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 |
|---|---|---|---|---|---|---|
| Using machine learning to detect invasive bullfrogs [2024 INV#WO0000000109159] DOI-0210 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Deep Learning application for automated mapping of surficial landforms, surficial geological deposits, and abandoned mine sites from lidar-derived topography [2024 INV#WO0000000109153] DOI-0209 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Lava lake thermal pattern classification using self organizing maps and relationships to eruption processes at Kilauea Volcano, Hawaii [2024 INV#WO0000000109098] DOI-0204 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| InSAR and other geodetic studies at Volcanoes [2024 INV#WO0000000109093] DOI-0202 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery [2024 INV#WO0000000109078] DOI-0199 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Classifying GPS data to understand flight behavior of birds [2024 INV#WO0000000109015] DOI-0196 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Whole-lake indexing of round goby abundances with photographic catch data [2024 INV#WO0000000109010] DOI-0195 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Predicting PFAS in shallow soils in northern New England [2024 INV#WO0000000108973] DOI-0194 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Improving accuracy and precision of sonar-based estimates of fish abundance [2024 INV#WO0000000108800] DOI-0193 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine learning-based landscape feature classification using satellite and airborne imagery [2024 INV#WO0000000108791; 2024 INV#WO0000000108794] DOI-0192 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Predicting PFAS occurrence in groundwater using machine learning [2024 INV#WO0000000108780] DOI-0191 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine Learning Image Classification of Wetlands and Soil moisture [2024 INV#WO0000000108779] DOI-0190 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Contract and in-house |
| Inventorying landforms with convolutional neural networks [2024 INV#WO0000000108738; WO0000000109117] DOI-0188 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Shoreline modeling [2024 INV#WO0000000108297; 2024 INV#WO0000000109312] DOI-0185 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Sediment Transport in Coastal Environments [2024 INV#WO0000000108118] DOI-0179 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Machine learning based shoreline time-series imputation, classification and forecasting (time-series analyses) [2024 INV#WO0000000108117] DOI-0178 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| 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 |
| Well Activity Report Classification DOI-0256 · BSEE | DOI | Pre-deployment | Not high-impact | Energy and the Environment | Classical / predictive ML | Contract and in-house |
| Harmful Algal Bloom prediction and detection system for Williams Fork Reservoir [2024 INV#WO0000000184339] DOI-0244 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Use modern AI/ML approaches to gain insight into USGS unstructured data such as text [2024 INV#WO0000000131706] DOI-0240 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Parsing large quantities of text data [2024 INV#WO0000000113692] DOI-0239 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Purchased from vendor |
| Machine Learning algorithm for stream velocity prediction [2024 INV#WO0000000109319] DOI-0226 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Wildlife species recognition and distance from camera estimation [2024 INV#WO0000000109245] DOI-0220 · USGS | DOI | Pre-deployment | Not high-impact | Science | Classical / predictive ML | Developed in-house |