GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Feature extraction and characterization for update of The National Map
Agency: Department of the Interior - USGS
Problem addressed: 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.
Purpose and benefits: Increased efficiency, reduced costs, error reduction, and more timely updates to national geospatial products including maps, GIS data, and web services.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Four AI systems using different strategies to identify, classify and locate both volcano-tectonic and non-traditional volcanic earthquakes
Agency: Department of the Interior - USGS
Problem addressed: 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-trained neural networks for picking P- and S-waves in seismograms, hierarchial clustering, identifying non-traditional volcanic earthquakes (e.g., long-period, degassing events, bubble collapse, and assigning magnitudes).
Purpose and benefits: These methods will automatically detect and classify earthquakes in large data sets without human review.  In the case of identifying small earthquakes, the method has detected 4.5x more earthquakes than human review.
Topic area: Science
Development stage: Pilot
First used: 2011-01-01
US federal AI use case: Global Detection of Historical Harmful Algal Blooms via combined Satellite Data and Deep Learning Methods
Agency: Department of the Interior - USGS
Problem addressed: 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 machine learning methods.
Purpose and benefits: Provide resource managers, researchers, and the public with a novel ability to track and compare HABs occurrences across time and space, which could identify systematic trends and patterns that affect multiple inland water bodies.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning for High-Resolution Downscaling in the Hawaiian Islands
Agency: Department of the Interior - USGS
Problem addressed: Currently, models of global climate change lack the resolution needed to model the processes that create most of Hawai?i’s rainfall.
Purpose and benefits: predict precipitation at locations where no measurement data is available, using rainfall measurements from nearby locations
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Classifying CWD-Infected Elk Using Recurrent Neural Networks on GPS Movement Data
Agency: Department of the Interior - USGS
Problem addressed: 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 analyzing GPS tracking data with AI — specifically, Recurrent Neural Networks (RNNs) — the model aims to:

Automatically identify movement patterns indicative of CWD infection,

Enable earlier or non-invasive detection of disease, and

Support wildlife disease surveillance and management decisions
Purpose and benefits: helps wildlife managers detect CWD in elk early using GPS movement data, reducing the need for costly and invasive testing. It supports faster, data-driven decisions for disease control and wildlife management, protecting both animal health and resources
Topic area: Energy and Environment
Development stage: Deployed
First used: 2025-01-01
US federal AI use case: Refining Flood Risk Predictions in Hawai?i with Generative Machine Learning
Agency: Department of the Interior - USGS
Problem addressed: While global climate models (GCMs) offer climate projections, their coarse spatial resolution does not allow regional characteristics to be captured accurately, which are details crucial for managing risks related to climate hazards like floods, droughts, and heatwaves.
Purpose and benefits: Recent breakthroughs in generative artificial intelligence (AI) with neural networks offer new possibilities for for statistical downscaling.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Random forest models for predicting water quality of inland waters from remotely sensed imagery
Agency: Department of the Interior - USGS
Problem addressed: 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. While it is important to monitor the water quality of these waterbodies, it can be costly and labor intensive to continuously monitor sites across the US. This project aims to improve the efficiency of monitoring by using Machine Learning (Random Forest) models to predict two water quality parameters of waterbodies seen in remotely sensed images. These remotely sensed images span the entire Conterminous US (CONUS) and are collected approximately every 5 days, allowing for water quality monitoring at high temporal and spatial resolutions with relatively low effort and cost.
Purpose and benefits: cost savings by estimating water quality parameters across the entire country on a frequent (5 day) schedule with minimal labor. By making these modeled water quality products publicly available, it may aid in managing water resources
Topic area: Energy and Environment
Development stage: Pilot
First used: 2025-05-01
US federal AI use case: Using High-Resolution Imagery and Artificial Intelligence to Support Climate Change Resilience in Agroforestry Across the Pacific
Agency: Department of the Interior - USGS
Problem addressed: 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. Growing instability from climate change and invasive species like the coconut rhinoceros beetle threaten these resources. Actively managing and sustaining agroforestry resources requires detailed and up-to-date knowledge of forest inventories and conditions.
Purpose and benefits: The results from this work can be used by smallholder coconut farmers and processors and local and national government agencies to better manage agroforestry resources for coconut, pandanus, and other species of interest across the Pacific Island region.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Predicting from the past - identifying characteristics of invasion-resistant and invasion-prone waterbodies to aid horizon scanning
Agency: Department of the Interior - USGS
Problem addressed: Machine learning and statistical modeling will be used to leverage region-wide waterbody invasion histories and datasets on the physical, biological, chemical, anthropogenic, and geographic characteristics of these waterbodies to: a) identify  those variables that increase or decrease invasion risk, b) categorize all waterbodies in the region based on their invasion risk, and c) provide a decision support tools for managers and policy makers to identify at-risk sites.
