Low-Altitude Classification Technologies using Machine Learning

CAL Analytics and The Ohio State University are proposing development of Low-Altitude Airspace Classification Technologies using Machine Learning. We believe classification technology built upon machine learning techniques will significantly reduce the amount of time required to develop, integrate, and test key classification technology that is critical for Unmanned Traffic Management (UTM) and Urban Air Mobility (UAM). We also believe this technology is extensible to reduce the timeliness and cost of key Classification technology development for critical Air Force programs and missions. In addition, we would like to understand how our development can leverage or integrate with the Air Force Cognitive Engine (ACE) Our technical objectives include developing an Air Force commercialization strategy, demonstrating capabilities of Machine Learning Classifier to improve UTM/UAM system performance, demonstrating how Classification technology enables automation with UTM/UAM, demonstrating ability to extend classification technology to other missions without extensive development to find Air Force customers, and to look into integration opportunities using the Air Force Cognitive Engine (ACE).