Filippo Bolletta
A deep learning approach to estimate population density of urban areas to compute risk maps for UASs.
Rel. Alessandro Rizzo, Stefano Primatesta. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021
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Abstract
Unmanned aerial vehicles (UAVs) have gained interest in recent years in many different fields. UAVs are used for remote sensing, surveillance, precision agriculture and goods delivery, to name a few. The growing interest in their use leads to safety concerns which can be faced using a systematic approach to define safe operations. The approach proposed in this thesis consists in the definition of risk maps that quantify the risk connected with a flight operation over a certain area. Risk maps can be used for risk-informed decision making: by National Aviation Authorities to evaluate the risk of a specific flight mission, or to compute safe routes using a risk-aware path planning algorithm.
Specifically, the risk is measured as the probability of having a fatality due to a drone crash
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