Purpose and benefits: AI/ML will provide a decision support tool for managers and policy makers to identify waterbodies vulnerable to invasion by non-native fishes and potential actions that could be implemented to reduce risk of invasion.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Forecasting Earthquake Ground Motion Time Series [2024 INV#WO0000000109733]
Agency: Department of the Interior - USGS
Problem addressed: Development of a deep learning models to generate earthquake ground motion time series for potential application to Earthquake Early Warning, Operational Aftershock Forecasting, and the National Seismic Hazard Model.
Purpose and benefits: Improved Earthquake Early Warning, Operational Aftershock Forecasting, and the National Seismic Hazard Model
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Enhancing Community and Wildlife Resilience to Sea?Level Rise and Infrastructure Development in the San Pablo Baylands
Agency: Department of the Interior - USGS
Problem addressed: 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 infrastructure changes on wildlife habitat, local communities, and public access are largely unknown.
Purpose and benefits: The goal of our project is to understand the potential impacts of SLR and transportation redesigns on the Baylands and identify management actions that could be taken to achieve desired future habitat and public access targets.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Biotic and abiotic drivers of the prevalence of a tick and associated vector-borne disease
Agency: Department of the Interior - USGS
Problem addressed: 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 maculatum) has been recently discovered in Arizona and has the potential to transmit a bacterial spotted fever (Rickettsia parkeri) to humans and other animals. We do not yet understand why this tick has recently appeared in Arizona.
Purpose and benefits: Data will be used to inform management and conservation activities in the region and better understand the risk of emerging disease to Arizona wildlife and people.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Predictive AI applications for estimating water quality constituents as causal factors of harmful algal blooms.
Agency: Department of the Interior - USGS
Problem addressed: Ensemble regressions to predict suspended sediment, total nitrogen, total phosphorus, algal pigments, and algal cell abundances and image-based estimation of suspended sediment concentration via machine learning used in combination with custom developed python code to help build on our correlative understanding of what drives harmful algal blooms and introduces a technique to better understand and attribute causality.
Purpose and benefits: Better understanding of the causes of harmful algal blooms in order for localities and state agencies to take proactive action to prevent harmful algal blooms that potentially have impact on life and property.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Adaptive Management with AMMonitor
Agency: Department of the Interior - USGS
Problem addressed: Automated species identification from remote sensed data (images and audio files)
Purpose and benefits: Using AI to accurately identify species from remotely sensed data will save thousands of hours of personnel time previously required and would allow researchers to inexpensively capture extensive data sets on species abundances and distributions.
Topic area: Science
Development stage: Pilot
First used: 2022-01-01
US federal AI use case: Machine learning for stream velocity prediction
Agency: Department of the Interior - USGS
Problem addressed: To predict stream velocity from streamflow and geographic attributes.
Purpose and benefits: USGS possess unusually deep knowledge of biological radar targets which can readily be confused with drones, since both fall into the category of "low and slow" flying targets.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Improved earthquake detection for research studies [2024 INV#WO0000000108499]
Agency: Department of the Interior - USGS
Problem addressed: 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 earthquake occurrence, rupture processes and seismic hazard.
Purpose and benefits: Better understand earthquake occurrence, rupture processes and seismic hazard.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning for Avalanche Frequency Modeling
Agency: Department of the Interior - USGS
Problem addressed: The machine learning (Random Forest) was used to identify vegetation characteristics in avalanche paths. This helps determine avalanche return periods in specific avalanche paths.
Purpose and benefits: Benefits include results that inform avalanche forecasters, transportation departments, and infrastructure planners on estimating spatial extents of avalanche return periods.
Topic area: Science
Development stage: Deployed
First used: 2024-01-24
US federal AI use case: discrimination among biological radar target types detected by NEXRAD
Agency: Department of the Interior - USGS
Development stage: Retired
US federal AI use case: AI to survey boat traffic
Agency: Department of the Interior - USGS
Problem addressed: 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 recording boat entrance and exit times.  Using AI, we can dramatically lower the time a person monitors videos, making a more efficient use of the employee's time at work.
Purpose and benefits: cost savings
Topic area: Science
Development stage: Pilot
First used: 2024-03-20
US federal AI use case: target discrimination on portable radar
Agency: Department of the Interior - USGS
Problem addressed: This application of machine learning is intended as a pilot effort to discriminate among radar target types, specifically between flying animals and precipitation.
Purpose and benefits: quantify moth movement into the Yellowstone ecosystem, or rather, the calories they represent as a food source. Army cutworm moths are a critical but poorly understood food for grizzly bears
Topic area: Science
Development stage: Pilot
First used: 2024-06-15
US federal AI use case: Lead attribution model
Agency: Department of the Interior - USGS
Problem addressed: Classification model identifying soils potentially contaminated by lead from battery recycling
Purpose and benefits: Model is intended to be used by a state agency in California to identify soils potentially affected by battery recycling
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning for Rapid Earthquake Magnitude Estimation
Agency: Department of the Interior - USGS
Problem addressed: 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 applications to the earthquake early warning system.
Purpose and benefits: Improve accuracy and speed of earthquake magnitude determination for earthquake early warning. Public safety benefits from a faster, more accurate warning system.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: An integrated sensor network and data driven approach to satellite remote sensing of Dissolved Organic Matter
Agency: Department of the Interior - USGS
Problem addressed: AI leverages the very large water quality dataset from extensive existing in situ data at continuous monitoring stations to develop a remote sensing model with improved accuracy for  DOM estimates from satellite images
Purpose and benefits: Improved monitoring of water quality
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Machine Learning for automatic fracture mapping and rock identification [2024 INV#WO0000000109499]
Agency: Department of the Interior - USGS
Problem addressed: Machine learning algorithms are being used to improve detection and characterization of fault surface geometries using the spatial patterns of earthquake locations. We have improved our ability to generate long, continuous, fault surface representations and have implemented non-planar machine learning based fitting approaches.
Purpose and benefits: Improved fault geometries that advance our ability to accurately model earthquake hazards.
Topic area: Science
Development stage: Deployed
First used: 2025-03-01
US federal AI use case: PRObability of Streamflow PERmanence (PROSPER models) [2024 INV#WO0000000109074]
Agency: Department of the Interior - USGS
Problem addressed: predictions of reliable surface flow in streams at regional scales to inform land and water resource decisions related to water availability.
Purpose and benefits: more accurate estimates of water availability for more efficient use of field verification efforts, expected increased success of restoration or species conservations projects.
Topic area: Science
Development stage: Deployed
First used: 2019-12-01
US federal AI use case: Remote sensing of particulate and filter passing mercury species: models and proxies
Agency: Department of the Interior - USGS
Problem addressed: AI provides the framework for understanding the relationship between optical water quality parameters and non-optical contaminants to develop highly accurate remote sensing models for mercury from satellite images and field measurements
Purpose and benefits: Synoptic, spatially cohesive maps of mercury distribution in surface water
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: HOTLink: identifying elevated thermal anomalies at volcanoes [2024 INV#WO0000000109217]
Agency: Department of the Interior - USGS
Problem addressed: Moves beyond threshold-based hotspot detection algorithms using computer vision and CNN for improved detection of weka thermal signals that may be the first indiciation of volcanic unrest
Purpose and benefits: Detects 22% more hotspots at volcanoes and produces 12% fewer false positivies across multiple satellite sensor platforms to accurately characterize background and eruptive periods, as well as providing probabilistic measures of detection confidence.
Topic area: Science
Development stage: Pilot
First used: 2025-06-09
US federal AI use case: Tephra classification with machine learning [2024 INV#WO0000000109095]
Agency: Department of the Interior - USGS
Problem addressed: Layers of volcanic ash can be classified using a their geochemical components to link a the ash to the volcano it erupted from.
Purpose and benefits: This model helps probabilistically identify distal tephra layers from throughout the region, helping us better understand its long-term record of explosive volcanism.
Topic area: Science
Development stage: Pilot
First used: 2023-01-01
US federal AI use case: use of random forest for species distribution modeling
Agency: Department of the Interior - USGS
Problem addressed: We use random forest models in R as part of an ensemble species distribution modelling workflow. Random forests have been applied to model the distribution of Joshua trees, as well as other species in the Mojave Desert in support of the BLM's Mojave Desert Native Plant Program (interagency partnership). Traditional machine learning methods, including random forests and maxent, will continue to feature in our development of species distribution models.
Purpose and benefits: The use of AI in this application improves species distribution models, reduces error, and makes models more precise which provides more accurate and useful information to decision-makers atInterior agencies.
Topic area: Science
Development stage: Deployed
First used: 2023-12-14
US federal AI use case: Determining the resource potential of critical minerals in seafloor massive sulfide deposits [2024 INV#WO0000000109311]
Agency: Department of the Interior - USGS
Problem addressed: The system predicts the location of seafloor massive sulfide deposits for use as critical mineral indicators. Produces associated mapping products for public use.
Purpose and benefits: More efficient searching for seafloor critical minerals - predictions can be used to inform resource expeditions rather than using traditional guesswork planning.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Mapping ecohydrological headwater refugia
Agency: Department of the Interior - USGS
Problem addressed: This application of machine learning is being used to aggregate complex spatial data to develop a statistical model used to create detailed maps of headwater stream resources and habitat.
Purpose and benefits: cut costs by producing more accurate results when mapping headwater stream resources and habitat that can be used to help ensure water security for the nation and healthy aquatic habitat necessary to maintain safe water resources
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Tsunami Hazard Analysis [2024 INV#WO0000000108470]
Agency: Department of the Interior - USGS
Problem addressed: Improve onshore probabilistic inundation forecasts
Purpose and benefits: Better/more efficient emergency management and infrastructure planning processes
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning based shoreline detection and sea ice dynamics using coastal cameras [2024 INV#WO0000000108008]
Agency: Department of the Interior - USGS
Problem addressed: Determine shoreline location and change as well as sea ice dynamics from coastal cameras in remote communities
Purpose and benefits: Output will be incorporated into Total Water Level and Coastal Change numerical models.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine learning for streamflow forecasting [2024 INV#WO0000000109317]
Agency: Department of the Interior - USGS
Problem addressed: Improve streamflow forecasting predictions in the Willamette River basin.
Purpose and benefits: Improved understanding of the system and predictions.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning for Bat Acoustics
Agency: Department of the Interior - USGS
Problem addressed: Improving the accuracy and timeliness of species status assessments and science to support deregulation efforts.
Purpose and benefits: This effort will result in significant cost savings related to environmental review for permitting.
Topic area: Science
Development stage: Pilot
First used: 2022-08-23
US federal AI use case: CHS Q Business AI Assistant (theKraken)
Agency: Department of the Interior - USGS
Problem addressed: The AI Assistant is designed to address the challenge of quickly finding and using information spread across multiple platforms like GitLab and Confluence. Instead of employees spending valuable time searching through scattered documentation, theKraken provides a centralized tool to query, summarize, and generate content directly from CHS resources. This strengthens the agency’s mission by enabling staff to focus on high value work rather than time consuming administrative tasks.
Purpose and benefits: The AI Assistant has significantly enhanced the new employee onboarding experience by acting as a personalized guide to our data. It provides a centralized place to ask questions and quickly access information.
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-04-01
US federal AI use case: Everlaw AI Assistant for Responsive Review
Agency: Department of the Interior - OCIO
Problem addressed: Everlaw AI Assistant’s Coding Suggestions lets OTAR and DO set natural-language criteria (case, category, and code descriptions) to assess document responsiveness. By tagging documents using keywords, account numbers, and contextual clues, it reduces manual review and speeds document triage
Purpose and benefits: Reduced manual labor cost and cycle time for responsive review; more consistent and rapid identification of responsive documents; meet discovery deadlines; large scale document search; and improved classification for historical accounting.
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-09-25
US federal AI use case: Rangeland Condition Monitoring Assessment and Projection (RCMAP) [2024 INV#WO0000000107126]
Agency: Department of the Interior - USGS
Problem addressed: 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 Assessment and Projection (RCMAP) project.
Purpose and benefits: RCMAP quantifies the percent cover of ten rangeland components (annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree cover and shrub height) at yearly time-steps across the western U.S.
Topic area: Science
Development stage: Deployed
First used: 2022-12-31
US federal AI use case: Foundation Models to Advance Earth Science [2024 INV#WO0000000107153]
Agency: Department of the Interior - USGS
Problem addressed: Advance the understanding of Earth's conditions and processes by developing and deploying generalist AI models (Foundation Models) trained on Earth Observations from field, suborbital, and orbital sensors.
Purpose and benefits: These models, along with the resulting insights, will empower scientists and land user managers to achieve more while also advancing the broader field of AI and machine learning science.
Topic area: Science
Development stage: Pilot
First used: 2024-06-01
US federal AI use case: Seabird and Marine Mammal Surveys Near Potential Renewable Energy Sites Offshore Central and Southern California [2024 INV#WO0000000107535]
Agency: Department of the Interior - USGS
Problem addressed: 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 digital imagery.
Purpose and benefits: time and cost savings
Topic area: Science
Development stage: Deployed
First used: 2020-11-16
US federal AI use case: Data-driven approaches to filling missing time-series data within the San Francisco Bay-Delta [2024 INV#WO0000000107683; INV#WO0000000107488]
Agency: Department of the Interior - USGS
Problem addressed: 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, calibrate models, and predict future behavior.
Purpose and benefits: filling in missing time-series data
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Deep Learning based image segmentation [2024 INV#WO0000000107975]
Agency: Department of the Interior - USGS
Problem addressed: Machine Learning based shoreline detection and mapping, automated data suitability analyses from satellite imagery
Purpose and benefits: time savings
Topic area: Science
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: Using Machine Learning in USGS StreamStats to make suspended sediment and bedload predictions [2024 INV#WO0000000107977]
Agency: Department of the Interior - USGS
Problem addressed: Getting estimates of suspended sediment and bedload in Minnesota rivers without sampling data.
Purpose and benefits: A tool for resource managers who need estimates of suspended sediment and bedload in Minnesota rivers without sampling data.
Topic area: Science
Development stage: Deployed
First used: 2024-12-31
US federal AI use case: National Oceanographic Partnership Program (NOPP) [2024 INV#WO0000000108018]
Agency: Department of the Interior - USGS
Problem addressed: Machine Learning based coastal sediments assessment and prediction
Purpose and benefits: time savings
Topic area: Science
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: Machine learning based shoreline time-series imputation, classification and forecasting (time-series analyses) [2024 INV#WO0000000108117]
Agency: Department of the Interior - USGS
Problem addressed: data generation and QC/QA procedures, for large scale and short-term forecasting of shoreline trends
Purpose and benefits: time savings
Topic area: Science
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: Sediment Transport in Coastal Environments [2024 INV#WO0000000108118]
Agency: Department of the Interior - USGS
Problem addressed: time-series imputation of oceanographic time-series
Purpose and benefits: filling in missing data
Topic area: Science
Development stage: Pilot
First used: 2025-06-01
US federal AI use case: Office of Grants Management (PGM) Grants Utility Tool
Agency: Department of the Interior - OS
Problem addressed: PGM faced growing operational and compliance challenges across the entire financial assistance lifecycle. Manual processes—project description reviews, pre-award SAM.gov validations, and detailed budget analyses—were extremely labor-intensive, inconsistent across bureaus, and vulnerable to human error. Staff were required to review thousands of records each year, including over 8,000 project descriptions, more than 13,000 pre-award validation actions, and more than 8,000 detailed budget submissions. Each task required extensive reading, cross-checking across multiple systems, and detailed documentation. These demands strained a shrinking grants workforce, delayed internal control reviews, increased the risk of compliance failures under 2 CFR 200, and diverted staff from higher-value oversight activities. The Department needed a standardized, accurate, and scalable way to conduct internal controls testing, ensure timely eligibility checks, and complete budget reviews without overwhelming staff resources or jeopardizing compliance.
Purpose and benefits: Automated analysis increased objectivity, removed inconsistencies in how staff interpreted regulatory requirements, and provided faster, more reliable information to support program decisions.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2024-04-10
US federal AI use case: National Wildlife Disease Database (NWDD) [2024 INV#WO0000000108149; 2024 INV#WO0000000109192]
Agency: Department of the Interior - USGS
Problem addressed: bring together various wildlife health data streams across informational domains (i.e., laboratory results, environmental observations, news media, etc.)
Purpose and benefits: visualize and contextualize information from one or more sources
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Wave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies [2024 INV#WO0000000108262]
Agency: Department of the Interior - USGS
Problem addressed: separation (segmentation) of land and water in images
Purpose and benefits: ability to compare actuals to forecasted water levels
Topic area: Science
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: Cell Phone Application for Oil Spill Detection [2024 INV#WO0000000108285]
Agency: Department of the Interior - USGS
Problem addressed: develop a model that can be used to interpret cell phone images to predict oil in environmental samples
Purpose and benefits: The tool can be rapidly deployed for use in the field by the oil spill responder community.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Summarization of documents and output to ECOSphere species workflow [2024 INV#DOI-63]
Agency: Department of the Interior - FWS
Problem addressed: The ECOSphere species workflow relies on extracting relevant ecological and biological insights from a vast and continuously growing repository of unstructured documents, currently numbering in the millions. Manual review and summarization of these documents is infeasible due to scale, time constraints, and resource limitations. There is a critical need for an AI-driven solution that can automatically ingest, analyze, and summarize large volumes of scientific and technical documents, and seamlessly output structured summaries into the ECOSphere workflow. This will enhance data accessibility, accelerate species-related research, and support timely decision-making in environmental and conservation efforts.
Purpose and benefits: Implementing AI-powered document summarization for the ECOSphere species workflow will significantly enhance operational efficiency by automating the extraction of key insights from millions of unstructured documents. This will reduce manual workload, acc
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Shoreline modeling [2024 INV#WO0000000108297; 2024 INV#WO0000000109312]
Agency: Department of the Interior - USGS
Problem addressed: predict shoreline evolution and compare their accuracy to traditional physics-based models
Purpose and benefits: increased accuracy of shoreline evolution
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Everglades-Flux, Digital Surveys [2024 INV#WO0000000108630]
Agency: Department of the Interior - USGS
Problem addressed: automatically process Normalized Difference Vegetation Index images and come up with a true value of live vegetation and fill in missing data
Purpose and benefits: automatically process Normalized Difference Vegetation Index images
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Tracking wetlands and water movement across watersheds [2024 INV#WO0000000108734]
Agency: Department of the Interior - USGS
Problem addressed: Accurate prediction of flood and drought impacts requires understanding upstream surface water storage dynamics and storage capacity
Purpose and benefits: classify satellite imagery into open and vegetated water extent, use deep learning algorithms to relate daily river discharge to meteorology and surface water storage dynamics
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Inventorying landforms with convolutional neural networks [2024 INV#WO0000000108738; WO0000000109117]
Agency: Department of the Interior - USGS
Problem addressed: efficiently identify and inventory these features landform features from LiDAR derived topographic data images
Purpose and benefits: efficient identification of landform features
Topic area: Science
Development stage: Pilot
First used: 2024-02-01
US federal AI use case: Zero shot segmentation to expedite Quaternary geologic mapping [2024 INV#WO0000000108739; 2024 INV#WO0000000109150]
Agency: Department of the Interior - USGS
Problem addressed: The construction of detailed geologic maps requires a lot of manual GIS data input to outline the extent of interpreted geologic features.
Purpose and benefits: expedite the process of creating GIS data for geologic maps
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning Image Classification of Wetlands and Soil moisture [2024 INV#WO0000000108779]
Agency: Department of the Interior - USGS
Problem addressed: inform land managers, planners, and researchers about historical and current changes to human and natural environments, focused on floods, droughts, and fires
Purpose and benefits: ability to classify wetlands and soil moisture at large scales
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Predicting PFAS occurrence in groundwater using machine learning [2024 INV#WO0000000108780]
Agency: Department of the Interior - USGS
Problem addressed: predict PFAS occurrence in groundwater at the depths of drinking water supplies across the conterminous U.S.
Purpose and benefits: better understand the occurrence of PFAS in groundwater
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Machine learning-based landscape feature classification using satellite and airborne imagery [2024 INV#WO0000000108791; 2024 INV#WO0000000108794]
Agency: Department of the Interior - USGS
Problem addressed: need to increase the accuracy of habitat and land cover classifications
Purpose and benefits: enhanced accuracy of habitat and land cover classifications
Topic area: Science
Development stage: Pilot
First used: 2013-08-01
US federal AI use case: Improving accuracy and precision of sonar-based estimates of fish abundance [2024 INV#WO0000000108800]
Agency: Department of the Interior - USGS
Problem addressed: Sonar-based estimates of fish abundance are prone to inaccuracies that can limit their utility
Purpose and benefits: improved accuracy and precision of USGS's annual prey fish abundance estimates
Topic area: Science
Development stage: Pilot
First used: 2023-01-01
US federal AI use case: Predicting PFAS in shallow soils in northern New England [2024 INV#WO0000000108973]
Agency: Department of the Interior - USGS
Problem addressed: predict PFAS in soils across Maine, New Hampshire, and Vermont
Purpose and benefits: more accurate prediction of PFAS in soils across Maine, New Hampshire, and Vermont
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Whole-lake indexing of round goby abundances with photographic catch data [2024 INV#WO0000000109010]
Agency: Department of the Interior - USGS
Problem addressed: quantify abundances of one of the most abundant prey fishes in the Great Lakes, an invasive species called Round Goby
Purpose and benefits: create a more effective method of monitoring abundances of prey fish across the entirety of the Great Lakes
Topic area: Science
Development stage: Pilot
First used: 2019-02-01
US federal AI use case: Classifying GPS data to understand flight behavior of birds [2024 INV#WO0000000109015]
Agency: Department of the Interior - USGS
Problem addressed: understand under what circumstances eagles are more likely to collide with wind turbines
Purpose and benefits: better understand circumstances where eagles are more likely to collide with wind turbines
Topic area: Science
Development stage: Pilot
First used: 2019-11-01
US federal AI use case: Extracting robust, searchable data from narrative geologic descriptions [2024 INV#WO0000000109022]
Agency: Department of the Interior - USGS
Problem addressed: Extracting robust, searchable data from narrative geologic descriptions
Purpose and benefits: time savings
Topic area: Science
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: Environmental streamflows in the United States: historical patterns and predictions [2024 INV#WO0000000109075]
Agency: Department of the Interior - USGS
Problem addressed: It is important that environmental streamflow assessments by water managers consider changes in climate, land use, and water management; this cannot be done effectively without understanding historical variability and changes in environmental streamflows
Purpose and benefits: Estimates of environmental streamflows for ungaged streams
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery [2024 INV#WO0000000109078]
Agency: Department of the Interior - USGS
Problem addressed: delineate sub-surface agricultural drainage
Purpose and benefits: ability to delineate sub-surface agricultural drainage from satellite imagery
Topic area: Science
Development stage: Pilot
First used: 2023-05-11
US federal AI use case: Predicting inundation dynamics of small forested wetlands [2024 INV#WO0000000109089]
Agency: Department of the Interior - USGS
Problem addressed: better understand the wetting/drying dynamics of small wetlands relevant to amphibians
Purpose and benefits: help land managers in the Upper Midwest understand the wetting/drying dynamics of small wetlands relevant to amphibians
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Climate Futures for Lizards and Snakes in Western North America [2024 INV#WO0000000109092]
Agency: Department of the Interior - USGS
Problem addressed: Identifying new management challenges to reptiles based on shifting environmental conditions
Purpose and benefits: ability to identify new management challenges to reptiles based on shifting environmental conditions
Topic area: Science
Development stage: Deployed
First used: 2024-10-13
US federal AI use case: InSAR and other geodetic studies at Volcanoes [2024 INV#WO0000000109093]
Agency: Department of the Interior - USGS
Problem addressed: recognize transient signals in combined InSAR and GPS data that may be indications of impending hazardous volcanic activity
Purpose and benefits: help predict hazardous volcanic activity
Topic area: Science
Development stage: Pilot
First used: 2024-01-01
US federal AI use case: Advancing image-based surveys to support sea duck conservation along the Pacific Flyway [2024 INV#WO0000000109096]
Agency: Department of the Interior - USGS
Problem addressed: Safety, expense, observer bias and lack of methodological consistency are rising concerns associated with observer-based surveys, making it imperative to transition to more sustainable methods.
Purpose and benefits: Digital aerial surveys (DAS) that automate counts from aerial imagery using convolutional neural network (CNN) models are one way to improve survey safety and count accuracy.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Lava lake thermal pattern classification using self organizing maps and relationships to eruption processes at Kilauea Volcano, Hawaii [2024 INV#WO0000000109098]
Agency: Department of the Interior - USGS
Problem addressed: classify lava lake thermal patterns
Purpose and benefits: ability to classify Lava lake thermal patterns from thermal infrared time-lapse imagery
Topic area: Science
Development stage: Pilot
First used: 2018-10-01
US federal AI use case: Mapping wildfire fuels in previously burned landscapes [2024 INV#WO0000000109121]
Agency: Department of the Interior - USGS
Problem addressed: understand how land management treatments affect the probability of reburning
Purpose and benefits: understand how land management treatments affect the probability of reburning
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: SAMPLE Toolbox [2024 INV#WO0000000109123]
Agency: Department of the Interior - USGS
Problem addressed: monitoring vegetation
Purpose and benefits: ability for land managers to develop plans for monitoring vegetation
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Pacific Northwest Stream Flow Permanence [2024 INV#WO0000000109137]
Agency: Department of the Interior - USGS
Problem addressed: streamflow classification of perennial versus non-perennial which is the charge of many land steward agencies
Purpose and benefits: inform management decisions that require streamflow classification of perennial versus non-perennial
Topic area: Science
Development stage: Deployed
First used: 2023-10-01
US federal AI use case: Oil Spill Response for Ice-Covered Rivers [2024 INV#WO0000000109142]
Agency: Department of the Interior - USGS
Problem addressed: The goal of this DOI Inland Oil Spill Preparedness Program (IOSPP) funded work is to provide rapid, near real-time information to oil spill response crews concerning about the safety of ice-covered areas
