Rangeland Condition Monitoring Assessment and Projection (RCMAP) [2024 INV#WO0000000107126]
To address the need for long-term tracking of vegetation change, scientists from the USGS and Bureau of Land Management (BLM) developed the Rangeland Condition Monitoring Assessme…
Foundation Models to Advance Earth Science [2024 INV#WO0000000107153]
Advance the understanding of Earth's conditions and processes by developing and deploying generalist AI models (Foundation Models) trained on Earth Observations from field, suborb…
Seabird and Marine Mammal Surveys Near Potential Renewable Energy Sites Offshore Central and Southern California [2024 INV#WO0000000107535]
Using rapidly developing machine- learning (ML) techniques, the USGS WERC team is developing new methods to automate the detection and counts of seabirds and marine mammals from d…
Data-driven approaches to filling missing time-series data within the San Francisco Bay-Delta [2024 INV#WO0000000107683; INV#WO0000000107488]
Environmental time-series data may suffer from gaps at a variety of time scales, significantly reducing the number of observations to understand phenomena, identify change, calibr…
Deep Learning based image segmentation [2024 INV#WO0000000107975]
Machine Learning based shoreline detection and mapping, automated data suitability analyses from satellite imagery
Using Machine Learning in USGS StreamStats to make suspended sediment and bedload predictions [2024 INV#WO0000000107977]
Getting estimates of suspended sediment and bedload in Minnesota rivers without sampling data.
National Oceanographic Partnership Program (NOPP) [2024 INV#WO0000000108018]
Machine Learning based coastal sediments assessment and prediction
Machine learning based shoreline time-series imputation, classification and forecasting (time-series analyses) [2024 INV#WO0000000108117]
data generation and QC/QA procedures, for large scale and short-term forecasting of shoreline trends
Sediment Transport in Coastal Environments [2024 INV#WO0000000108118]
time-series imputation of oceanographic time-series
Office of Grants Management (PGM) Grants Utility Tool
PGM faced growing operational and compliance challenges across the entire financial assistance lifecycle. Manual processes—project description reviews, pre-award SAM.gov validatio…
National Wildlife Disease Database (NWDD) [2024 INV#WO0000000108149; 2024 INV#WO0000000109192]
bring together various wildlife health data streams across informational domains (i.e., laboratory results, environmental observations, news media, etc.)
Wave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies [2024 INV#WO0000000108262]
separation (segmentation) of land and water in images
Cell Phone Application for Oil Spill Detection [2024 INV#WO0000000108285]
develop a model that can be used to interpret cell phone images to predict oil in environmental samples
Summarization of documents and output to ECOSphere species workflow [2024 INV#DOI-63]
The ECOSphere species workflow relies on extracting relevant ecological and biological insights from a vast and continuously growing repository of unstructured documents, currentl…
Shoreline modeling [2024 INV#WO0000000108297; 2024 INV#WO0000000109312]
predict shoreline evolution and compare their accuracy to traditional physics-based models
Everglades-Flux, Digital Surveys [2024 INV#WO0000000108630]
automatically process Normalized Difference Vegetation Index images and come up with a true value of live vegetation and fill in missing data
Tracking wetlands and water movement across watersheds [2024 INV#WO0000000108734]
Accurate prediction of flood and drought impacts requires understanding upstream surface water storage dynamics and storage capacity
Inventorying landforms with convolutional neural networks [2024 INV#WO0000000108738; WO0000000109117]
efficiently identify and inventory these features landform features from LiDAR derived topographic data images
Zero shot segmentation to expedite Quaternary geologic mapping [2024 INV#WO0000000108739; 2024 INV#WO0000000109150]
The construction of detailed geologic maps requires a lot of manual GIS data input to outline the extent of interpreted geologic features.
Machine Learning Image Classification of Wetlands and Soil moisture [2024 INV#WO0000000108779]
inform land managers, planners, and researchers about historical and current changes to human and natural environments, focused on floods, droughts, and fires
Predicting PFAS occurrence in groundwater using machine learning [2024 INV#WO0000000108780]
predict PFAS occurrence in groundwater at the depths of drinking water supplies across the conterminous U.S.
Machine learning-based landscape feature classification using satellite and airborne imagery [2024 INV#WO0000000108791; 2024 INV#WO0000000108794]
need to increase the accuracy of habitat and land cover classifications
Improving accuracy and precision of sonar-based estimates of fish abundance [2024 INV#WO0000000108800]
Sonar-based estimates of fish abundance are prone to inaccuracies that can limit their utility
Predicting PFAS in shallow soils in northern New England [2024 INV#WO0000000108973]
predict PFAS in soils across Maine, New Hampshire, and Vermont