NASA · NASA-868 · record R0470

Integrating Explainable Machine Learning with Physics for Enhanced Wildfire Detection in Observation-Constrained Environments

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: Forecasting and prediction Derived by keyword rules; see methodology

Problem the AI is intended to solve

Satellite-based fire detection provides critical data for fire management, fire spread modeling, air quality forecasts, and assessments of fire impacts on ecosystems and communities. Current fire detection algorithms, whether physics-based or machine learning (ML)-based, frequently fail when wildfires are obscured by dense clouds or smoke, creating data gaps that degrade the quality of air quality and fire emissions estimates. This project will develop an explainable multitask ML model for fire detection that is integrated with cloud and aerosol retrieval to enhance fire detection capabilities under clouds.

Expected benefits

The research has the potential to subtantially improve satellite-based fire detection, a key application and societal benefit from NASA's Earth science program, based on more consistent detection of fire activity needed for fire tracking and situational awareness, and improved detection under difficult observing conditions, including on days when extreme fire behavior generates deep injection of smoke (pyrocumulonimbus or PyroCb).

System outputs

Outputs will be joint retrievals of cloud and aerosol vertical profile information and fire detections.

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