AI Use Case LibraryDoD FM · Federal · Audit · Finance · OMB 2025
Home/Browse catalog/OMB Individually Reported/OMB-IND-NASA-661
OMB Individually Reported

Planted Area mapping in food insecure conflict zones

Low riskExact public inventory row

Description

Planted Area mapping in food insecure conflict zones

Detailed example

Predictions

AI / analytics pattern

Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.

Automation level / stage

a) Pre-deployment – The use case is in a development or acquisition status.

Expected benefit

Planted Area mapping in food insecure conflict zones

Controls / human review

ATO: Not reported; PIA: Not published

Source

OMB 2025 Federal Agency AI Use Case Inventory

Related

More from Science

OMB-IND-NASA-1OMB Individually Reported

Anomaly Detection and Precursor Identification in UAV flight data

This project is using past algorithms developed by the NASA ARC (Ames Research Center) Data Sciences Group and modifying them with application to identifying previously-unknown an…

OMB Individually ReportedScienceLow risk
OMB-IND-NASA-8OMB Individually Reported

Deep Learning for Flood Mapping (DELTA)

DELTA simplifies machine learning for satellite imagery.

OMB Individually ReportedScienceLow risk
OMB-IND-NASA-9OMB Individually Reported

Distributed Spacecraft Autonomy

Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems through the developme…

OMB Individually ReportedScienceLow risk
OMB-IND-NASA-10OMB Individually Reported

ExoMiner discovery of ExoPlanets via data from the Kepler and TESS space telescopes

ExoMiner is being used to statistically validate exoplanets detected by the Kepler space telescope and to identify promising exoplanet candidates for the TESS space telescope

OMB Individually ReportedScienceLow risk
OMB-IND-NASA-13OMB Individually Reported

Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model

Our team developed high-performance unsupervised deep machine-learning models for the detection of flight-operations anomalies. The models’ engineered-feature (latent) spaces are…

OMB Individually ReportedScienceLow risk
OMB-IND-NASA-21OMB Individually Reported

Machine Learning Airport Surface Model: Estimated ON Time Prediction

The ML-airport-estimated-ON software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models inten…

OMB Individually ReportedScienceLow risk