GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Oil Spil Response for Ice-Covered Rivers
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: This project uses backscatter data from the SAR (synthetic aperture radar instrument) on the Sentinel-1 satellite to determine ice phenology for large rivers and lakes based on machine learning method (i.e., a Random Forest Classifier). The goal of this DOI Inland Oil Spill Preparedness Program (IOSPP) funded work is to provide rapid, near real-time information to oil spill response crews concerning about the safety of ice-covered areas (FY2023-25).
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Operation and Maintenance
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: Modeling Sediment Abundance in the Eastern Snake River Plain Aquifer Using Supervised 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: This project aims to model sediment abundance in the eastern Snake River Plain aquifer near the Idaho National Laboratory. We are employing supervised learning techniques to predict sediment presence at various depths within boreholes using natural gamma ray readings. The model is trained with independently derived estimates of sediment probability to enhance its accuracy. This initiative will significantly aid scientists in understanding and managing sediment distribution in the aquifer.
12_outputs: see description
16_dev_stage: Initiated
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: Pacific Northwest Stream Flow Permanence
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 are using machine learning approaches, specifically the random forest algorithm, to provide spatially explicit estimates of the presence of year-round surface flow in streams across large (several US states) geographic extents. Empirical random forest models include discrete flow/no flow observations as the response variable and a broad suite of physio climatic covariates. The models are used to inform management decisions that require streamflow classification of perennial versus non-perennial which is the charge of many land steward agencies including the Bureau of Land Management, U.S. Forest Service and State and private forests. Funding for this project is congressional allocated funds (FY2023-25).
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Operation and Maintenance
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: SAMPLE Toolbox
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: A toolbox for land managers to develop plans for monitoring vegetation
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
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: Mapping wildfire fuels in previously burned landscapes
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 goal is to understand how land management treatments affect the probability of reburning.
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
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: Inventorying landforms with convolutional neural networks
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: Geosciences and Environmental Change (GEC) Science Center (FEDMAP-funded): LiDAR derived topographic data images earth's surface in unprecedented detail and is available across most of the country. This data reveals landforms relevant for understanding many scientific phenomena (e.g., patterned ground) and natural hazards (e.g., karst). We are developing simple pipelines to train and deploy convolutional neural networks on high resolution topographic data in order to efficiently identify and inventory these features.
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: Lava lake thermal pattern classification using self organizing maps and relationships to eruption pr
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 apply a machine learning algorithm called self-organizing maps (SOM) to thermal infrared time-lapse images .
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
19_date_acq_dev_began: 1/1/2024
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
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: Advancing image-based surveys to support sea duck conservation along the Pacific Flyway
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: For most of their annual cycle, North American sea ducks are densely distributed in estuaries and along the coastal nearshore where they are susceptible oil spills, energy development, changing ocean conditions, and other potential threats. Observer-based aerial surveys have been an important tool for evaluating coastal distributions and estimating population abundances to understand sea duck responses to their changing environment. However, safety, expense, observer bias and lack of methodological consistency are rising concerns associated with observer-based surveys, making it imperative to transition to more sustainable methods. Digital aerial surveys (DAS) that automate counts from aerial imagery using convolutional neural network (CNN) models are one way to improve survey safety and count accuracy. We are developing a standardized DAS for the lower Pacific Flyway to help maximize safety, while improving data consistency and model accuracy among important regions within the Flyway.
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
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: Probabilistic source classification of large tephra producing eruptions using supervised machine lea
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:   USGS has produced a model that accurately and confidently identifies large tephra-producing eruption volcanic sources in the Alaska-Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. Eruptive products from Alaska's Aleutian Arc-Alaska Peninsula and Wrangell volcanic field were used as a test environment for 11 supervised classification algorithms, trained on nearly 2000 electron probe microanalysis measurements of glass major oxides, representing 10 volcanic sources.
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: InSAR and other geodetic studies at Volcanoes
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 USGS uses InSAR (Inferometric Synthetic Aperture Radar) to map ground deformation and track volcanic activity globally. Artificial Intelligence approaches are being used to recognize transient signals in combined InSAR and GPS data that may be indications of impending hazardous volcanic activity.
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: Climate Futures for Lizards and Snakes in Western North America
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: Identifying new management challenges to reptiles based on shifting environmental conditions
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Operation and Maintenance
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.