DOI · DOI-0095 · record R2660

Global food-and-water security-support analysis data (GFSAD) project [2024 INV#WO0000000107073]

Department of the Interior · USGS

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2017-11-30 (day precision)Source: 11/30/2017
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

1. Landsat-derived rainfed and irrigated area-product of Conterminous United States (LRIP30) 2. Landsat-derived global cropland extent product @ 30 m (LGCEP30) 3. Landsat-derived global rainfed and irrigated area product @ 30 m (LGRIP30)

Expected benefits

1. assessing Nation's irrigated and rainfed cropland areas 2. critical to assessing crop water use, crop water productivity, and crop water savings 3. providing information on global irrigated and rainfed cropland area maps and statistics

System outputs

Producing irrigated and rainfed cropland maps and statistics of United States of America (USA) and the world using Landsat and other similar satellites at 30 m or better spatial resolution. For example: https://www.usgs.gov/apps/croplands/app/map. Numerous datasets and scientific papers are published on this work. Please visit: www.usgs.gov/wgsc/gfsad30

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: All data used in our AI models (ML\DL) are publicly available from sources such as U. S. Geological Survey (USGS), NASA, European Space Agency (ESA). These data are Landsat and Sentinel Satellites of USGS\NASA and ESA. We also use USDA Cropland Data Layer (CDL) for training and testing models. All these data are publicly available online.
Federal Data Catalog: doi.org ↗

Governance

5 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewReported in placeSource: a) Yes – by another appropriate agency office or reviewer not directly involved in the AI’s development

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeNot applicableSource: b) Not applicable

Appeal processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported