GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Incident Invoice Document Understanding
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to analyze incident invoices and return the values that need to be entered into a database. This new approach leads to faster invoice processing and reduces data entry mistakes for more accurate data.
12_outputs: The tool outputs an Excel document containing required values identified from incident invoices.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/21/2021
19_date_acq_dev_began: 1/26/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: 12314419F0612 and another USDA DISC contract that I don't have access to
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The dataset is created with PDF files of OF-286 documents, which are paid invoices of varying quality and structure.
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.
40_has_ato: Yes
41_system_name: UiPath
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Other
43_question_type: We are working closely with DISC to build the necessary infrastructure. It has been a moving target.
45_compute_request: No
47_timely_resources: Yes
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Forest disease detection and screening
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this project is to improve tree disease diagnosis and screening, thereby facilitating ongoing efforts within and outside the Forest Service to manage diseases of forest trees. 
12_outputs: The model will output a prediction indicating whether a tree is diseased or not, and if a tree is resistant or susceptible to a disease.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/3/2020
19_date_acq_dev_began: 8/3/2020
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Data has and is currently being collected using commercially available and custom sensors and images from trees in natural forest landscape, trees in planted settings, and trees that are being screened for disease resistance in controlled environments. Data is collected for both model calibration and validation. 
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.
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
45_compute_request: Other
45_question_type: Use government furnished equipment
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for 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: Use of LLMs for data extraction
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this model is to quickly gather information from scientific papers to track plant diseases. The practical benefit is that using this method would save time compared to manually collecting the information, which can be slow and error-prone when done for a long time.
12_outputs: The model outputs a table of requested data variables (e.g., country, pathogen name, host name, etc.).
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2023
30_saop_review: No
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: IPWG Application Survey Analysis
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to analyze over 5,000 responses from an internal employee survey on IT applications, then give a summary of the employee feedback regarding each IT application. This decreases the time required to go through each response manually, helping the team make informed investment decisions more quickly.
12_outputs: The model outputs a text summarization for each IT application in the survey data, and potentially includes text summaries of responses and sentiment analysis. The project will also produce a dashboard that allows users to see similar attributes at the agency, office, application, and individual response level.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/3/2024
19_date_acq_dev_began: 10/3/2024
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data has been collected from a Microsoft Forms survey sent to Forest Service employees and downloaded into a CSV (comma-separated values) file. The survey responses are not anonymous and contain personnel information such as region, office, and job title. There are approximately 5000 responses about hundreds of applications. The data is comprised of approximately 54 attributes, with a mix of numerical ratings, categories, and free text comments. The data is restricted and encrypted due to personnel information. 
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.
40_has_ato: Yes
41_system_name: Excel, Python, Google Cloud Platform 
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: Fire Resilent Landscapes
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The goal of this tool is to quantify the cost of forest treatments. Benefits include providing the ability to accurately map treatment costs for users to make more informed decisions.
12_outputs: The tool outputs predictions in the form of raster surfaces/maps.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/1/2021
19_date_acq_dev_began: 8/1/2021
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: No agency-owned data used in this project.
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 – source code is publicly available.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
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: PC Rasterize
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to be able to process point cloud data more efficiently. This will reduce costs associated with processing point cloud data.
12_outputs: The tool outputs point clouds and raster surfaces/maps.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 8/1/2024
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: No agency-owned data used in this project.
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 – source code is publicly available.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
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 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: Spread and Balance Sample Design
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to produce samples that are well spread and balanced. This sample design will reduce the quantity of samples needed and further reduce costs associated with collecting field data.
12_outputs: The tool outputs data frames and geospatial-data-frames.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 5/1/2024
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: No agency-owned data used in this project.
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 – source code is publicly available.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
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: Regression, Classification, Clustering with Hilbert Curves
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to perform better regression, classification, and clustering. This will create a new and better ways to produce various estimates, reducing cost and error.  
12_outputs: The tools will output Data frame and Raster Surfaces/maps. 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 6/1/2024
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: No agency-owned data used in this project.
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 – source code is publicly available.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
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: The Big Data, Mapping, and Analytics Platform (BIGMAP) Project
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this project is to use geospatial predictions from Forest Inventory and Analysis samples to make more accurate estimates of different forest characteristics. Greater precision in estimates leads to more informed decisions about the forest resources in the US.
12_outputs: The model outputs predictions in the form of raster maps.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/7/2019
20_date_implemented: 7/5/2021
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Esri FS GeoPlatform
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: The methods used are all documented in previously published peer-reviewed articles. Results are validated using data from the Forest Inventory and Analysis program and a peer-reviewed comparative assessment methodology.
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Data from the Forest Inventory and Analysis program and the Forest Inventory and Analysis Database were used to finetune and validate the models used. While the data are publicly available, the actual coordinates of the plot locations used are not, in order to protect the confidentiality of the land owner.
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.
40_has_ato: Yes
41_system_name: Esri Managed Cloud Services; Amazon Web Services
42_dev_tools_wait: More than 12 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.
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: BirdNET to detect bird vocalizations for research and species monitoring
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: BirdNET quickly scans thousands of hours of forest audio recordings to detect bird calls from species that are important for forest monitoring, like spotted owls, black-backed woodpeckers, and willow flycatchers. This decreases the time and cost associated with manually listening to recordings to identify bird calls.
12_outputs: The model outputs text files of bird calls, which include the bird species and time that the call was recorded.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2021
19_date_acq_dev_began: 6/1/2021
20_date_implemented: 2/13/2023
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: BirdNET was developed using data from the MacAulay Library of Natural Sounds at Cornell University. Forest Service did not provide any contract or data. BirdNET is publicly available.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: Yes – source code is publicly available.
39_code_link: https://www.sciencedirect.com/science/article/pii/S1574954121000273
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: Hurricane impact descriptions
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of the AI model is to convert a table of data about a tropical cyclone's path and estimated impact on forests into a clear and understandable story. This is part of a rapid assessment given to stakeholders after a cyclone hits, so it needs to be done fast. We are creating a tool to automate this process and the AI helps to make better quality reports.
12_outputs: The model outputs a few paragraphs of easy-to-read text that explains the effects of a cyclone. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 7/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: No agency owned data is used to train, fine-tune, or evaluate the performance. 
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
45_compute_request: Other
45_question_type: Not sure
47_timely_resources: Other
47_question_type: No communication necessary thus far
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Predictive flood modeling
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service; USDOT: US Department of Transportation
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to predict water flow during floods and assesses the vulnerability of drains under roads. This will help U.S. Department of Transportation (USDOT) and USDA Forest Service to make informed decisions in drain restoration and protection against flooding.
12_outputs: The model outputs water flow predictions during flood events and the vulnerability level of drains under roads.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
30_saop_review: No
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: FuelCast
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: This project predicts future fuel conditions and gives early warnings to help plan fuel management. The benefits include better preparation of the US firefighting teams for potential increases in large wildfires. This system also reduces the workload for fire behavior analysts because it provides fuel estimates, so they don't have to spend as much time figuring out fire behavior patterns through trial and error.
12_outputs: The model outputs predictions of the future quantity of wood and plants that could be present and contribute to wildfires. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/17/2019
19_date_acq_dev_began: 10/17/2020
20_date_implemented: 10/18/2020
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: I have no idea what PIID is
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
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: More than 12 months
45_compute_request: Other
45_question_type: Not sure
47_timely_resources: No
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
51_extension_request: No – Agency did not request an extension for this use case.