Remote Sensing is a technology that combined with Machine Learning can provide relevant information for decision making support in a variety of applications. Hyperspectral imaging sensors are the state-of-the-art of Remote Sensing. Based on this technology, TracSense is developing a sensor system solution that gives detailed information regarding the icing status of the aircraft. To be able to achieve optimal results, investigations regarding the assembly of the system, hardware and software solutions are needed. Also, the system should be extensively tested in a large set of environmental variables in order to be improved and its robustness validated. The deicing of aircraft bears significant financial and environmental costs to airlines, deicing companies and airport management. TracSense’s technology can significantly reduce the expenses of de-icing by providing the flight- and ground-crew with highly detailed information on the status of the aircraft - directly at the gate. Better information allows the crew to make more accurate decisions on the need for deicing. The more efficient operation of deicing facilities helps to prevent delays, since shorter deicing time may be used to achieve the same results. It also reduces the environmental and financial costs, since less energy, chemicals, man-hours and equipment-hours will be needed. Also, flight availability may be increased since the take off queues will be shorter and sometimes not even needed. Finally, flight safety is increased by making decisions based on more accurate information.
Project leader: Christopher Dahlin Rodin
Institution: TRACSENSE AS