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…

Machine Learning for High-Resolution Downscaling in the Hawaiian Islands

Currently, models of global climate change lack the resolution needed to model the processes that create most of Hawai?i’s rainfall.

Classifying CWD-Infected Elk Using Recurrent Neural Networks on GPS Movement Data

Chronic Wasting Disease is difficult and costly to diagnose using traditional biological testing. However, the disease affects an elk's behavior and movement over time. By analyzi…

Refining Flood Risk Predictions in Hawai?i with Generative Machine Learning

While global climate models (GCMs) offer climate projections, their coarse spatial resolution does not allow regional characteristics to be captured accurately, which are details…

Random forest models for predicting water quality of inland waters from remotely sensed imagery

Inland waterbodies (i.e. rivers, lakes, reservoirs, ponds, etc.) can face issues of poor water quality which may pose issues to water users, water infrastructure, and ecosystems.…

Using High-Resolution Imagery and Artificial Intelligence to Support Climate Change Resilience in Agroforestry Across the Pacific

On remote Pacific islands and outer atolls, agroforestry (i.e., the cultivation and conservation of trees for agriculture) provides food security and income to local communities.…

Predicting from the past - identifying characteristics of invasion-resistant and invasion-prone waterbodies to aid horizon scanning

Machine learning and statistical modeling will be used to leverage region-wide waterbody invasion histories and datasets on the physical, biological, chemical, anthropogenic, and…

Forecasting Earthquake Ground Motion Time Series [2024 INV#WO0000000109733]

Development of a deep learning models to generate earthquake ground motion time series for potential application to Earthquake Early Warning, Operational Aftershock Forecasting, a…

Enhancing Community and Wildlife Resilience to Sea?Level Rise and Infrastructure Development in the San Pablo Baylands

Considerable public dollars will be invested in both tidal marsh restoration and transportation upgrades in the Baylands; yet the combined and interactive effects of SLR and infra…

Biotic and abiotic drivers of the prevalence of a tick and associated vector-borne disease

Ticks are one of the most important vectors of disease in North America; however, their presence in desert ecosystems is often underestimated. The Gulf Coast tick (Amblyomma macul…

Predictive AI applications for estimating water quality constituents as causal factors of harmful algal blooms.

Ensemble regressions to predict suspended sediment, total nitrogen, total phosphorus, algal pigments, and algal cell abundances and image-based estimation of suspended sediment co…

Adaptive Management with AMMonitor

Automated species identification from remote sensed data (images and audio files)

Machine learning for stream velocity prediction

To predict stream velocity from streamflow and geographic attributes.

Improved earthquake detection for research studies [2024 INV#WO0000000108499]

Deep learning methods are being used to improve detection of earthquakes to provide more complete, high-resolution catalogs that are used in research to better understand earthqua…

Machine Learning for Avalanche Frequency Modeling

The machine learning (Random Forest) was used to identify vegetation characteristics in avalanche paths. This helps determine avalanche return periods in specific avalanche paths.

discrimination among biological radar target types detected by NEXRAD

AI to survey boat traffic

We are using AI to collect boat traffic times when traveling in a specific area by scanning video. Typically, this type of data would be collected by manually watching video and…

target discrimination on portable radar

This application of machine learning is intended as a pilot effort to discriminate among radar target types, specifically between flying animals and precipitation.

Lead attribution model

Classification model identifying soils potentially contaminated by lead from battery recycling

Machine Learning for Rapid Earthquake Magnitude Estimation

A machine learning algorithm that utilizes statistics of earthquake waveforms to determine whether an earthquake is large enough to warrant an earthquake early warning alert, with…