NASA · NASA-410 · record R0232

Nighttime Combustion Detection from NASA’s Black Marble

National Aeronautics and Space Administration · GSFC: Goddard Space Flight Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
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

This use case focuses on monitoring nighttime combustion over land from NASA’s Black Marble product and has demonstrated the detection of fires and gas flaring mapping by jointly using both thermal and light emission properties of these event.

Expected benefits

The ML capability first demonstrates the generation of training samples using anomaly detection, tackling the challenge of training data generation in Earth Sciences. This is followed by classification of combustion and background across diverse geographic reasons and seasons and produces detections from a suite of baseline ML approaches including per-band outlier detections, fully-connected neural networks and Siamese Networks trained with triplet and contrastive loss. The detections are then jointly considered to create a high confidence ensemble detection layer.

System outputs

daily detections, ensemble output.

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: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
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: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 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 reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported