| 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 |
| Seismology of Magmatic Injection [2024 INV#WO0000000109214] DOI-0215 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Earthquake Catalog Development [2024 INV#WO0000000109208] DOI-0214 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Seedling Identification and Percent Growth Analysis [2024 INV#WO0000000109200] DOI-0213 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Gulf Coast Geologic Energy Machine Learning [2024 INV#WO0000000109198] DOI-0212 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| 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 |
| 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 |
| 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 |
| NCCBR Data Validation Pilot TREASURY-OFR-003 · Office of Financial Research (OFR) | TREAS | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |