GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Piloting Machine Learning Inflow Forecasts Across Reclamation [2024 INV#DOI-58]
Agency: Department of the Interior - BOR
Problem addressed: Reclamation utilizes streamflow forecasts to inform reservoir operations.
Purpose and benefits: More skillful forecasts enable improved reservoir operations, yielding benefits such as more hydropower generation, increased water deliveries, and more effective flood risk management.
Topic area: Energy and Environment
Development stage: Pilot
First used: 2025-09-17
US federal AI use case: Seasonal Water Supply Forecasting: Pyforecast [2024 INV#DOI-69]
Agency: Department of the Interior - BOR
Problem addressed: Reclamation utilizes water supply forecasts (cumulative expected inflow over multiple months) to inform reservoir operations and water allocation decisions.
Purpose and benefits: More skillful forecasts and internal forecasting tools support improved reservoir operations.
Topic area: Energy and Environment
Development stage: Pilot
First used: 2021-01-01
US federal AI use case: Using Machine Learning to Automate Crack Mapping and Structural Health Monitoring
Agency: Department of the Interior - BOR
Problem addressed: Monitoring infrastructure health, particularly crack mapping in large concrete structures is highly time consuming and can be subject to the individual performing the work.
Purpose and benefits: Automated crack mapping via machine learning trained on high-resolution imagery would increase the efficiency of this task and make the result more consistent.
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: User guides/training provided in chat form
Agency: Department of the Interior - BLM
Problem addressed: We have multiple systems with multiple user guides that are 200 plus pages. Additionally, we have a significant amount of training material. Our customer survey responses always identify that they are not usable because they are so dense and they are often needed in the field, i.e., they are too cumbersome to download and read on tablets and phones. I would like them to be available to the AI as source material so that our users can ask specific questions. I want to do this without having to rewrite and format the PDF documents. The applications are only for internal users, and the documentation is not considered sensitive. If this use case works, I have other documentation I would like to also consider in the future.
Purpose and benefits: We have 10 applications, each with user guides that range from minimal to over 200 pages. This will create efficiency by helping the staff finding relevant information when needed. This should reduce the amount of training material and user guides created
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: GraphRAG Comment Analysis and Management Solution
Agency: Department of the Interior - BLM
Problem addressed: Comment analysis - identifying and binning comments from the public.  Will intake comments from a variety of sources and AI then breaks them down by issue area.  Anticipate training that model with NEPA projects, rulemaking, and public comments on a draft document.
Purpose and benefits: Speed of comment response.  Handle the responses and consolidate responses.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: NEPA Document Generation
Agency: Department of the Interior - BLM
Problem addressed: Utilize generative AI to generate content for NEPA documentation. Will gather information from ePlanning, and with the link from other systems will be able to draw the project information from that system into ePlanning and then into a CE.  We have the templates mapped with about 35% of the information coming from either ePlanning or ePlanning/MLRS.
Purpose and benefits: Faster processing and writing of NEPA documents, which can often be time intensive.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Assessment Inventory and Monitoring (AIM) Chatbot
Agency: Department of the Interior - BLM
Problem addressed: The AIM program provides access to data from 73,000+ monitoring locations and offers tools, guidance, and workflows to support BLM staff in structured, repeatable data collection and application. While resources enhance decision-making, their volume makes discovery and use challenging, often requiring AIM team support. To streamline access, we propose a Retrieval-Augmented Generation (RAG) chatbot that uses AI to summarize documents and tools via metadata and embedded PDFs, enabling natural language queries. This will help users quickly identify and apply resources relevant to their workflows, reduce manual search time, and improve access to credible scientific evidence for decision-making. The chatbot will also free AIM staff to focus on advanced analysis by handling basic inquiries, increasing efficiency and capacity for both users and the Bureau.
Purpose and benefits: Data collected under the AIM program are made available for both internal BLM use in decision making and public consumption.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: RUDD AI Development of a Small Language Model for Land Use Planning Decisions Consolidation
Agency: Department of the Interior - BLM
Problem addressed: Consolidate Resource Management Planning Documents including but not limited to Resource Management Plans and Amendments to create a "current" decisions outputs.
Purpose and benefits: Quickly identify what decisions apply in a land use planning area.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI Trapline for Fire
Agency: Department of the Interior - BLM
Problem addressed: This project would gather various information from various websites/applications after manipulating the inputs on the webpage to get images and store them for human analysis. Create national weather synopsis. This is used for national level management briefings for fuels/weather conditions forecasts in regard to wildland fire and all hazard response, planning and resource allocation.
Purpose and benefits: Essentially it would allow SME's more time to interpret and create products instead of spending hours gathering them every day.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: General Land Office Records  Modernization
Agency: Department of the Interior - BLM
Problem addressed: Using optical character recognition and natural language processing, the GLO system will automatically extract text and data elements from scanned land record documents that define mineral and lands ownership. Specifically, publicly available land records including land patents (title deeds), survey plats and field notes, and other title documents.
Purpose and benefits: reduced labor in collecting metadata for document discovery
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: FBMS UPC Chatbot
Agency: Department of the Interior - OCIO
Problem addressed: An OIG draft finding identified a significant number of purchase requests where the "Other" UPCs were used instead of the IT related UPCs that would trigger an IT Approval. This misclassification undermines OCIO's ability to track IT spending
Purpose and benefits: DOI staff that initiate purchase requests will interact with the UPC Chatbot Assistant so that the correct UPC code is used especially when planned purchases are IT related.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Aveva Predictive Analytics (APA)
Agency: Department of the Interior - BOR
Problem addressed: A means to predict and prevent failure is at the forefront of operations and maintenance.  One of the best economical means to accomplish this is through data anomaly detection and predictive maintenance for power generation applications.
Purpose and benefits: The maintenance savings to Reclamation for the successful implementation of predictive maintenance is estimated to exceed $60 million dollars 
per year when implemented across Reclamation’s hydropower fleet.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Invasive Carp Harvest Predictive Model
Agency: Department of the Interior - USGS
Problem addressed: identify where invasive carp are congregating in large numbers in the Mississippi River for targeted harvest
Purpose and benefits: target for commercial harvesters seeking invasive carp in the Mississippi River
Topic area: Science
Development stage: Pilot
First used: 2025-01-27
US federal AI use case: Automated power line extraction using deep learning
Agency: Department of the Interior - USGS
Problem addressed: Provide a national, consistent, powerline dataset.
Purpose and benefits: Cost savings over manual creation. Improves public access to information.
Topic area: Science
Development stage: Pilot
First used: 2025-09-01
US federal AI use case: Mapping wood and wood hazards in the Willamette Basin, Oregon
Agency: Department of the Interior - USGS
Problem addressed: 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 understand how wood is distributed throughout river systems pre and post wildfire.
Purpose and benefits: This work will help inform infrastructure hazards such as at dams and bridges, as well as to river users, such as rafters, inter-tubers, and fishermen.
Topic area: Science
Development stage: Pilot
First used: 2025-10-01
US federal AI use case: Willamette Regional IWAAs, gradient boosted ML stream temperature modeling.
Agency: Department of the Interior - USGS
Problem addressed: Improve model predictions of stream temperature across the basin over traditional methods.
Purpose and benefits: The expected benefits and outcomes are improved stream temperature predictions, which is a cornerstone of the integrated water availability assessment for the region, and methods that are transferable across the basin.
Topic area: Science
Development stage: Pilot
First used: 2025-03-12
US federal AI use case: DARPAs CriticalMAAS [2024 INV#WO0000000108419; WO0000000096527]
Agency: Department of the Interior - USGS
Problem addressed: 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 future mineral exploration.
Purpose and benefits: Assessment of the domestic availability of critical minerals. Insight into the U.S. critical mineral supply landscape. Evaluation of national critical mineral supply chains. Analysis of domestic sources and supply of critical minerals.
Topic area: Science
Development stage: Pilot
First used: 2024-09-22
US federal AI use case: National Land Cover Database (NLCD) [2024 INV#WO0000000107887]
Agency: Department of the Interior - USGS
Problem addressed: 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 of the most widely used datasets produced within the Department of Interior, with applications including natural resource management, hydrology, biodiversity and habitat, energy and minerals, natural disasters, agricultural sustainability, and more. The criticality of these data demand 1) frequent updates, and 2) a characterization of how our landscapes are changing across time and space. Our linkage of three deep learning models, including a generative transformer-based AI model, has been vital for not only improving our characterization of the landscape with NLCD, but for enabling us to produce land cover products faster and more efficiently.  The application of AI to this problem has saved the government valuable resources.
Purpose and benefits: Our revamping of the NLCD methodology with a series of 3 linked deep learning models has allowed us to improve the product, reduce latency of delivery, and to produce that product more cheaply.
Topic area: Science
Development stage: Deployed
First used: 2025-10-22
US federal AI use case: Evapotranspiration mapping and monitoring
Agency: Department of the Interior - USGS
Problem addressed: 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, ET maps help farmers and water managers track crop water use, improve irrigation efficiency, and manage limited water resources more sustainably. Beyond farming, ET mapping supports drought monitoring, groundwater management, ecosystem health assessments, and fire fuel moisture assessments by showing how water and energy cycles vary over space and time. Without remote sensing, this kind of detailed, spatially explicit information would be impossible to obtain at regional or global scales. AI was used by USGS EROS to improve our ability to map ET, using multi-layer perceptrons and DNNs.
Purpose and benefits: An improved ET product means a more informed farmer, allowing them to efficiently manage water resources and the crops that depend on them. The use of AI improves our ability to characterize surface temperature boundary conditions
Topic area: Science
Development stage: Deployed
First used: 2025-04-30
US federal AI use case: Invasive Grass Mapping
Agency: Department of the Interior - USGS
Problem addressed: 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 outcompeting native vegetation. Accurate, large-scale maps help land managers anticipate fire risk, prioritize restoration efforts, and guide grazing or herbicide treatments. Beyond fire management, these maps support wildlife habitat conservation, water resource protection, and long-term monitoring of ecosystem change, providing essential information for both local decision-making and national land management strategies.
Purpose and benefits: DNNs have greatly improved our ability to accurately map invasive annual exotic grasses. We now produce weekly estimates of invasive grasses for the western US from early April until July, providing fire and land managers with critical data.
Topic area: Science
Development stage: Deployed
First used: 2025-04-04
US federal AI use case: LANDFIRE
Agency: Department of the Interior - USGS
Problem addressed: 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 fuels data needed to support wildfire management, land-use planning, ecosystem restoration, and habitat management. By producing and updating these datasets, EROS ensures that federal, state, tribal, and local partners all work from the same trusted information, improving coordination and decision-making. The national scope and scientific rigor of EROS mapping make LANDFIRE an indispensable foundation for assessing risk, allocating resources, and sustaining resilient landscapes across the United States.
Purpose and benefits: LANDFIRE produces a variety of geospatial products to support the fire community, including Existing Vegetation Cover, Existing Vegetation Type, Existing Vegetation Height, Environmental Site Potential, Biophysical Settings, and more
Topic area: Science
Development stage: Deployed
First used: 2025-01-01
US federal AI use case: Telemetry Analysis Learning Algorithm (TALA)
Agency: Department of the Interior - USGS
Problem addressed: Applies machine learning to analyze operational data from agency-managed systems to support maintenance and reliability objectives.
Purpose and benefits: Increased efficiencies in trending and analysis of satellite health and safety telemetry.
Topic area: Other
Development stage: Deployed
First used: 2023-07-24
US federal AI use case: Pathogen identification in salmon
Agency: Department of the Interior - USGS
Problem addressed: 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 salmon. Currently, evaluation of Ichthyophonus infection in salmon requires lethal sampling, which is often prohibited in rapidly declining populations. We are using a transcriptomic approach in conjunction with random forest modelling to identify a panel of biomarkers capable of identifying Ichthyophonus in salmon using a non-lethal muscle tissue sample.
Purpose and benefits: ability to aid in the conservation of sensitive salmon species
Topic area: Science
Development stage: Pilot
First used: 2025-07-02
US federal AI use case: Machine learning in remote sensing-based wildfire and natural resource risk assessments
Agency: Department of the Interior - USGS
Problem addressed: Predict the risks of wildfire, drought, and invasive species spread on assets of value to the American public
Purpose and benefits: Predictions inform land management planning and decision-making to mitigate risk and save American taxpayers millions of dollars in damage from the additive effect of these stressors.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2024-11-01
US federal AI use case: Automated photographic identification of Eastern box turtles
Agency: Department of the Interior - USGS
Problem addressed: 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 demographic parameters.
Purpose and benefits: efficiencies in field data collection, allows data from community/citizen science to be used in monitoring activities, allows for the application of more sophisticated capture-recapture models
Topic area: Science
Development stage: Pilot
First used: 2024-09-06
US federal AI use case: Automated identification of ducks from hunter-submitted photos using deep learning models
Agency: Department of the Interior - USGS
Problem addressed: species identification of photographs of hunter-shot ducks for use in waterfowl harvest management
Purpose and benefits: cost savings and improved efficiency in an operational monitoring program
Topic area: Science
Development stage: Pilot
First used: 2024-04-01
US federal AI use case: Automating the Detection and Classification of Wildlife in Aerial Imagery [2024 INV#WO0000000109409]
Agency: Department of the Interior - USGS
Problem addressed: The tools and workflows developed by this project will be used by the Bureau of Ocean Energy Management (BOEM) to assess wildlife populations as part of planning and monitoring for offshore energy development. BOEM requires information on the environmental sensitivity of marine species and marine productivity to make informed decisions regarding offshore oil and gas energy infrastructure placement and mitigation measures. This is mandated by the Outer Continental Shelf Lands Act (OCSLA Section 18(2)(G)), which requires the Secretary of the Interior to consider these factors when determining the size, timing, and location of future lease sales for the National Oil and Gas Leasing Program.
Purpose and benefits: This work will support BOEM's activities to manage resources within the OCS Planning Areas, informing decisions about lease area selection and mitigation strategies.  Using AI will reduce the time, cost, and risk associated with aerial surveys
Topic area: Energy and Environment
Development stage: Pilot
First used: 2024-02-21
US federal AI use case: PFAS model of soils in the northeast
Agency: Department of the Interior - USGS
Problem addressed: We are predicting PFAS concentrations in soil across the northeast region
Purpose and benefits: We intend to provide a map that will allow states to determine risk areas with high soil PFAS concentrations
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ATLAS
Agency: Department of the Interior - USGS
Problem addressed: 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-assessment-workflow). Identifying and extracting datasets referenced in historical documents, journal articles and published databases is currently done using traditional methods of browsing the web, downloading manuscripts and datasets, and extracting relevant data. The AI system will automatically extract metadata about datasets from manuscripts and make it available to users in a catalog. By supporting user queries of the extracted metadata, the system will enable assessment scientists to identify data sources much more quickly. The tool is intended to support other AI tools down the line that facilitate data extraction and synthesis. The system will also be useful for tracking lineage and usage of published datasets, helping to assure data quality and assess impact.
Purpose and benefits: The AI will accelerate the mineral resource assessment process and provide a model for other types of resource assessments. By improving efficiency, accuracy, and transparency of assessment workflows, AI tools can help USGS deliver results more rapidly
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: PFAS Groundwater Model
Agency: Department of the Interior - USGS
Problem addressed: We are building a model (likely random forest or boosted regression tree) to predict PFAS concentrations in groundwater supplies in the US.
Purpose and benefits: Predictions of PFAS in groundwater across the US. The intention is that the results can serve to inform states where sampling is needed and to reduce costs associated with sampling areas that aren't likely to have PFAS.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Flor-AI: Developing a Remotely Sensed Image Classification Method for Inventory and Monitoring of Flora in Digital UAS Imagery
Agency: Department of the Interior - USGS
Problem addressed: This project supports the management of oak-pine barrens on the Necedah National Wildlife Refuge, Wisconsin. Necedah NWR staff conduct habitat management actions (prescribed burning, mowing, seeding, and herbicide application) to increase wild lupine (Lupinus perennis) abundance for the endangered Karner blue butterfly (Lycaeides melissa samuelis). This project will use uncrewed aerial systems (UAS) to collect imagery and apply artificial intelligence to efficiently process imagery and detect lupine. Model performance and the overall project workflow will inform future efforts and may be applied to additional vegetation monitoring programs.
Purpose and benefits: Benefits of using UAS and AI for this work include more efficient surveys of large landscapes, reduced costs associated with staffing for on-the-ground surveys, and faster generation of maps for planning management actions.
Topic area: Energy and Environment
Development stage: Pilot
First used: 2024-11-01
US federal AI use case: U.S. Wind Turbine Database
Agency: Department of the Interior - USGS
Problem addressed: Capture of geographic location of wind turbines from high resolution satellite imagery with object detection pipelines vs manual methods
Purpose and benefits: Cost savings, efficiency in data capture
Topic area: Energy and Environment
Development stage: Deployed
First used: 2024-06-01
US federal AI use case: Submersed Aquatic Vegetation Vulnerability Evaluation Application (SAVVEA)
Agency: Department of the Interior - USGS
Problem addressed: Aid understanding of aquatic ecosystem constraints for vegetation growth
Purpose and benefits: Define opportunities for aquatic ecosystem restoration that can cascade to improve water quality, hunting, fishing, bird watching, and recreational boating.
Topic area: Science
Development stage: Deployed
First used: 2025-04-10
US federal AI use case: Hydrologic predictions for the Upper Mississippi River System using a hybrid deep learning approach.
Agency: Department of the Interior - USGS
Problem addressed: Utilize historical datasets of air temperature, precipitation, discharge, and water surface elevation to train a deep learning model to predict discharge and water surface elevation across the Upper Mississippi River System.
Purpose and benefits: This model will be useful for filling gaps in the hydrologic record and understanding how changes in temperature and precipitation influence hydrology across the system.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Integrate High-resolution Satellite Remote Sensing Data with  Automated Machine Learning Techniques to Enhance Water Quality Assessment
Agency: Department of the Interior - USGS
Problem addressed: understanding water quality in the Mississippi River using available data
Purpose and benefits: improving understanding of water quality, prioritizing restoration of the Mississippi River
Topic area: Science
Development stage: Pilot
First used: 2024-09-02
US federal AI use case: Estimates of Habitat Suitability of Reed Canarygrass (Phalaris arundinacea) in Upper Mississippi River Floodplain Forest Understories
Agency: Department of the Interior - USGS
Problem addressed: A better understanding of where in the Upper Mississippi River floodplain and invasive grass may occur.
Purpose and benefits: This will help managers and researchers prioritize areas for restoration and further research.
Topic area: Science
Development stage: Deployed
First used: 2023-10-31
US federal AI use case: Detecting and tracking reed canarygrass (Phalaris arundinacea) invasion in the Upper Mississippi River floodplain using remote sensing and artifial intelegence.
Agency: Department of the Interior - USGS
Problem addressed: 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 distribution of the invasive grass reed canarygrass.
Purpose and benefits: This will lead to a better understanding of the dynamics (e.g., changes in inundation) that drive changes in the distribution of the invasive grass reed canarygrass to help achieve management objectives.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Developing land cover maps for barrier islands using satellite imagery
Agency: Department of the Interior - USGS
Problem addressed: The objective of this ML/AL use case was to map land cover on barrier islands using satellite imagery.
Purpose and benefits: Prediction of land cover types based on signatures from training data (i.e., pixels with class labels).
Topic area: Science
Development stage: Pilot
First used: 2023-07-19
US federal AI use case: Reducing elevation error in coastal wetland digital elevation models
Agency: Department of the Interior - USGS
Problem addressed: 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.
Purpose and benefits: The model led to an enhanced digital elevation model for coastal wetland areas.
Topic area: Science
Development stage: Pilot
First used: 2022-10-04
US federal AI use case: Patterns in the Landscape – Analyses of Cause and Effect
Agency: Department of the Interior - USGS
Problem addressed: ML satellite image classification is being used to better map flooding and fire events/characteristics for more effect hazard management.
Purpose and benefits: High quality mapping of disasters allows us to better understand the causes and effects.
Topic area: Science
Development stage: Deployed
First used: 2023-01-31
US federal AI use case: Diploid Detector/Triploid Tracker for Grass Carp
Agency: Department of the Interior - USGS
Problem addressed: 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 whether the captured Grass Carp have three sets of chromosomes (triploid) and were hatchery produced and will not reproduce: (1) the fish can be eliminated immediately (2) law enforcement can address illegalities per state per the Lacey Act (3) the ecosystem will be maintained and protected from reproductive Grass Carp.
Purpose and benefits: science for development, evaluation, and refinement of technologies for preventing the spread of Grass Carp to uninvaded waters, and for reducing populations where where they are present or established.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Frog vocalization recognition from digital recordings
Agency: Department of the Interior - USGS
Problem addressed: 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 effective to listen to all of them. AI helps us search the recordings for calls of specific species to determine if they were there without us listening to thousands of hours of recordings.
Purpose and benefits: We are using this method to screen for Cuban treefrogs, which are in invasive species in the southern U.S. This will allow us to increase our early detection and rapid response to detections at sites that were not previously known to have the species.
Topic area: Science
Development stage: Deployed
First used: 2024-11-04
US federal AI use case: Using deep learning to classify potential piping plover habitat along the Upper Missouri River
Agency: Department of the Interior - USGS
Problem addressed: 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 map these habitats for over a decade. However, these methods are outdated and we will deep learning tools to automate the mapping of these habitats. This will create efficiencies once we have a production-scale model.
Purpose and benefits: reduce time and effort, shorter customer wait times, allows shifting efforts to value-added science
Topic area: Science
Development stage: Pilot
First used: 2024-09-30
US federal AI use case: Global food-and-water security-support analysis data (GFSAD) project [2024 INV#WO0000000107073]
Agency: Department of the Interior - USGS
Problem addressed: 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 global rainfed and irrigated area product @ 30 m (LGRIP30)
Purpose and benefits: 1. assessing Nation's irrigated and rainfed cropland areas 
2. critical to assessing crop water use, crop water productivity, and crop water savings 3. providing information on global irrigated and rainfed cropland area maps and statistics
Topic area: Science
Development stage: Deployed
First used: 2017-11-30
US federal AI use case: Fire Effects at Whiskeytown National Recreation Area and Lava Beds National Monument
Agency: Department of the Interior - USGS
Problem addressed: We used random forests to predict the mortality class (live/dead) of lidar derived tree approximate objects and vegetation conditions.
Purpose and benefits: These science products will strengthen wildland fire and timber management practices.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Forest metrics at Redwood National and State Parks
Agency: Department of the Interior - USGS
Problem addressed: We are developing a convolutional neural network in TensorFlow to predict the presence of treefall gaps based on Sentinel-2 imagery.
Purpose and benefits: This information will help active timber management and will reduce wildfire risk and increase forest resilience.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Lake Champlain Cyanobacteria Bloom modeling
Agency: Department of the Interior - USGS
Problem addressed: Developing prediction for harmful algal bloom occurrence
Purpose and benefits: Better understanding of the risks of harmful algal blooms at recreational areas
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Predicting post-fire tree mortality
Agency: Department of the Interior - USGS
Problem addressed: We used random forests to select predictor variables for models of individual tree mortality following fire.
Purpose and benefits: These science products strengthen wildland fire and timber management practices.
Topic area: Science
Development stage: Deployed
First used: 2021-10-01
US federal AI use case: Multiple machine-learning estimation of groundwater levels and trends for the regional Mississippi River Valley alluvial aquifer
Agency: Department of the Interior - USGS
Problem addressed: Improved water management at regional scales.
Purpose and benefits: Prediction of groundwater levels at regional scales for better water management.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Enhancing U.S. critical mineral supply chains through AI and remote sensing mapping of legacy mine sites and tailings
Agency: Department of the Interior - USGS
Problem addressed: There is growing interest in producing critical minerals and energy-related commodities within the United States to reduce dependence on foreign sources. However, many legacy mine sites remain poorly documented, with unknown locations, extents, and conditions, limiting opportunities to reassess the potential for extracting valuable materials from existing tailings and waste piles.
Purpose and benefits: develop a scalable, data-driven framework for identifying and prioritizing legacy mine sites with potential for critical mineral recovery. offers a cost-effective, repeatable method for improving national inventories of mine waste and tailings
Topic area: Energy and Environment
Development stage: Pre-deployment
US federal AI use case: Improving recreation opportunities and access to public lands through machine learning and transportation planning
Agency: Department of the Interior - USGS
Problem addressed: Increasing access to public lands for recreation starts with effective transportation planning. However, most states lack comprehensive transportation datasets, including traffic volume estimates on roads accessing public lands.
Purpose and benefits: Temporal traffic estimates across all roads with Colorado will improve how FHWA allocates money and addresses needs for increasing access to public lands. These data will also improve CDOT and other federal agencies in their transplantation planning needs
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Experimental Forecast for River Chlorophyll
Agency: Department of the Interior - USGS
Problem addressed: Forecast total chlorophyll concentrations in streams across various locations, part of the Ecological Forecasting Initiative (EFI) which is a collaboration between USGS and this research organization.
Purpose and benefits: Part of an experimental process to visualize stream forecasts for chlorophyll, improve model refinements, improve model accuracy, test understanding of drivers of harmful algal blooms.
Topic area: Science
Development stage: Pilot
First used: 2025-02-12
US federal AI use case: Accelerating scientific discovery through AI-driven literature synthesis and meta-analysis using large language models [2024 INV#WO0000000201793]]
Agency: Department of the Interior - USGS
Problem addressed: 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 facilitate an efficient and reliable literature synthesis that can inform decision-making. These include effectively ranking most relevant literature to questions, identifying gaps in the literature, identifying data gaps, and coalescing currently known information.
Purpose and benefits: These efforts will help expedite reviewing thousands of scientific studies that can support the project, but also support future efforts of compiling relevant information for NEPA planning or related tasks.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Storm Induced Erosion Response Network
Agency: Department of the Interior - USGS
Problem addressed: 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 levels and may be displayed on USGS webpages
Purpose and benefits: Fast, efficent, and less expensive data processing than previous method, which was labor-intensive
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Catalog of stock ponds using machine learning
Agency: Department of the Interior - USGS
Problem addressed: 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 stock ponds to water managers for use in contaminant monitoring or water budget analysis and drought mitigation.