Purpose and benefits: provide rapid, near real-time information to oil spill response crews concerning about the safety of ice-covered areas
Topic area: Science
Development stage: Deployed
First used: 2023-08-21
US federal AI use case: Deep Learning application for automated mapping of surficial landforms, surficial geological deposits, and abandoned mine sites from lidar-derived topography [2024 INV#WO0000000109153]
Agency: Department of the Interior - USGS
Problem addressed: mapping of surficial landforms, surficial geological deposits, and abandoned mine sites
Purpose and benefits: automation of mapping
Topic area: Science
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: Using machine learning to detect invasive bullfrogs [2024 INV#WO0000000109159]
Agency: Department of the Interior - USGS
Problem addressed: Detecting bullfrogs along their invasion front in order to inform removal efforts
Purpose and benefits: rapid detection
Topic area: Science
Development stage: Pilot
First used: 2020-05-01
US federal AI use case: Predicting Sparse (Geothermal) Resources Availability by using Machine Learning [2024 INV#WO0000000109195]
Agency: Department of the Interior - USGS
Problem addressed: developing new ML metrics for evaluating model performance that work with sparse natural resources, addressing the extreme mathematical sparsity of these resources at the regional scale, and engineering new evidence layers to inform modeling workflows
Purpose and benefits: increasing the explainability, reproducibility, and accessibility of the assessment modeling process
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Gulf Coast Geologic Energy Machine Learning [2024 INV#WO0000000109198]
Agency: Department of the Interior - USGS
Problem addressed: predict expected ultimate recovery of shale oil wells
Purpose and benefits: predict total organic carbon
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Seedling Identification and Percent Growth Analysis [2024 INV#WO0000000109200]
Agency: Department of the Interior - USGS
Problem addressed: extraction of alphanumeric labels and analyze seedling growth in petri dish images
Purpose and benefits: saving time and reducing human error
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Earthquake Catalog Development [2024 INV#WO0000000109208]
Agency: Department of the Interior - USGS
Problem addressed: develop more complete and robust earthquake catalogs
Purpose and benefits: volcanic earthquake catalog enhancement using integrated detection, matched-filtering, and relocation tools
Topic area: Science
Development stage: Pilot
First used: 2021-10-01
US federal AI use case: Seismology of Magmatic Injection [2024 INV#WO0000000109214]
Agency: Department of the Interior - USGS
Problem addressed: understand the nature and dynamics of seismic sources associated with magmatic injection and magmatic transport
Purpose and benefits: greater understanding of volcanic systems
Topic area: Science
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Quantifying Watershed Controls on Fine Sediment Flux to Lake Tahoe, California/Nevada [2024 INV#WO0000000109215]
Agency: Department of the Interior - USGS
Problem addressed: estimate watershed parameters of importance that drive sediment flux
Purpose and benefits: Ability to better estimate watershed parameters of importance that drive sediment flux.
Topic area: Science
Development stage: Pilot
First used: 2019-10-01
US federal AI use case: Development of a Strategic Framework for Use and Implementation of Machine Learning in Energy Resource Program Workflows [2024 INV#WO0000000109216]
Agency: Department of the Interior - USGS
Problem addressed: development of a strategic framework for integrating Energy Resources Program science with traditional information technology related platforms
Purpose and benefits: position the ERP to more effectively deliver its unique data-driven information products
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Ecological niche models for bat species [2024 INV#WO0000000109233]
Agency: Department of the Interior - USGS
Problem addressed: We are trying to understand what environmental factors determine the presence and absence of bat species across their range.
Purpose and benefits: understand what environmental factors determine the presence and absence of bat species across their range
Topic area: Science
Development stage: Pilot
First used: 2022-01-01
US federal AI use case: Machine Learning to evaluate water quality [2024 INV#WO0000000109241]
Agency: Department of the Interior - USGS
Problem addressed: Examining the effect of physicochemical and meteorological variables on water quality indicators of harmful algal blooms in a shallow hypereutrophic lake
Purpose and benefits: Better understanding of physicochemical and meteorological variables on water quality indicators of harmful algal blooms in a shallow hypereutrophic lake
Topic area: Science
Development stage: Pilot
First used: 2024-09-30
US federal AI use case: Wildlife species recognition and distance from camera estimation [2024 INV#WO0000000109245]
Agency: Department of the Interior - USGS
Problem addressed: need reliable population estimates of animal density
Purpose and benefits: ability to obtain reliable population estimates of animal density
Topic area: Science
Development stage: Pre-deployment