GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Data Extraction Using MS Power Automate AI Functionality
3_agency: Department of the Interior
3_abr: DOI
4_bureau: ONRR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Train pre-built Machine Learning model to extract data from different document formats and convert into a consistent structured format to improve business operations.
12_outputs: Improved business documentation. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Document copies received from Bureau of Indian Affairs (BIA)
37_custom_code: No
38_code_access: No – agency does not have access to source code.
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: 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: CESU project to detect of bird nests using deep learning to support annual colonial bird monitoring
3_agency: Department of the Interior
3_abr: DOI
4_bureau: NPS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This work is already being accomplished via a cooperative agreement with a university (Florida International University)

The National Park Service (NPS), has been monitoring colonial nesting birds monthly via low level aerial photography from a helicopter platform in Biscayne National Park since 2010. These photographs are processed to determine the number of active nests for specific focal species: Double-crested Cormorants, Great Egrets, Great White Herons, Great Blue Herons, White Ibises, and Roseate Spoonbills. NPS now has 13 years of data which is composed of 64,350 uniquely identified nests and 57,793 birds representing.

This monitoring is time consuming and needs to be accomplished consistently and accurately. Currently, the aerial photos are processed by individuals. Which takes substantial time and effort. We propose to explore the creation of automatic script that would allow the active nest to be identified using software. The goal of this project is to explore software programs and create automatic script that can identify potential bird nests on the photograph and to highlight this area on the photograph. By developing a robust object detection model, researchers can monitor bird colonies and their populations more efficiently, e.g. by consistently identifying nests with software which will allow the tracking of nesting patterns with less person effort needed to accomplish this task. It will also help to control observer detection variation over time.
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
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: Image Generation and Audio Video Editing
3_agency: Department of the Interior
3_abr: DOI
4_bureau: ONRR
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: To efficiently create and edit images, audio, and video.
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: Use of AI for developing bioacoustic and remote camera imagery wildlife species classifiers for noni
3_agency: Department of the Interior
3_abr: DOI
4_bureau: NPS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: University of Illinois created a model to predict rainfall on a watershed scale in Great Smoky Mountains National Park. They are now working with IBM to create a system that will use this model to provide forecasts of flooding events with a goal of a 24+ hour lead time. The developers are using AI to improve on the Quantitative Precipitation Forecast of the National Weather Service to thus improve on the accuracy of their tool. The NPS is cooperating as an end-user of the flood forecast tool, but not as a developer or direct user of the AI.
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
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: Machine Learning Model Optimization
3_agency: Department of the Interior
3_abr: DOI
4_bureau: ONRR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits:  Alteryx machine learning model optimization and fitting helps users select which model is optimal to fit to an underlying dataset. It also can help refine the model, recommending variable removal if autocorrelation issues are detected in the dataset.
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
20_date_implemented: 4/13/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Internal data, subscription sourced data, and publicly available data.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
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: 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: NPS Acquisition Management Workload Software
3_agency: Department of the Interior
3_abr: DOI
4_bureau: NPS
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Provide assistance for employees working on procurment packages by drafting documents and summarizing content. 
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: Caseguard Studio
3_agency: Department of the Interior
3_abr: DOI
4_bureau: NPS
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits:  Our office uses CaseGuard Studio to streamline and enhance the accuracy of video and audio redactions for law enforcement and public records. This tool addresses the challenge of protecting sensitive information while ensuring compliance with privacy laws and FOIA requirements, improving efficiency and reducing manual workload.
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: Azure based Internal Controls Testing App for Award Descriptions
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OS - Office of the Secretary of the Interior
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: PGM will work with Microsoft to develop an application that will live the the secure Azure environment and allow bureaus to conduct their own testing of project descriptions as a learning tool. The application will be locked to the same prompts for review of project descriptions but will allow bureau financial assistance policy personnel to run their own tests to compare to FY 2023 results and look for improvements.
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: Internal Controls Testing - SAM.gov certifications
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OS - Office of the Secretary of the Interior
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits:   Federal financial assistance regulation and DOI PGM policy requires awarding officers check SAM.gov, a Federal public website, to ensure that the entity funds are to be award to is eligible to receive Federal funds and that the POCs on the awards are not debarred. The use case involves using a large language AI model to assess and score SAM.gov pdf documents against the date the award was made.
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
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
31_data_catalog: Yes
33_agency_data: PDF documents
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
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: Potential Applications of AI Models to UAS Post-fire Mapping Data Analysis and Image Processing
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OS - Office of the Secretary of the Interior
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits:  Evaluating AI Models for UAS Post-Fire Mapping Analysis Focusing on the Following Issues...
Post-Fire Damage Assessment:  Efficiently allocate resources for future rehabilitation efforts.
Vegetation Recovery Prediction:  Assess burn severity to estimate ecosystem regeneration speed.
Soil Erosion Risk Mapping:  Identify vulnerable areas and guide erosion control measures.
Habitat Restoration Planning:  Prioritize areas for reforestation, invasive species removal, and soil stabilization.
Water Quality Monitoring:  Predict sediment runoff, nutrient levels, and contaminants for future water management.
Structural Evolution Analysis and Prediction:  Analyze fire spread and growth to aid risk assessment and response planning.
Environmental Impact Assessment: Aid in planning rehabilitation efforts and assessing ecosystem resilience.
Risk Communication and Public Awareness: Inform communities about rehabilitation efforts and safety precautions.
Ongoing Post-Fire Rehabilitation Treatment Effects:  Assist BAER teams in assessing burned landscapes and monitoring ecological effects.
Cross-Disciplinary Integration:  Bridge communication gaps between experts in various fields for wildfire risk assessment.
Literature Reanalysis:  Assist researchers in developing insights, identifying gaps, and informing future research.
Assessing Damage and Recovery:  Identify fire-damaged objects (e.g., infrastructure, trees) to prioritize rehabilitation.
Monitoring Ecosystem Recovery:  Identify plant species, soil erosion patterns, and ecological indicators for restoration planning.
Identifying Hazards and Safety Risks:  Identify post-fire hazards (e.g., unstable terrain, debris) to ensure safety during rehabilitation.
Identifying Cultural and Archeological Objects: Identify relevant objects (e.g., artifacts, disturbances) for avoidance during rehabilitation.
Customized Rehabilitation Plans:  Detect specific objects (e.g., utility poles, contaminated soil) for tailored rehabilitation plans.
12_outputs: see description 
16_dev_stage: Initiated
17_impact_type: Neither
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data:    UAS-derived 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Proposal for the I-NEPA System: Leveraging Artificial Intelligence (AI) for Enhanced Efficiency in E
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OS - Office of the Secretary of the Interior
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: I-NEPA aims to streamline and enhance environmental reviews, foster improved public engagement, and facilitate advanced searching, tracking, and reporting of both past and present NEPA actions throughout the Department of the Interior. I-NEPA will leverage the Retrieval Augmented Generation (RAG) process, combining NEPA and policy documents and user input into a large language model (LLM) prompt to get tailored output for each project.
12_outputs: see description 
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: Public Comment Analysis Tool (PCAT): Leveraging Artificial Intelligence (AI) for Enhanced Efficiency
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OS - Office of the Secretary of the Interior
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Federal agencies struggle with efficiently processing and analyzing public comments, particularly for NEPA actions involving controversial topics with millions of responses. Manual processing is labor-intensive and introduces delays and inconsistencies. PCAT seeks to address these challenges by automating the analysis process steps only to improve accuracy and efficiency.
12_outputs: Streamline environmental reviews and permitting by utilizing AI and ML for efficient and accurate analysis of public comments.
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: Pilot: Using Machine Learning to Harvest Data for Standardized Species Conservation
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: The U.S. Fish and Wildlife Service is in the process of reimagining and creating a new database to hold, depict, and disseminate information about the species we manage. This effort will greatly expand and standardize the data related to species and their conservation. We have worked with contractors to develop a preliminary tool that explores the possibiliy of using machine learning and large language modelling to harvest species information related to their biological and physical needs into standardized pick-lists. This includes a way to then vet the harvested data. As part of this we are evaluating the infrastructure needed to containerize tool like this in our environment.
12_outputs: we aim to integrate  summmarized data, using some sort of tailored AI and framework or API, into our species workflows in ECOSPhere as it relates to species information.  

