The airborne Compact Fire Imager (CFI) for measurements across the entire fire lifecycle
Description
CFI is a new pushbroom instrument with six spectral bands between the shortwave infrared (SWIR) and thermal infrared (TIR), including two channels in the mid-wave infrared (MWIR) specifically designed to detect and characterize flaming and smoldering fires. CFI builds on the design and performance of the dual-band Compact Thermal Imager (CTI) that collected more than 15 million images from the International Space Station (ISS) in 2019, including thousands of fires. CFI leverages the stability and proven performance of innovative Strained-Layer Superlattice (SLS) detector technology on CTI with four specific improvements for fire science and applications: 1) a larger format SLS detector array that improves cross-track resolution and swath width, 2) a custom butcher block filter that provides six specific bands for fire science and applications, 3) a custom optical design that leverages the latest infrared glass technology, and 4) an enhanced processor card that supports instrument operation and onboard fire detection using machine learning (ML) algorithms.
Detailed example
Onboard AI/ML system outputs fire detection information and estimated fire radiative power (FRP), a measure of fire intensity.
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
Onboard fire detection can accelerate the delivery of life-saving information to first responders and fire managers. The demonstration of lightweight and effective AI/ML tools for onboard processing leverages recent advances in hardware and software needed to advance edge computing for disaster applications such as wildfire detection and characterization.
Controls / human review
ATO: Not reported; PIA: Not published