GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Applying Deep Learning to Detect and Classify Ocean Wildlife
3_agency: Department of the Interior
3_abr: DOI
4_bureau: FWS
8_topic_area: Mission-Enabling
8_question_type:  
10_commercial_ai: None of the above.
11_purpose_benefits: Deep learning methods are being advanced to automate data processing and improve the cost-efficiency of remote sensing technologies for surveys covering broad geographic areas and generating very large image datasets. The FWS is partnering with the Bureau of Ocean Energy Management (BOEM), U.S. Geological Survey (USGS), academic institutions, and private contractors to accomplish these objectives. Initial focus has been on marine bird and other wildlife surveys given overlapping agency requirements for these data. A workflow is now being established to advance the imagery data from the sensors, to an AI detection model in the aircraft, to a species classification algorithm that is able to distinguish and count each species, and then a georeferenced location is obtained for each bird. More specifically, a cutting-edge artificial intelligence/deep learning algorithm has been developed to automatically detect seabirds in imagery. This detection algorithm has 80 to 90% accuracy across a wide range of seabird species. In FY25, we will continue to iterate on this model using a human-in-the-loop technique to improve model generalization.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2016
19_date_acq_dev_began: 10/1/2016
20_date_implemented: On-going development
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Data not reported by submitter and will be updated once additional information is collected
24_hisp_support: No
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Remote sensing digitial imagery and labels for bird species
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
35_demo_features:  
35_question_type:  
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/USFWS/AI-for-USFWS-Migratory-Birds
40_has_ato:  
41_system_name:  
43_infra_provisioned: No
43_question_type:  
45_compute_request: Other
45_question_type: We have local computing resources that are suitable
47_question_type:  
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Enhancing Migratory Bird Surveys with Thermal Imagery and Deep Learning
3_agency: Department of the Interior
3_abr: DOI
4_bureau: FWS
8_topic_area: Mission-Enabling
8_question_type:  
10_commercial_ai: None of the above.
11_purpose_benefits: The U.S. Fish and Wildlife Service (USFWS) Division of Migratory Bird Management is aiming to improve upon migratory bird surveys by utilizing aerial remote sensing combined with deep learning (a form of artificial intelligence) analyses to automate survey counts. The goal is to provide accurate wildlife counts while simultaneously reducing risk to pilots by allowing aerial surveys to occur at higher altitudes. In partnership with the College of William and Mary, USFWS has previously demonstrated that thermal remote sensing technology, coupled with deep learning, can provide accurate counts of sandhill cranes (Antigone canadensis) at night during their critically important migratory stopover in the Platte River Valley of Nebraska
12_outputs: Migratory bird spatial locations, bird species identification, and bird counts
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/15/2020
19_date_acq_dev_began: 8/15/2020
20_date_implemented: 7/1/2024
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support: No
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Remote sensing digitial imagery and labels for bird species
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
35_demo_features:  
35_question_type:  
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/USFWS/AI-for-USFWS-Migratory-Birds
40_has_ato:  
41_system_name:  
43_infra_provisioned: No
43_question_type:  
45_compute_request: Other
45_question_type: We have local computing resources that are suitable
47_question_type:  
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: SOCS documents analysis
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOEM
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits:  The BOEM Status of the Outer Continental Shelf (SOCS) environmental information portal contains over 900 BOEM documents including Environmental Impact Statements (EISs), Environmental Assessments (EAs), Categorical Exclusion Reviews (CERs), Biological Assessments (BAs), Biological Opinions (BiOPs), National Historic Preservation Act (NHPA) Section 106 documents, policy documents, environmental guidance, reusable content, and environmental studies. Some potential use cases for integrating LLM/AI into SOCS:
 - Generate resource affected sections for different resources based on existing recent EISs and pull in new information to keep the analysis current. For example, if a wind energy lease is being contemplated in the vicinity of an already analyzed wind energy lease, the information would be pulled from the existing environmental analyses as a starting point, incorporating any new information in the system. The drafted information would include citations and a references cited section in a particular format. 
- Have LLM/ summarize key findings from multiple studies on similar resources. For example, multiple studies addressing noise or sound impacts to different species could be summarized with references to the source publications.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 9/9/2024
22_dev_method: Data not reported by submitter and will be updated once additional information is collected
23_contract_piids: Data not reported by submitter and will be updated once additional information is collected
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Data not reported by submitter and will be updated once additional information is collected
34_data_docs: Data not reported by submitter and will be updated once additional information is collected
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: ONRR Video Hosting Platform [OVHP]
3_agency: Department of the Interior
3_abr: DOI
4_bureau: ONRR
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits:  ONRR's Video Hosting Platform is a secure, internal-only video hosting site hosting training videos, all-hands meetings, health & wellness, SEP events and more.

AI-generation of captions/subtitles, metadata, and chapters to make video hosting, editing, and searching more efficient. AI-generated captions support Section 508 requirements.
12_outputs: see description 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Video Creation and Editing
3_agency: Department of the Interior
3_abr: DOI
4_bureau: ONRR
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: To efficiently generate captions, subtitles, text-to-speech, video, photo, and audio.
12_outputs: see description 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Predictive AI Applications for Wildlife Monitoring: SeeOtter, a custom built software solution for a
3_agency: Department of the Interior
3_abr: DOI
4_bureau: FWS
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Marine Mammals Management is tasked with Stock Assessments under the Marine Mammal Protection Act, Section 117 and sea otter population monitoring has shifted to image-based surveys, relying on on a YoloV5, AI-assisted process to procure a data set of sea otter observations from photos. Department of Interior agencies, USFWS, USGS, and NPS have all adopted an image-based methodology for sea otter population monitoring, but the model requires continued maintenance and refinement with new surveys, sensor upgrades, and model updates. Unfortunately, there are no AI expert permanent staff tased with this project across Alaska DOI programs and the need has been fulfilled through contractors. SeeOtter was a custom developed software by Collin Power and Evan Wetherington, under contract to USFWS Sea Otter Program, to turn hundreds of thousands of images from across the Gulf of Alaska into datasets usable for statistical analyses by USGS and Stock Assessment Reporting by USFWS and NPS. Glacier Bay NPS assisted in developing an SOP for the AI-assisted processing for sea otter data and Collin Power, under contract with USGS built the system out through a Center for Data Integration Project to make it more accessible to interested users. We would like SeeOtter to be a tool availabel to Partners, across DOI as well as Tribes and researchers, who are interested in applying a predictive AI model for counting objects from imagery.

USGS also have plans for large scale photo surveys coming up in 2025/26 and we will be working collaboratively to figure out the support for image processing following the SeeOtter SOP that has been developed in a partnership among USFWS, USGS, and NPS.
12_outputs: The Yolo V5 algorithm provides georeferenced bounding boxes around each object detected in an image, in our case, sea otters with an associated confidence score.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
20_date_implemented: 1/3/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: Data not reported by submitter and will be updated once additional information is collected
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Images captured during aerial sea otter surveys across Alaska.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link: public release in development.
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Non-Generative AI use for Trust Information Analysis and Reporting Tool
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BTFA
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The use of Non-Generative AI for identifying  blank pages, document classification, title extraction, date extraction. This endeavor is using Azure Computer Vision and Document Intelligence Read  to help with identifying blank pages. Azure AI Search Services (This is on hold right now and not listed under AI Tool Name) for document classification, title extraction and date extraction.
12_outputs: see description
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Intelligent Optical Character Recognition
3_agency: Department of the Interior
3_abr: DOI
4_bureau: IBC
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits:   IOCR is machine learning software that replaces the need for collecting documents and manually entering them into systems.  The software learns over time based on volume fed through the software and corrects common errors automatically, such as 0 vs O.  Invoice processing is one of the immediate applications considered for IOCR.  IOCR will speed the flow of data to the certifying officer for review and ultimate payment.  This capability also has broader potential to enhance data extraction, validation, and preservation.  IOCR will be deployed in conjunction with Robotics Process Automation to allow for more comprehensive automation solutions. The same robust testing and validation of automations prior to deployment to production will be applied to IOCR.
12_outputs:   IOCR will speed the flow of data to the certifying officer for review and ultimate payment.  This capability also has broader potential to enhance data extraction, validation, and preservation.  IOCR will be deployed in conjunction with Robotics Process Automation to allow for more comprehensive automation solutions. The same robust testing and validation of automations prior to deployment to production will be applied to IOCR
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Seasonal/Temporary Wetland/Floodplain Delineation using Remote Sensing and Deep Learning
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Reclamation was interested in determining if recent advancements in machine learning, specifically convolutional neural network architecture in deep learning, can provide improved seasonal/temporary wetland/floodplain delineation (mapping) when high temporal and spatial resolution remote sensing data is available? If so, then these new mappings could inform the management of protected species and provide critical information to decision-makers during scenario analysis for operations and planning.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2018
22_dev_method: Developed in-house.
24_hisp_support: No
28_iqa_compliance: Actitivity as proof of concept and did not yield influential scientific information, so IQA not applicable.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Data Driven Sub-Seasonal Forecasting of Temperature and Precipitation 
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Reclamation has run 2, year-long prize competitions where particants developed and deployed data driven methods for sub-seasonal (2-6 weeks into future) prediction of temperature and precipitation across the western U.S. Particpants outperformed benchmark forecasts from NOAA. Reclamation is currently working with Scripps Institute of Oceanography to further refine, evaluate, and pilot implement the most promising methods from these two copmetitions. Improving sub-seasonal forecasts has significant potential to enhance water management outcomes.  
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 12/19/2016
22_dev_method: Developed in-house.
24_hisp_support: No
28_iqa_compliance: Actitivity as proof of concept and did not yield influential scientific information, so IQA not applicable.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Data Driven Streamflow Forecasting
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Reclamation, along with partners from the CEATI hydropower industry group (e.g. TVA, DOE-PNNL, and others) ran a year-long  evaluation of existing 10-day streamflow foreasting technologies and a companion prize competition open to the public, also focused on 10-day streamflow forecasts. Forecasts were issued every day for a year and verified against observed flows. Across locations and metrics, the top perfoming foreacst product was a private, AI/ML forecasting company - UpstreamTech. Several competitors from the prize competition also performed strongly; outperforming benchmark forecasts from NOAA. Reclamation is working to further evaluate the UpstreamTech forecast products and also the top performers from the prize competition.  
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/1/2020
19_date_acq_dev_began: 8/1/2023
20_date_implemented: 12/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
28_iqa_compliance: Actitivity as proof of concept and did not yield influential scientific information, so IQA not applicable.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.