fill in worflows for reporting and analytics
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 2/14/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Literature provided by field offices related to species information. Evalutaed with sample data taken from published literature in USFWS library Zotero.
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: 6-12 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: Summarization of documents and output to ECOSphere species workflow
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: The U.S. Fish and Wildlife Service has a substantial number of documents that we would like to take advantage of NLP or ML process in order to summarize their content.  Afterwards, we aim to integrate that summmarized data, using some sort of tailored AI and framework or API, into our species workflows in ECOSPhere as it relates to species information.  
12_outputs:  we aim to integrate  summmarized data, using some sort of tailored AI and framework or API, into our species workflows in ECOSPhere as it relates to species information.  

fill in worflows for reporting and analytics
16_dev_stage: Initiated
17_impact_type: Neither
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
37_custom_code: 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: Prediction of Suitable Habitat for ESA-listed Species
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: The U.S. Fish and Wildlife Service currently uses species distribution models (i.e., machine learning algorithms) to predict potential habitat for ESA-listed threatened and endangered species.  These habitat predictions are vetted by USFWS field biologists and, in some cases, are used as ranges in IPaC to inform users of possible impacts on ESA-listed species.  Species distribution model outputs have also been used to identify suitable habitat on the landscape for potential reintroduction efforts or suitable areas for additional surveys, with the potential to locate new, previously undocumented populations.
12_outputs: fill in worflows for reporting and analytics
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/1/2019
19_date_acq_dev_began: 4/1/2019
20_date_implemented: 4/1/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Species occurrence data.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name:  ECOSphere
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside the agency.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Computer Vision Model to Rapidly Identify Habitat Change
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: The U.S. Fish and Wildlife Service is planning to partner with the Chesapeake Conservancy, an NGO, to develop a computer vision model to monitor for early detection of habitat loss across the landscape, a significant threat to biodiversity, including threatened and endangered species.  By using a computer vision model one can rapidly identify and flag areas where habitat loss may be occurring due to natural or human-caused disturbances.  Early detection can facilitate rapid responses, when appropriate, or allow practitioners to accurately calculate habitat loss over time.  More accurate estimates of habitat loss allow for better management decisions and potentially shorter recovery times for threatened and endangered species.
12_outputs:  By using a computer vision model one can rapidly identify and flag areas where habitat loss may be occurring due to natural or human-caused disturbances.  Early detection can facilitate rapid responses, when appropriate, or allow practitioners to accurately calculate habitat loss over time.  More accurate estimates of habitat loss allow for better management decisions and potentially shorter recovery times for threatened and endangered species.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Species occurrence data.
42_dev_tools_wait: 6-12 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.