AI use-case explorer
AI use cases
Search and filter every reported use case. Open a record for the agency's full description, governance answers and source provenance.
Columns (7)
| Use case | Agency | Stage | High-impact | Topic area | AI classification | Sourcing |
|---|---|---|---|---|---|---|
| Design Optimization of Turbomachinery Rotor Blades using Neural Network Surrogate Models NASA-526 · GRC: Glenn Research Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Implement machine learning in NMIS to assist with mishap coding NASA-545 · OSMA: Office of the Chief Safety & Mission Assurance | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Adaptive Problem Solving / Hyperparameter Optimization NASA-809 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Time Series Forecasting, Evaluation and Deployment (Time-FED) NASA-814 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| AI Risk-aware task and motion planning for snake-like robots in icy enviroments NASA-815 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Retrieving stratospheric NO2 profiles from OMPS Limb Profiler measurements NASA-850 · GSFC: Goddard Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Quantification of Uncertainty Analysis Toolkit (QUAnT) NASA-861 · GSFC: Goddard Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Determining if multiple isolates share the same antimicrobial resistant plasmids using short read whole genome sequencing data R0703 · HHS/CDC | HHS | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Enhancing influenza A risk assessment rubrics: leveraging predictive correlates and machine learning from in vivo experiments. R0718 · HHS/CDC | HHS | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| LCAT - Land Change Analysis Technique USDA-011 · Farm Production and Conservation | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Raster Tools USDA-017 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Genomic Analyses of Pathogen Subtypes USDA-043 · Food Safety | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Foodborne Illness Source Attribution USDA-044 · Food Safety | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Fire Resilient Landscapes USDA-074 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Spread and Balance Sample Design USDA-076 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Regression, Classification, Clustering with Hilbert Curves USDA-077 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC ND Field Station Birds and Agriculture Project USDA-100 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC LSS Bioinformatic Coding USDA-101 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC Rabies ML A.D. USDA-102 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC Rabies ML M.H. USDA-103 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC Pepin ML & AI USDA-104 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC Genetics Metadata USDA-105 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Digital Soil Mapping USDA-120 · Farm Production and Conservation | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Species Distribution Data analysis USDA-126 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Random Forest with Aquatic Effectiveness Monitoring USDA-131 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Forest fire effects remote sensing models USDA-132 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Bugnet USDA-143 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| Forest Mapping using G-LiHT airborne data in interior Alaska USDA-146 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Forest Health Protection: Survey and mapping of forest damage from insects and diseases using machine learning USDA-148 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| Forest Inventory and Cruising using GAIA AI USDA-152 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| App for predicting soil temperature during prescribed burns USDA-154 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Wildfire effects and postfire forest dynamics USDA-155 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Forest habitat mapping for marbled murrelets USDA-157 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Long-term monitoring of northern spotted owl habitat and populations USDA-160 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| VegScape/CropCASMA Data Portals USDA-172 · Research, Education and Economics | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| Machine Learning approach to predict the composition of seafloor massive sulfide deposits [2024 INV#WO0000000108420] DOI-0245 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| USGS Azure OpenAI ChatGPT [2024 INV#WO0000000154392] DOI-0241 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Prioritized Constituents: Sediment [2024 INV#WO0000000109726] DOI-0237 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Data-Driven Streamflow Drought [2024 INV#WO0000000109714] DOI-0234 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| National-Extent Groundwater Quality Prediction for the National Water Census and Regional Integrated Water Availability Assessments [2024 INV#WO0000000109709] DOI-0233 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Vegetation mapping on the Hawaiian island of Lanai [2024 INV#WO0000000109501] DOI-0229 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Population and critical habitat modeling of overwintering monarch butterflies [2024 INV#WO0000000109410] DOI-0227 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Automated otolith aging using image processing [2024 INV#WO0000000109315] DOI-0225 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine learning for tsunami source zones [2024 INV#WO0000000109313] DOI-0224 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Oceanographic, coastal, and geomorphic change analysis: data generation, QC/QA, and data management [2024 INV#WO0000000109310] DOI-0223 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Quantifying the effects of land-use change and bioenergy crop production on pollinators, wildlife, and ecosystem services in the Northern Great Plains [2024 INV#WO0000000109305] DOI-0222 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Computationally efficient emulation of spheroidal elastic deformation sources using machine learning [2024 INV#WO0000000109302] DOI-0221 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning to evaluate water quality [2024 INV#WO0000000109241] DOI-0219 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Ecological niche models for bat species [2024 INV#WO0000000109233] DOI-0218 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Quantifying Watershed Controls on Fine Sediment Flux to Lake Tahoe, California/Nevada [2024 INV#WO0000000109215] DOI-0216 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
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