University of South Australia has spearheaded a project utilizing advanced onboard artificial intelligence (AI) technology to create a highly efficient early fire smoke detection system for Kanyini, South Australia's pioneering cubesat.
Conventional Earth observation satellites have typically lacked the processing capabilities to analyze intricate Earth images captured from space in real-time. To overcome this limitation, the researchers devised a lightweight AI model capable of detecting smoke within the constrained processing, power consumption, and data storage capacities of CubeSats.
When compared to ground-based processing of hyperspectral satellite imagery for fire detection, the AI-powered airborne model significantly reduced downlink data volume to a mere 16% of its original size, while simultaneously slashing energy consumption by 69%.
The AI-powered airborne model demonstrates a fire smoke detection speed that is 500 times faster than traditional ground-based processing methods.
According to the researchers, airborne AI presents notable advantages over traditional ground-based processing. Not only does it hold immense value in the event of a bushfire, but it also has the potential to function as an early warning system for other natural disasters.
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