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.

Inference of PFAS precursor compositions

This approach will allow for better identification of PFAS source zones and materials, and will enable better estimation of transport processes

RSCC and TCA projects [2024 INV#WO0000000108017]

Automated identification of coastal features in remote sensing data, reduced analyst time

Lake-wide monitoring and assessment of Great Lakes fisheries with autonomous vehicles and image analysis

Fisheries monitoring and assessment

Improving Prediction Capabilities for Barrier Island Landscape Change

This research uses several AI/ML tools to observe and analyze coastal landscape change at critical habitats along barrier islands. The work employs traditional ML methods (random…

Coastal Change Likelihood: Synthesizing change factors using supervised learning

A supervised machine-learning framework (support vector machine algorithm) is used to predict future decadal-scale coastal change and its primary driver by combining over 20 publi…

Synthesizing mapping and monitoring data to understand fluctuations in prairie dog colony size and densities in Theodore Roosevelt National Park [2024 INV#WO0000000109308]

Mapping prairie dog colonies in national is labor intensive and costly. One of the aims of this project is to develop open-source methods for mapping prairie dog colonies using sa…

USGS Flow Photo Explorer [2024 INV#WO0000000109196]

The Flow Photo Explorer (FPE) is an integrated database, machine learning, and data visualization platform for monitoring streamflow and other hydrologic conditions using timelaps…

Using Graph Neural Networks for development of nonergodic earthquake ground-motion models

May ultimately enable better hazard assessment, reduces potential disaster costs, and improves the accuracy of public hazard maps and building guidelines.

Deep?learning Integrations into NEIC Operations [2024 INV#WO0000000109496]

The AI model improves the rapid detection of earthquakes.

Modeling Rupture Directivity Effects on Ground Motion Using Neural Networks

We use Artificial Neural Networks (ANNs) to more accurately modeling ground motion amplification effects caused by rupture directivity during earthquakes. These effects are comple…

[Un]supervised clustering of [non-]earthquake signals commonly recorded on regional seismic networks

Surficial mass movements (SMMs), such as landslides and rockfalls, have seismic signatures distinct from other routinely-recorded seismic sources like earthquakes and explosions.…

Wildfire Risk Assessment & Predictive Modeling

Assess fire risk potential using historical data, fuel conditions, weather and other conditions.

Animas River Metals Surrogate

Estimating metal concentrations in the Animas River near Cedar Hill, NM.

MAGMA-net: Multimodal analysis for geophysical monitoring of activity

Identifying volcanic deformation in satellite imagery combined with ground-based GNSS

Species Distribution Models for Pectis imberbis, a Rare Plant Species in Southeastern Arizona

AI/ML For Hazard Detection in Austere Environments with Automated or Remotely-Operated Rovers [2024 INV#WO0000000107424]

Identify unique or hazardous ground cover in the field of view of an autonomous robot's camera system

Mapping vegetation classes to understand wildfire fuel conditions

Mapping vegetation classes that can be used to understand wildfire fuel condition across space and through time.

Supervised learning for phycocyanin estimation from Sentinel-2 MSI in inland waters with independent validation

Need to accurately predict specific pigment concentrations directly from satellite data

Feature extraction and characterization for update of The National Map

Reduce time and level of effort for update of The National Map datasets which include the 3D Elevation Program, 3D Hydrography Program, geographic names, and topographic mapping.

Four AI systems using different strategies to identify, classify and locate both volcano-tectonic and non-traditional volcanic earthquakes

Identifying and classifying volcanic earthquakes, which can be very different from traditional tectonic earthquakes. These are addressed with CNN's, hidden Markov models, and pre…

Global Detection of Historical Harmful Algal Blooms via combined Satellite Data and Deep Learning Methods

Create a global-scale geospatial database of HABs occurrences over the past 40 years and compile in-situ measured HAB events and related parameters, remotely sensed data, and mach…