Automated power line extraction using deep learning
Provide a national, consistent, powerline dataset.
Mapping wood and wood hazards in the Willamette Basin, Oregon
Large wood in rivers pose hazards to people and infrastructure, particularly post wildfire. This work uses neural network models to map large wood in rivers from aerial imagery to…
Willamette Regional IWAAs, gradient boosted ML stream temperature modeling.
Improve model predictions of stream temperature across the basin over traditional methods.
DARPAs CriticalMAAS [2024 INV#WO0000000108419; WO0000000096527]
Identify patterns in the data, isolate those that correlate with known mineral deposits, and integrate these with other geospatial layers to generate a predictive map for guiding…
National Land Cover Database (NLCD) [2024 INV#WO0000000107887]
Characterization of the physical land cover is critical for managing the lands, waters, and resources of the United States. The National Land Cover Database (NLCD) is easily one o…
Evapotranspiration mapping and monitoring
Mapping evapotranspiration (ET) with remote sensing is essential because it provides a consistent, large-scale view of how water is being used across landscapes. In agriculture, E…
Invasive Grass Mapping
USGS mapping of invasive exotic annual grasses is critical because these species, such as cheatgrass, alter ecosystems by increasing the frequency and intensity of wildfires and o…
LANDFIRE
USGS EROS has provided the expertise and staff to conduct LANDFIRE mapping for over 20 years. LANDFIRE products provide the nationally consistent, high-quality vegetation and fue…
Pathogen identification in salmon
Ichthyophonus, the most ecological and economically important pathogen of wild marine fish, is hypothesized to be a major driver of premature mortality in Yukon River Chinook salm…
Automated photographic identification of Eastern box turtles
Matching photographs from a database of photos to determine capture history of individuals which can then be used in capture-recapture models to estimate population size and demog…
Automated identification of ducks from hunter-submitted photos using deep learning models
species identification of photographs of hunter-shot ducks for use in waterfowl harvest management
PFAS model of soils in the northeast
We are predicting PFAS concentrations in soil across the northeast region
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…