Machine learning in remote sensing-based wildfire and natural resource risk assessments
Description
Predict the risks of wildfire, drought, and invasive species spread on assets of value to the American public
Detailed example
Maps of risk, prediction of which factors contribute to risk, highlight areas for potential mitigation actions.
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
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Predictions inform land management planning and decision-making to mitigate risk and save American taxpayers millions of dollars in damage from the additive effect of these stressors.
Controls / human review
ATO: No; PIA: Not published
Data needed
Publicly available satellite imagery, wildfire perimeters and burn severity, topographic information, drought indices