) The LOA/SOPs are published internally as scanned PDFs and are the responsibility of the facility to maintain. To reduce the manual effort of tagging the documents for ease of reference, there is an opportunity to use modern data analytics and machine learning to produce and disseminate constraints in a standardized manner.
Purpose and benefits: Providing LOAs or SOPs to stakeholders will enable flight planners (pilots and vendors) to study or ingest this information and thereby plan flight trajectories that remain consistent with air traffic constraints. It is also fundamental to Next Gen capabilities to share accurate data for purposes of creating new noise abatement procedures; improve NAS information for common situational awareness and alignment to implement new tools to assist in future time-based flow management.
Topic area: Transportation
Development stage: Deployed
US federal AI use case: Crew Earth Observations automated georeferencing/geolocation
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Automated georeferencing of Crew Earth Observation images. For deployment on the Gateway to Astronaut Photography.
Purpose and benefits: Automated georeferencing of Crew Earth Observation images. For deployment on the Gateway to Astronaut Photography.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning for RFID (Radio Frequency Identification) tag localization to support logistics
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Currently have two production machine learning approaches to tackle RFID (Radio Frequency Identification) tag localization in the highly reflective environment imposed by the International Space Station. First use case, REALMRFC, is a random forest classifier model with feature engineering performed by an RFID localization expert. The second use case is P-RFIDNet, a neural network with a ResNet50 backbone. In continued work, we have leveraged transfer learning to show how P-RFIDNet can be generalized to new RFID environments with limited training data. We benchmark P-RFIDNet and REALMRFC using data from the RFID Enabled Autonomous Logistics Management (REALM) and using truth derived from the Inventory Management System (IMS).
Purpose and benefits: Currently have two production machine learning approaches to tackle RFID (Radio Frequency Identification) tag localization in the highly reflective environment imposed by the International Space Station. First use case, REALMRFC, is a random forest classifier model with feature engineering performed by an RFID localization expert. The second use case is P-RFIDNet, a neural network with a ResNet50 backbone. In continued work, we have leveraged transfer learning to show how P-RFIDNet can be generalized to new RFID environments with limited training data. We benchmark P-RFIDNet and REALMRFC using data from the RFID Enabled Autonomous Logistics Management (REALM) and using truth derived from the Inventory Management System (IMS).
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Noise suppression in Human Spaceflight audio systems
Agency: National Aeronautics and Space Administration - JSC: Johnson Space Center
Problem addressed: Noise. Is. Aggravating. Especially on voice and video calls. It’s certainly an unwelcome party to most conversations, but it has always been around.
Purpose and benefits: Until recently Machine learning (ML) and artificial intelligence (AI) have led to highly elective noise-suppression and cancellation techniques, many of which are now being used at various levels of technology stacks: in hardware, middleware, apps, and more recently, SDKs (Software Development Kits) that are now available to a broad range of developers. The industries developing these new techniques and technologies have been mostly focused on standard use cases like call centers, telephony, simple voice, and video calls that power daily business meetings, calls with friends, family, and so forth. Recently, calls are no longer just calls—increasingly, they are becoming online rooms, virtual pods, or meeting spaces where people interact in new ways or take part in activities together.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Adaptive Neural Network Molecular Dynamics
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: ML approach, based on artificial neural networks (ANNs) is used in atomistic simulations to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations.
Purpose and benefits: ML approach, based on artificial neural networks (ANNs) is used in atomistic simulations to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Aerosol and Cloud Identification in SAGE III/ISS Aerosol Extinction Profiles
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: This work uses machine learning techniques to classify aerosols in SAGE (Stratospheric Aerosol and Gas Experiment) III / ISS (International Space Station) Aerosol Extinction Profile data.
Purpose and benefits: This work uses machine learning techniques to classify aerosols in SAGE (Stratospheric Aerosol and Gas Experiment) III / ISS (International Space Station) Aerosol Extinction Profile data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: AMP: An Automated Metadata Pipeline
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: In this work, we combine ontologies and machine learning to auto-generate robust, semantically consistent, variable-level metadata records for large NASA satellite collections, and AMPed metadata supports improved data discovery and the AMP (automated data pipeline) provides API (Application Program Interface) access to subsetted data on demand.
