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 |
|---|---|---|---|---|---|---|
| Hurricane Score DHS-2408 · ICE | DHS | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Developed in-house |
| RCRA PA 78 · OECA | EPA | Deployed | High-impact | Law Enforcement | Classical / predictive ML | Contract and in-house |
| Rangeland Condition Monitoring Assessment and Projection (RCMAP) [2024 INV#WO0000000107126] DOI-0171 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| use of random forest for species distribution modeling DOI-0161 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Global food-and-water security-support analysis data (GFSAD) project [2024 INV#WO0000000107073] DOI-0095 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Frog vocalization recognition from digital recordings DOI-0092 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Patterns in the Landscape – Analyses of Cause and Effect DOI-0090 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Submersed Aquatic Vegetation Vulnerability Evaluation Application (SAVVEA) DOI-0081 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine learning in remote sensing-based wildfire and natural resource risk assessments DOI-0072 · USGS | DOI | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Telemetry Analysis Learning Algorithm (TALA) DOI-0070 · USGS | DOI | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| LANDFIRE DOI-0069 · USGS | DOI | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Pre-Effectuation Review (PER) / Targeted Denial Review (TDR) Models R3100 · Chief Information Officer, Analytics and Improvements | SSA | Deployed | High-impact | Government Benefits Processing | Classical / predictive ML | Developed in-house |
| Invoice Scanning System DOE-632 · RL - Hanford - Richland Operations Office - Hanford (EM) | DOE | Deployed | Not high-impact | Procurement and Financial Management | 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Audit Green Book Classification SBA-3 · OCFO: Office of the Chief Financial Officer | SBA | Pre-deployment | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
| Internal OFO Chatbot Recommender SBA-4 · OFO: Office of Field Operations | SBA | Pre-deployment | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| 7(a) Purchases ML Model SBA-7 · OCA: Office of Capital Access | SBA | Pre-deployment | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
| Crickets DOE-723 · SRS - ESB - Savannah River Site - Enterprise System Boundary | DOE | Pre-deployment | Not high-impact | Cybersecurity | Classical / predictive ML | Developed in-house |
| Data-driven approaches to filling missing time-series data within the San Francisco Bay-Delta [2024 INV#WO0000000107683; INV#WO0000000107488] DOI-0174 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning based shoreline detection and sea ice dynamics using coastal cameras [2024 INV#WO0000000108008] DOI-0166 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Tsunami Hazard Analysis [2024 INV#WO0000000108470] DOI-0165 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Determining the resource potential of critical minerals in seafloor massive sulfide deposits [2024 INV#WO0000000109311] DOI-0163 · USGS | DOI | Pre-deployment | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Lead attribution model DOI-0152 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| Feature extraction and characterization for update of The National Map DOI-0131 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| Supervised learning for phycocyanin estimation from Sentinel-2 MSI in inland waters with independent validation DOI-0130 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| [Un]supervised clustering of [non-]earthquake signals commonly recorded on regional seismic networks DOI-0121 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| Inference of PFAS precursor compositions DOI-0110 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| Detecting and tracking reed canarygrass (Phalaris arundinacea) invasion in the Upper Mississippi River floodplain using remote sensing and artifial intelegence. DOI-0085 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| PFAS model of soils in the northeast DOI-0076 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| PFAS Groundwater Model DOI-0078 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| Water Time-Series Record Automation Framework development DOI-0036 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Not reported |
| GovCon Match SBA-17 · OCIO: Office of the Chief Information Officer | SBA | Retired | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
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