ATLAS
Literature review and data compilation is the most time-consuming phase of the mineral resource assessment workflow (https://www.usgs.gov/media/images/usgs-mineral-resource-assess…
PFAS Groundwater Model
We are building a model (likely random forest or boosted regression tree) to predict PFAS concentrations in groundwater supplies in the US.
Submersed Aquatic Vegetation Vulnerability Evaluation Application (SAVVEA)
Aid understanding of aquatic ecosystem constraints for vegetation growth
Hydrologic predictions for the Upper Mississippi River System using a hybrid deep learning approach.
Utilize historical datasets of air temperature, precipitation, discharge, and water surface elevation to train a deep learning model to predict discharge and water surface elevati…
Integrate High-resolution Satellite Remote Sensing Data with Automated Machine Learning Techniques to Enhance Water Quality Assessment
understanding water quality in the Mississippi River using available data
Estimates of Habitat Suitability of Reed Canarygrass (Phalaris arundinacea) in Upper Mississippi River Floodplain Forest Understories
A better understanding of where in the Upper Mississippi River floodplain and invasive grass may occur.
Detecting and tracking reed canarygrass (Phalaris arundinacea) invasion in the Upper Mississippi River floodplain using remote sensing and artifial intelegence.
We have a limited understanding of how the distribution of an invasive grass has changed over time. We seek to use satellite imagery to identify and track annual changes in the di…
Developing land cover maps for barrier islands using satellite imagery
The objective of this ML/AL use case was to map land cover on barrier islands using satellite imagery.
Reducing elevation error in coastal wetland digital elevation models
The objective of this use case was to train/deploy a random forest regression model to reduce elevation error in a coastal wetland digital elevation model.
Patterns in the Landscape – Analyses of Cause and Effect
ML satellite image classification is being used to better map flooding and fire events/characteristics for more effect hazard management.
Diploid Detector/Triploid Tracker for Grass Carp
There are expected benefits from the ability to determine whether Grass Carp can reproduce (diploid, with two sets of chromosomes) and thus take over and damage an ecosystem, or w…
Frog vocalization recognition from digital recordings
Automated audio recorders make it easy to gather large amounts of digital audio recordings where frogs may be vocalizing. These recordings are too numerous to make it cost effecti…
Using deep learning to classify potential piping plover habitat along the Upper Missouri River
The U.S. Army Corps of Engineers is required to assess piping plover habitat on the Missouri River annually, per a Biological Opinion. We have been using classification tools to m…
Global food-and-water security-support analysis data (GFSAD) project [2024 INV#WO0000000107073]
1. Landsat-derived rainfed and irrigated area-product of Conterminous United States (LRIP30) 2. Landsat-derived global cropland extent product @ 30 m (LGCEP30) 3. Landsat-derived…
Fire Effects at Whiskeytown National Recreation Area and Lava Beds National Monument
We used random forests to predict the mortality class (live/dead) of lidar derived tree approximate objects and vegetation conditions.
Forest metrics at Redwood National and State Parks
We are developing a convolutional neural network in TensorFlow to predict the presence of treefall gaps based on Sentinel-2 imagery.
Lake Champlain Cyanobacteria Bloom modeling
Developing prediction for harmful algal bloom occurrence
Predicting post-fire tree mortality
We used random forests to select predictor variables for models of individual tree mortality following fire.
Multiple machine-learning estimation of groundwater levels and trends for the regional Mississippi River Valley alluvial aquifer
Improved water management at regional scales.
Experimental Forecast for River Chlorophyll
Forecast total chlorophyll concentrations in streams across various locations, part of the Ecological Forecasting Initiative (EFI) which is a collaboration between USGS and this r…
Accelerating scientific discovery through AI-driven literature synthesis and meta-analysis using large language models [2024 INV#WO0000000201793]]
A team of USGS researchers are conducting a review of literature on drought and its affects on the western United States. Due to a large volume of literature, we are using AI to f…
Storm Induced Erosion Response Network
Tool is used for separation (segmentation) of land and water in images. The resulting mask is used to calculate water levels. Tool will be used to compare to forecasted water leve…
Catalog of stock ponds using machine learning
There are many undocumented stock ponds that retain water for use by farmers and ranchers throughout the landscape in the Dakotas. The stock pond AI model could identify unknown s…
LLM-Assisted Volcanic Alert Monitoring
The primary goal of this project is to automate the monitoring of volcanic alert levels from the public websites of our partner observatories.