Purpose and benefits: Identifying stock ponds in North Dakota using an automated ML model rather than manually doing so increases efficiency at the state level that could free up labor for other tasks.
Topic area: Science
Development stage: Pilot
First used: 2026-04-01
US federal AI use case: LLM-Assisted Volcanic Alert Monitoring
Agency: Department of the Interior - USGS
Problem addressed: The primary goal of this project is to automate the monitoring of volcanic alert levels from the public websites of our partner observatories.
Purpose and benefits: Currently, this process can involve manual checks, which are time-consuming. This project aims to create a rapid, efficient, and resilient method for gathering this critical data, increasing the quality of our situational awareness and value
Topic area: Science
Development stage: Pilot
First used: 2025-07-31
US federal AI use case: Inference of PFAS precursor compositions
Agency: Department of the Interior - USGS
Problem addressed: This approach will allow for better identification of PFAS source zones and materials, and will enable better estimation of transport processes
Purpose and benefits: Improved understanding of PFAS compositions, sources, and transport mechanisms will improve mitigation strategies. These improvements have the potential to reduce mitigation costs while improving overall outcomes.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: RSCC and TCA projects [2024 INV#WO0000000108017]
Agency: Department of the Interior - USGS
Problem addressed: Automated identification of coastal features in remote sensing data, reduced analyst time
Purpose and benefits: Increased efficiency in identifying coastal features in remote sensing data compared to existing methods that are human analyst time intensive. Increased efficiency was tested by timing traditional methods versus AI/ML methods
Topic area: Science
Development stage: Pilot
First used: 2023-06-21
US federal AI use case: Lake-wide monitoring and assessment of Great Lakes fisheries with autonomous vehicles and image analysis
Agency: Department of the Interior - USGS
Problem addressed: Fisheries monitoring and assessment
Purpose and benefits: Improved management of economically valuable predator stocks that underpin recreational, economic, and subsistence fisheries of the Great Lakes
Topic area: Science
Development stage: Pilot
First used: 2020-10-01
US federal AI use case: Improving Prediction Capabilities for Barrier Island Landscape Change
Agency: Department of the Interior - USGS
Problem addressed: 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 forest model), and more cutting-edge methods, like foundation models (e.g. Segment Anything). AI tools that leverage human-in-the-loop methods (e.g. Doodler) enable efficient and reliable landcover map creation from aerial and satellite imagery and enhanced historical imagery that provides important long-term perspectives of landcover change. This work aids land management decision-making and risk assessments and provides data for model validation.
Purpose and benefits: More accurate and longer-term information leads to more efficient and effective resource management, protecting property and national interests.
Topic area: Science
Development stage: Pilot
First used: 2023-06-01
US federal AI use case: Coastal Change Likelihood: Synthesizing change factors using supervised learning
Agency: Department of the Interior - USGS
Problem addressed: 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 published coastal geospatial datasets that describe the coastal landscape and the hazards that affect it.
Purpose and benefits: The Coastal Change Likelihood project is a computer-aided synthesis of the factors that determine future coastal landscape change that can be used by decision makers to support adaptation, mitigation, and prioritization of coastal zone resources and infra
Topic area: Science
Development stage: Deployed
First used: 2023-02-28
US federal AI use case: Synthesizing mapping and monitoring data to understand fluctuations in prairie dog colony size and densities in Theodore Roosevelt National Park [2024 INV#WO0000000109308]
Agency: Department of the Interior - USGS
Problem addressed: 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 satellite data.
Purpose and benefits: Cost and time savings
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: USGS Flow Photo Explorer [2024 INV#WO0000000109196]
Agency: Department of the Interior - USGS
Problem addressed: The Flow Photo Explorer (FPE) is an integrated database, machine learning, and data visualization platform for monitoring streamflow and other hydrologic conditions using timelapse images.

The goal of this project is to develop new approaches for collecting hydrologic data in streams, lakes, and other waterbodies, especially in places where traditional monitoring methods and technologies are not feasible or cost-prohibitive.

FPE uses an artificial intelligence/machine learning (AI/ML) deep learning model to estimate relative streamflow using timelapse imagery. The model is trained using pairs of images for which a person (a.k.a. an annotator) has selected which of the two images in each pair appears to have more flow. From this, the model learns how to sort the images from lowest to highest apparent flow. The rankings of the sorted images then serve as indicators of the relative amount of streamflow.
