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 |
|---|---|---|---|---|---|---|
| TreeMap and FuelMap (all versions) USDA-018 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Landscape Change Monitoring System (LCMS) USDA-019 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Cropland Data Layer USDA-022 · Research, Education and Economics | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| List Frame Deadwood Identification USDA-023 · Research, Education and Economics | USDA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Operational Water Supply Forecasting for Western US Rivers USDA-027 · Farm Production and Conservation | USDA | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
| Census of Agriculture Propensity Scores via Machne Learning USDA-035 · Research, Education and Economics | USDA | Deployed | Not high-impact | Service Delivery | Classical / predictive ML | Developed in-house |
| Survey Outlier Detection Model USDA-041 · Research, Education and Economics | USDA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Predictive Cropland Data Layer USDA-047 · Research, Education and Economics | USDA | Deployed | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| DISTRIB-II: Habitat Suitability of Eastern United States Tree USDA-051 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| US Poultry and Operations Dataset USDA-064 · Marketing and Regulatory Programs | USDA | Deployed | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| County-Level Remotely-Sensed Corn and Soybean Yield Estimation USDA-067 · Research, Education and Economics | USDA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| BirdNET to detect bird vocalizations for research and species monitoring USDA-080 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| Predictive flood modeling USDA-082 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Transportation | Classical / predictive ML | Developed in-house |
| Esri ArcGIS Pro Deep Learning Modules USDA-091 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Policy Analysis USDA-138 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| LANDFIRE USDA-167 · Natural Resources and Environment | USDA | Deployed | Not high-impact | Emergency Management | 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 |
| Yearly Incident Procurement Classification Report USDA-135 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Procurement and Financial Management | Classical / predictive ML | Developed 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 |
| Predicting fire severity potential in future wildfires in California USDA-151 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| 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 |
| 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 |
| Fire Containment Suitability (FireCON) USDA-162 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Potential Control Location Suitability USDA-163 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Alisha OCR Reader USDA-164 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Forest disease detection and screening USDA-071 · Natural Resources and Environment | USDA | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Markov random fields for mixed forests USDA-088 · Natural Resources and Environment | USDA | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
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