GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: R1 Forest Vegetation Modeling
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to use satellite images and methods such as LiDAR (light detection and ranging) with machine learning to model forest vegetation and make estimates. The use of machine learning improves models and estimates with decreased time and cost.
12_outputs: The model outputs predictions of forests and vegetation in the form of raster and vector geospatial maps. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2024
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: ESRI Support Chat Bot
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 chatbot is to help handle requests for support with geospatial software between our team and the software vendor. The benefits include saving time when dealing with Environmental Systems Research Institute (ESRI) support issues and reducing the number of specific ESRI support tickets that need to be sent to the Forest Service Geospatial Helpdesk or to ESRI through contract support services.
12_outputs: The chatbot outputs support ticket entries, code snippets for queries, and text and links for support ideas and answers.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/21/2024
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: Wildlife deterrent system
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service; Forest Service Research & Development
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of the AI device is to keep coyotes out of a fenced area by blocking their entry through a gap in the fence, while still allowing other wildlife to pass through. The benefits include making ecological research possible that couldn't be done otherwise, and saving time by reducing the need to watch camera footage and manually control the fence.
12_outputs: The model performs video object detection of a coyotes, and arms an electrical barrier to prevent passage of the coyote.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/26/2021
19_date_acq_dev_began: 8/26/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: GS-35F-0207T
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is composed of a few thousand photographs of coyotes collected from trail cameras. Images were used by the contractor to train the system to detect coyotes.
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: Less than 6 months
43_infra_provisioned: No
45_compute_request: 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.
2_use_case_name: The Lost Meadows Model
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this model is to find out where meadows used to be and how often they appeared in order to understand their original state and their potential for restoration. The discovery of these areas increases the potential for meadow restoration, which can benefit biodiversity, wildfire management, carbon storage, and water storage.
12_outputs: The model outputs predictions of areas with meadow-like environmental conditions. The predicted areas include a mixture of existing but undocumented meadows, non-meadow lands that may have once been meadows, and meadow-like areas that may never have been a meadow. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/10/2022
19_date_acq_dev_began: 10/10/2022
20_date_implemented: 7/1/2023
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: The data is a collection of 11,127 publicly available meadow polygons from the Sierra Nevada MultiSource Meadow Polygons Compilation, Version 2 (UC Davis and USDA Forest Service 2017). 
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: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No questions had
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: 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: Markov random fields
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 improve the accuracy of estimates in machine learning models. The benefits include helping stakeholders make more informed and effective decisions for managing mixed forests.
12_outputs: The model outputs predicted counts of tree species in a location, and the degree of competition between different tree species in the same location.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 10/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is comprised of ForestGEO tree count data from the Republic of Palau.
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: Yes
45_compute_request: Yes
47_timely_resources: No
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: 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: AI for regional forest mapping and monitoring
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this model is to use existing satellite images and forest survey data from the USDA Forest Service to create detailed maps of forest structures. This information will help land managers be more effective and efficient with their planning.
12_outputs: The model outputs GeoTiffs (raster maps of forest attributes, such as tree density and tree species data).
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2000
19_date_acq_dev_began: 1/1/2000
20_date_implemented: 10/1/2002
22_dev_method: Developed with contracting resources.
23_contract_piids: 22-JV-11261959-067 (with Oregon State University; other past JVAs as well)
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Mapping products are currently reviewed by for the USDA Forest Service Forest Inventory and Analysis program and the USDA Forest Service Region 6 Biometrics Program. All products are distributed with detailed accuracy assessments.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Forest Inventory and Analysis data collected by the USDA Forest Service (Research & Development). It contains 200,000 individual plot measurements, and hundreds of Geospatial Information System (GIS) features (climate data, satellite images, topography, etc.) stored as 30-meter raster maps (GeoTiffs).
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: 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: Other
43_question_type: N/A, AI application performed by Oregon State University, not USDA Forest Service
45_compute_request: Other
45_question_type: N/A, AI application performed by Oregon State University, not USDA Forest Service
47_timely_resources: Other
47_question_type: N/A, AI application performed by Oregon State University, not USDA Forest Service
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: IOL Focus Group and Survey Sensemaking
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this model is to efficiently process large quantities of focus group transcripts and survey results. Benefits include decreased labor hours manually processing transcripts and surveys.
12_outputs: The model outputs text summaries of focus group comments and surveys.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/11/2024
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: Esri ArcGIS Pro Deep Learning Modules
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 enhance scientific modeling and analysis, which will standardize Geographic Information System (GIS) workflows for modeling and analytics.
