Kongsberg Geospatial, SFL Scientific Introduce AI-Driven Technology to Identify and Display Chemical Threats Using Drones
Summary
Kongsberg Geospatial and SFL Scientific, a Boston-based data science consulting company announced that they will be demonstrating an Artificial Intelligence-driven system to allow autonomous Unmanned Aerial Vehicles equipped with sensors to detect and recognize a range of chemical, biological, radiological, nuclear, and explosive threats (CBRNE) from the air. Kongsberg Geospatial, along with its partner SFL Scientific, will demonstrate how a medium-sized commercial drone carrying a sensor package can autonomously recognize chemical threats, identifying and ultimately displaying invisible hazardous plumes, in real-time. The system leverages SFL custom bleeding-edge Generative Adversarial Networks (GANs) and Graph Deep Learning models to autonomously identify threats. “The rapid identification of chemical and visual threats is crucial in a variety of civilian and federal missions”, explained Michael Segala, PHD, CEO of SFL Scientific. “Next-generation devices will be integrated with the capability to autonomously identify, locate, and help prioritize decisions in the detection of threats, anomalous activity, and other key indicators to support the safety and effectiveness of individuals working as first responders in potentially hazardous situations.” Dr. Segala will describe how this pioneering work by SFL Scientific leverages next-gen AI technology to help interpret raw sensor data in real-time.
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