Master Thesis: Triage of Non-compliant UAS Flights Using Machine Learning Methods

Job description

Background With the steep increase in amateur drone flights in urban centres around the world, authorities are searching for effective ways to maintain an orderly and safe traffic situation. In the Netherlands, due to the popularity of some drone-flying spots and the high density of urban centres, many drone flights take place in restricted areas or close to high-importance buildings. For example, the city centre of Amsterdam often has days with hundreds of unique drone flights per day, most of which are lacking a permit to fly within the Schiphol Airport CTR. However, due to the high number of flights, as well as the difficulty to accurately determine the location of pilots, the local authorities are unable to prevent, pursue, and investigate them all of the ones that breach the rules. Many such flights occur due to the lack of awareness from pilots, and thus pose a low threat. However, these might obscure other flights that can produce harm and are of a high threat level. The goal of this thesis is to develop an online system that, based on live drone detection data, can determine the threat level of a drone flight and inform authorities whether it must be immediately addressed....