Purpose and benefits: In this work, we combine ontologies and machine learning to auto-generate robust, semantically consistent, variable-level metadata records for large NASA satellite collections, and AMPed metadata supports improved data discovery and the AMP (automated data pipeline) provides API (Application Program Interface) access to subsetted data on demand.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Application of High-Dimensional Fuzzy K-mean Cluster Analysis to CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) / CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) Version 4.1 Feature Classifications
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: This project uses Fuzzy K-means clustering (unsupervised learning) to validate the cloud-aerosol discrimination algorithm used in the publibly-distributed CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) / CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) data products.
Purpose and benefits: This project uses Fuzzy K-means clustering (unsupervised learning) to validate the cloud-aerosol discrimination algorithm used in the publibly-distributed CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) / CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) data products.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: CERES FluxByCldTyp Data Product Narrowband-to-Broadband Algorithm Improvement Through Deep Neural Network (DNN)
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Apply DNN (Deep Neural Networks) to the CERES (Clouds and the Earth's Radiant Energy System) Flux by Cloud Type data narrowband-to-broadband algorithms to improve shortwave and longwave fluxes both for clear sky and cloudy sky conditions.
Purpose and benefits: Apply DNN (Deep Neural Networks) to the CERES (Clouds and the Earth's Radiant Energy System) Flux by Cloud Type data narrowband-to-broadband algorithms to improve shortwave and longwave fluxes both for clear sky and cloudy sky conditions.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Cloud Detection Neural Network
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Detection of clouds below and above aircraft to reduce cloud contamination and improve NASA passive remote sensing of aerosols, oceans and lands from aircraft.
Purpose and benefits: Detection of clouds below and above aircraft to reduce cloud contamination and improve NASA passive remote sensing of aerosols, oceans and lands from aircraft.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Combining Satellite and Ground-based Observations to Improve Cloud Ceiling Observations over CONUS for Aviation Weather
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: A k-nearest neighbors method is applied to use satellite observations to extend sparse ceilometer observations at surface stations to high spatiotemporal resolutions over CONUS (Continental United States)
Purpose and benefits: A k-nearest neighbors method is applied to use satellite observations to extend sparse ceilometer observations at surface stations to high spatiotemporal resolutions over CONUS (Continental United States)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Detection of multi-layered clouds from multispectral MODIS/VIIRS data using an artificial neural network trained with CALIPSO-CloudSat data.
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: Detection of multi-layered clouds from multispectral MODIS (Moderate Resolution Imaging Spectroradiometer) / VIIRS (Visible Infrared Imaging Radiometer Suite) data using an artificial neural network trained with CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) - CloudSat data.
Purpose and benefits: Detection of multi-layered clouds from multispectral MODIS (Moderate Resolution Imaging Spectroradiometer) / VIIRS (Visible Infrared Imaging Radiometer Suite) data using an artificial neural network trained with CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observatory) - CloudSat data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Developing Quantum Reservoir Computing Hardware and Software for Deep Learning
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: This study uses integrated photonic circuits, mode-locked lasers and quantum optics to perform fast computing that use that to train the recurrent neural network deep learning through the so-called “reservoir computing” technique.
Purpose and benefits: This study uses integrated photonic circuits, mode-locked lasers and quantum optics to perform fast computing that use that to train the recurrent neural network deep learning through the so-called “reservoir computing” technique.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Estimation of Multilayered Cloud Properties from Geostationary Satellite Imager Data
Agency: National Aeronautics and Space Administration - LaRC: Langley Research Center
Problem addressed: A deep neural network is developed and applied to geostationary satellite imagery data to identify and retrieve characteristics of multi-layered clouds for potential use in weather and climate applications.
Purpose and benefits: A deep neural network is developed and applied to geostationary satellite imagery data to identify and retrieve characteristics of multi-layered clouds for potential use in weather and climate applications.
Topic area: Science
Development stage: Pre-deployment