12_outputs: The tools will output image classifications.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2024
19_date_acq_dev_began: 4/1/2024
20_date_implemented: 4/1/2024
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: The data is comprised of Natural Resource image 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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: FS ACE
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: 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: EMC Comment Parsing and Analysis
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: This project aims to extract, categorize, and respond to public comments based on past responses. The benefits include creating a standardized process for handling comments, making public comment data more accessible and ready for AI use, reducing the time and cost of processing comments, minimizing human errors due to high workloads and tight deadlines, improving responsiveness to public concerns, increasing public trust, enhancing accountability through clear reporting, and supporting team training by building a database of common themes and response strategies.
12_outputs: The model will output text analyses of categories pulled from public comments and recommend responses based on historic responses.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/1/2024
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: QUIC-Fire processing and analysis
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: AI is being used to analyze data from fire-atmosphere models to understand fire behavior and effects. The goal is to create tools that will help fire and smoke managers use the QUIC-Fire (Quick Urban & Industrial Complex-Fire) model for planning controlled burns and assessing wildfire behavior.
12_outputs: The AI output will be a collection of metrics that provide building blocks for a tool that fire and smoke managers will use to implement QUIC-Fire (Quick Urban & Industrial Complex - Fire) into their decision-making.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/25/2024
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: Analysis of prescribed fire turbulence data
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 find connections between the heat from a wildfire and the turbulence it creates in the air. Current tools are not very accurate and can make mistakes. This AI effort helps create better tools that can assist fire and smoke managers in making decisions about smoke management.
12_outputs: The model outputs correlation analysis of how temperature change is associated with air turbulence measurements above a prescribed fire.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/12/2023
19_date_acq_dev_began: 7/10/2023
22_dev_method: Developed in-house.
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is comprised of temperature and turbulence kinetic energy readings collected over time from 16 sonic anemometers (measure wind speed and direction) and 112 thermocouples (measures temperature). The datasets are published in the US Forest Service data archive. 
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. 
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been 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: Complaint Summarization
3_agency: Consumer Financial Protection Bureau
3_abr: CFPB
4_bureau: CFPB
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Support staff that analyze complaints by helping to scale internal review of consumers' complaint narratives and companies' response narratives in light of increasing complaint volumes. Cost avoidance. 
12_outputs: Brief narrative summaries describing concepts included in complaint narratives. 
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Ask Complaints
3_agency: Consumer Financial Protection Bureau
3_abr: CFPB
4_bureau: CFPB
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Support staff that analyze complaints by helping to scale production of quantiative data in response to questions posed by users. Cost avoidance.
12_outputs: Summary data points of cohorts of complaints. 
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Complaint Narrative Redaction
3_agency: Consumer Financial Protection Bureau
3_abr: CFPB
4_bureau: CFPB
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Redact personally identifiable information (PII) and other information that could be used to identify individuals from consumers' complaint narratives to support data sharing at scale. Cost containment, privacy protection, more informed consumer consent. 
12_outputs: Complaint narratives redacted of PII and other information that could be used to identify users to inform consumers' decisions about whether or not to publish their narratives. 
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Document Review Tool
3_agency: Consumer Financial Protection Bureau
3_abr: CFPB
4_bureau: CFPB
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Support investigations through document review assistance
12_outputs: Key documents
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Anomaly Detection for Data Quality
3_agency: Commodity Futures Trading Commission
3_abr: CFTC
4_bureau: DOD
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Anomaly detection model designed to identify potentially erroneous data loads in TCR data. Uses an isolation forest model with and automatically runs daily. 
12_outputs: Model provides an anomaly score on the full day of data for a single exchange
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2023
19_date_acq_dev_began: 5/1/2023
20_date_implemented: 7/1/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: REDACTED
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Acquisition of the data catalog was concurrent with development of the use case 
33_agency_data: Transactions by market and type
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: REDACTED
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.
2_use_case_name: Stress Testing Scenarios with Deep Learning
3_agency: Commodity Futures Trading Commission
3_abr: CFTC
4_bureau: DCR
8_topic_area: Mission-Enabling
8_question_type: i
10_commercial_ai: None of the above.
11_purpose_benefits: Pilot project to explore neural-network based machine learning methods for creating stress testing scenarios and estimating PnL (profit and loss) on FO (Futures and Options) portfolios based on the current/recent market states/conditions. 
12_outputs: The AI generates predicted price movements based on provided input probabilities 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/6/2023
19_date_acq_dev_began: 2/14/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: REDACTED
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
2_use_case_name: MPD Entity Risk Modeling
3_agency: Commodity Futures Trading Commission
3_abr: CFTC
4_bureau: MPD
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Entity-level risk modeling project. Use statistical and probabalistic models to predict firms experiencing changes in capital levels. Very early stage R&D effort. 
12_outputs: Now-cast score for each registrant and each day indicating probability or risk category for near-future capital adjustment indicator statisitc
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2025
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No