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 |
|---|---|---|---|---|---|---|
| National Oceanographic Partnership Program (NOPP) [2024 INV#WO0000000108018] DOI-0177 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Deep Learning based image segmentation [2024 INV#WO0000000107975] DOI-0175 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Tephra classification with machine learning [2024 INV#WO0000000109095] DOI-0159 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| HOTLink: identifying elevated thermal anomalies at volcanoes [2024 INV#WO0000000109217] DOI-0158 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| target discrimination on portable radar DOI-0151 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| AI to survey boat traffic DOI-0150 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Adaptive Management with AMMonitor DOI-0144 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Random forest models for predicting water quality of inland waters from remotely sensed imagery DOI-0137 · USGS | DOI | Pilot | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Four AI systems using different strategies to identify, classify and locate both volcano-tectonic and non-traditional volcanic earthquakes DOI-0132 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Wildfire Risk Assessment & Predictive Modeling DOI-0123 · USGS | DOI | Pilot | Not high-impact | Emergency Management | Classical / predictive ML | Contract and in-house |
| Mapping vegetation classes to understand wildfire fuel conditions DOI-0129 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| AI/ML For Hazard Detection in Austere Environments with Automated or Remotely-Operated Rovers [2024 INV#WO0000000107424] DOI-0127 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Modeling Rupture Directivity Effects on Ground Motion Using Neural Networks DOI-0120 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Improving Prediction Capabilities for Barrier Island Landscape Change DOI-0114 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| RSCC and TCA projects [2024 INV#WO0000000108017] DOI-0112 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Catalog of stock ponds using machine learning DOI-0108 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Experimental Forecast for River Chlorophyll DOI-0105 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Reducing elevation error in coastal wetland digital elevation models DOI-0088 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Developing land cover maps for barrier islands using satellite imagery DOI-0087 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Integrate High-resolution Satellite Remote Sensing Data with Automated Machine Learning Techniques to Enhance Water Quality Assessment DOI-0083 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Invasive Carp Harvest Predictive Model DOI-0061 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Automated identification of ducks from hunter-submitted photos using deep learning models DOI-0074 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Pathogen identification in salmon DOI-0071 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| DARPAs CriticalMAAS [2024 INV#WO0000000108419; WO0000000096527] DOI-0065 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Willamette Regional IWAAs, gradient boosted ML stream temperature modeling. DOI-0064 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Seasonal Water Supply Forecasting: Pyforecast [2024 INV#DOI-69] DOI-0047 · BOR | DOI | Pilot | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Piloting Machine Learning Inflow Forecasts Across Reclamation [2024 INV#DOI-58] DOI-0046 · BOR | DOI | Pilot | Not high-impact | Energy and the Environment | Classical / predictive ML | Purchased from vendor |
| Anomaly Detection for Financial Transactions Related to DOI Programs DOI-0010 · OIG | DOI | Pilot | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Pilot a suite of Fiscal Service Gen AI solutions REQ-2025-02120 · Bureau of the Fiscal Service (BFS) | TREAS | Pilot | Not high-impact | Information Technology | Classical / predictive ML | Contract and in-house |
| Vendor Risk Analytics TREAS-IRS-9 · Internal Revenue Service (IRS) | TREAS | Pilot | Presumed high-impact, determined not | Procurement and Financial Management | Classical / predictive ML | Developed in-house |
| NCCBR Data Validation Pilot TREASURY-OFR-003 · Office of Financial Research (OFR) | TREAS | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Email, Document, and Composing AI Editor SBA-23 · OCPL: Office of Communications & Public Liaison | SBA | Pilot | Not high-impact | Service Delivery | Classical / predictive ML | Purchased from vendor |
| NIV Adjudication Review Recommendation Engine (ARRE) R3266 · CA | STATE | Pilot | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| MI8 Collimators Surogate Model DOE-453 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| ACORN (Autonomous Operation for Reactor Technologies) DOE-527 · NE INL - NE Idaho National Laboratory (NE) | DOE | Pilot | Not high-impact | Other | Classical / predictive ML | Contract and in-house |
| NIF Shot Analytics & Predictive Maintenance Support Pilot DOE-529 · LLNL - Lawrence Livermore National Laboratory (LFO) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Drone Imagery Analysis DOE-539 · LM HQ - Office of Legacy Management (LM) | DOE | Pilot | Not high-impact | Energy and the Environment | Classical / predictive ML | Purchased from vendor |
| Soil Moisture Modeling DOE-545 · LM HQ - Office of Legacy Management (LM) | DOE | Pilot | Not high-impact | Energy and the Environment | Classical / predictive ML | Purchased from vendor |
| Machine Learning for Linac Improved Performance DOE-60 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| In-pixel AI for future tracking detectors DOE-66 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| SONIC: AI acceleration as a service DOE-67 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Advanced Long Term Environmental Monitoring Systems (ALTEMIS) DOE-714 · SRS - SRNL - Savannah River Site - Savannah River National L | DOE | Pilot | High-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Denoising Diffusion to Accelerate Detector Simulation DOE-716 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning for Accelerator Operations Using Big Data Analytics / L-CAPE DOE-73 · FNAL - Fermi National Accelerator (SC43 OIM) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model NASA-13 · ARC: Ames Research Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine learning for prediction and factor analysis of laser forming parameters NASA-932 · MSFC: Marshall Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| RDUST Regolith Dust Universal System Tracker NASA-935 · MSFC: Marshall Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Safeguards Digital Twin DOE-564 · NE INL - NE Idaho National Laboratory (NE) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| AGN-201 DT DOE-624 · NE INL - NE Idaho National Laboratory (NE) | DOE | Pilot | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| Secure Access Service Edge (SASE) VA-25-6479 · OIT: Office of Information & Technology | VA | Pilot | High-impact | Cybersecurity | Classical / predictive ML | Purchased from vendor |