GatorX Advances to Semi-Final Round of XPRIZE Track B Autonomous Wildfire Challenge (The Journalist Report)

GatorX Advances to Semi-Final Round of XPRIZE Track B Autonomous Wildfire Challenge

GatorX is a well-rounded partnership between Fire Neural Network, the University of Florida, NVIDIA, SATLANTIS, SwissDrones, Archer Aviation, SWIR Vision Systems, and N5 Sensors.

Fire Neural Network (FNN) announced that their team, GatorX, is advancing to the semi-final round of the XPRIZE Track B Autonomous Wildfire Challenge. The announcement was made at the Safety and Security Summit, during the  Esri User Conference (Esri UC) in San Deigo, California on July 15-19, 2024.

GatorX is a well-rounded partnership between Fire Neural Network, the University of Florida, NVIDIA, SATLANTIS, SwissDrones, Archer Aviation, SWIR Vision Systems, and N5 Sensors. Fire Neural Network and SATLANTIS LLC are resident clients of UF Innovate | Accelerate.

The fully autonomous GatorX solution removes first responders from harm’s way and displays the use of advanced AI for good, in accessible and responsible real-world applications. Using SATLANTIS satellite imaging, Fire Neural Network High-Risk-Lightning sensors, N5 smoke sensors, FireBird drones, and UF’s NVIDIA HiPerGator AI supercomputer, GatorX can quickly detect the location of hotspots, verify the hotspots, and autonomously eliminate fires.

The XPRIZE Wildfire is a 4-year, $11 million competition incentivizing the innovation of firefighting technologies to end destructive wildfires so humanity and beneficial wildfire can safely co-exist. The prize aims to transform current wildfire management approaches by developing new technologies that can rapidly and accurately detect, characterize, and respond to wildfires before they become destructive.

The $5 million Autonomous Wildfire Response Track (Track B) will transform how fires are managed and fought by rapidly and autonomously detecting and completely suppressing a destructive, high-risk fire in an environmentally challenging area with greater speed, accuracy, and precision than current best-in-class solutions.

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