Purpose and benefits: cost savings, increased safety for employees and the public
Topic area: Science
Development stage: Deployed
First used: 2021-10-01
US federal AI use case: Using Graph Neural Networks for development of nonergodic earthquake ground-motion models
Agency: Department of the Interior - USGS
Problem addressed: May ultimately enable better hazard assessment, reduces potential disaster costs, and improves the accuracy of public hazard maps and building guidelines.
Purpose and benefits: This work may help improves seismic ground motion predictions, guiding safer construction and disaster planning while reducing long-term costs.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Deep?learning Integrations into NEIC Operations [2024 INV#WO0000000109496]
Agency: Department of the Interior - USGS
Problem addressed: The AI model improves the rapid detection of earthquakes.
Purpose and benefits: Reduces the time analysts need to work on poorly located or incorrect earthquake detections.
Topic area: Science
Development stage: Deployed
First used: 2021-06-02
US federal AI use case: Modeling Rupture Directivity Effects on Ground Motion Using Neural Networks
Agency: Department of the Interior - USGS
Problem addressed: We use Artificial Neural Networks (ANNs) to more accurately modeling ground motion amplification effects caused by rupture directivity during earthquakes. These effects are complex and challenging to capture using traditional methods, which can impact the accuracy of seismic hazard assessments.
Purpose and benefits: computationally efficient method to improve seismic hazard modeling by incorporating rupture directivity effects
Topic area: Science
Development stage: Pilot
First used: 2024-06-01
US federal AI use case: [Un]supervised clustering of [non-]earthquake signals commonly recorded on regional seismic networks
Agency: Department of the Interior - USGS
Problem addressed: Surficial mass movements (SMMs), such as landslides and rockfalls, have seismic signatures distinct from other routinely-recorded seismic sources like earthquakes and explosions. This project aims to develop a classification scheme to differentiate between seismic signals generated by different sources, especially for those generated by vertical processes ("fall") versus those generated by horizontal processes ("slides").
Purpose and benefits: By automatically discriminating the different sources that generate observed seismic signals, we can more accurately catalog the events and respond to them in more timely, appropriate ways.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Wildfire Risk Assessment & Predictive Modeling
Agency: Department of the Interior - USGS
Problem addressed: Assess fire risk potential using historical data, fuel conditions, weather and other conditions.
Purpose and benefits: Minimize loss of life and property
Topic area: Emergency Mgmt
Development stage: Pilot
First used: 2023-10-01
US federal AI use case: Animas River Metals Surrogate
Agency: Department of the Interior - USGS
Problem addressed: Estimating metal concentrations in the Animas River near Cedar Hill, NM.
Purpose and benefits: Increase metal concentration temporal resolution by reducing the need to physically sample surface waters within the river system.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: MAGMA-net: Multimodal analysis for geophysical monitoring of activity
Agency: Department of the Interior - USGS
Problem addressed: Identifying volcanic deformation in satellite imagery combined with ground-based GNSS
Purpose and benefits: Automated detection of volcanic deformation in vast amounts of satellite and ground-based deformation data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Species Distribution Models for Pectis imberbis, a Rare Plant Species in Southeastern Arizona
Agency: Department of the Interior - USGS
Development stage: Retired
US federal AI use case: AI/ML For Hazard Detection in Austere Environments with Automated or Remotely-Operated Rovers [2024 INV#WO0000000107424]
Agency: Department of the Interior - USGS
Problem addressed: Identify unique or hazardous ground cover in the field of view of an autonomous robot's camera system
Purpose and benefits: Improving scientific analysis, and if deployed operationally, the preservation of investment through hazard avoidance of expensive remote or autonomous equipment
Topic area: Science
Development stage: Pilot
First used: 2024-02-21
US federal AI use case: Mapping vegetation classes to understand wildfire fuel conditions
Agency: Department of the Interior - USGS
Problem addressed: Mapping vegetation classes that can be used to understand wildfire fuel condition across space and through time.
Purpose and benefits: The machine learning mapping process we have developed can be use to more efficiently and effectively manage fuels for wildland fire
Topic area: Science
Development stage: Pilot
First used: 2025-07-01
US federal AI use case: Supervised learning for phycocyanin estimation from Sentinel-2 MSI in inland waters with independent validation
Agency: Department of the Interior - USGS
Problem addressed: Need to accurately predict specific pigment concentrations directly from satellite data
Purpose and benefits: Cost savings related to prediction of specific pigment concentrations directly from satellite data
Topic area: Science
Development stage: Pre-deployment