GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Automating the Detection and Classification of Wildlife in Aerial Imagery
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The US Geological Survey (USGS), Bureau of Ocean Energy Management (BOEM), and US Fish and Wildlife Service (FWS) are partnering on a multi-year effort to develop deep learning algorithms and tools for the detection and classification of seabirds and other marine wildlife in aerial imagery. The tools and workflows developed by this project will be used by BOEM to assess wildlife populations as part of planning and monitoring for offshore energy development.
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
20_date_implemented: 10/1/2018
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: High-resolution aerial imagery, expert annotations
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: Science Support System (SSS)
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 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: Machine Learning algorithm for stream velocity prediction
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The algorithm will be used to develop and incorporate a time-of-travel web-based application that will allow users to estimate travel times in a spill response scenario with greater accuracy.
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: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: streamflow 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: Science Support System (SSS)
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 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: Machine Learning for streamflow forecasting
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: In the Pacific Northwest, where the Willamette River is located, floods are becoming more common and severe. To help prevent flooding, the area has 13 dams built to control floods. This project, a collaboration with Portland State University, aims to build a smart system to predict river flows accurately and quickly across the Willamette River Basin by using state-of-the-art statistical and computational models as well as new hydrologic observations. The project aims to improve our understanding of how to use new river flow observational data that USGS provides to make better predictions.
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: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: streamflow 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: Science Support System (SSS)
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 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: Automated otolith aging using image processing
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Fisheries managers and researchers often need to know the age of fish for population estimates, stock assessment, and similar projects. Fish otoliths (an ear bone) often accumulated rings annual (similar to tress). This process traditionally is done manually and can vary across individual agers. We are training an image process ML program to automate this process to see if we can reduce variability across individual agers and automate the aging process of counting otolith rings possibly saving 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: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Otolith images (pictures) with known ages
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: Science Support System (SSS)
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 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: Automating blood smear cell counts using machine learning
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: We exposed largemouth bass to an immunogen (poly I:C) to stimulate an antiviral response and collected blood smears from 38 of these fish. The blood smear slides were scanned at 83x, digitized using an Aperio ScanScope CS, and uploaded for labeling using SageMaker Ground Truth software. We are working with USGS Cloud Hosting Solutions to label training sets of images that include lymphocytes, monocytes, and granulocytes. We are in the process of validating and testing the ability of the model to accurately recognize and distinguish WBC images. To accomplish this, we are comparing manual cell counts among the 3 human readers, manual cell counts to automated cell counts by the model, and automated cell counts of novel tiles taken from the training slides.
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: 10/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: blood smear slides were scanned at 83x, digitized using an Aperio ScanScope CS, and uploaded for labeling using SageMaker Ground Truth software
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: Science Support System (SSS)
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 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: Machine learning for tsunami source zones
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: State of the art tsunami hazard analysis for coastal communities and infrastructure is computationally demanding. Ml will be used to select the most representative source zones (among thousands of offshore earthquake ruptures)
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: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Offshore fault slip rate data and historical seismicity
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: Science Support System (SSS)
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 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: Shoreline Modeling
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Exploring the prospect of using AI/ML models to predict shoreline change and compare accuracy to traditional models. Examples of AI/ML models include LSTM, CNN, and Transformers
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: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Science Support System (SSS)
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 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: Determining the resource potential of critical minerals in seafloor massive sulfide deposits
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: ML to input data on seafloor massive sulfide geochemstry to predict composition.
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: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Geochemical 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: Science Support System (SSS)
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 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: Oceanographic, coastal, and geomorphic change analysis: data generation, QC/QA, and data management
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Machine learning to quantify coastal/marine change across broad scales. QC/QA processes in place to assess data robustness. Verified data will be used by USGS projects for forecasting trends (ie, shorelines, role of permafrost) in a variety of coastal/marine settings for US coasts.
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: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Deep Learning based image segmentation, time-series methods, classification & regression methods. Custom scripts/tools using python/pytorch/tensorflow/keras and other ML libraries and APIs
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: Science Support System (SSS)
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 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: Synthesizing mapping and monitoring data to inform prairie dog management in National Parks
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Synthesizing Mapping and Monitoring Data to Understand Fluctuations in Prairie Dog Colony Size and Densities in Theodore Roosevelt National Park. There have been efforts to capture variation in the size and extent of prairie dog colonies at Theodore Roosevelt National Park in some form since the 1940s. Prairie dog colonies have been mapped semi-annually since the 1990s, but shifting priorities and a largely static budget have made it difficult for park staff to continue mapping. Furthermore, little research has been conducted with the existing mapping data to assess prairie dog habitat quality and the factors that affect colony size fluctuations. This project is aimed at developing more cost-efficient methods for prairie dog colony mapping (that is, remote sensing techniques) and developing indices and models that can help managers derive population inferences based on colony area. Park staff need modeling and remote analysis tools they can use to assess variability in prairie dog populations within the park. These tools should include a method for remotely assessing prairie dog colony size and a predictive tool that can help park managers understand the relationship between colony size and prairie dog population size.
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: 10/1/2020
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Imagery on prairie dog colony and population size
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: Science Support System (SSS)
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 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: Computationally efficient emulation of spheroidal elastic deformation sources using machine learning
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Elastic continuum mechanical models are widely used to compute deformations due to pressure changes in buried cavities, such as magma reservoirs. In general, analytical models are fast but can be inaccurate as they do not correctly satisfy boundary conditions for many geometries, while numerical models are slow and may require specialized expertise and software. To overcome these limitations, we trained supervised machine learning emulators (model surrogates) based on parallel partial Gaussian processes which predict the output of a finite element numerical model with high fidelity.
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: 1/2/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: model output
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: Science Support System (SSS)
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 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.