Semantic Segmentation Learning for Autonomous UAVs using Simulators and Real Data

Deep learning requires large amounts of data for training models. For the task of semantic segmentation, manual annotation is time-consuming and difficult. With the recent advances in game engines, simulators have become more popular as they can instantly generate ground truth data for multiple sensors. In this paper, we make a thorough survey of the most recent and popular simulators and synthetic datasets, exploring solutions for semantic segmentation on images taken from drones. We also propose an extension of the CARLA simulator by introducing an aerial camera. We evaluate a deep learning model trained on both synthetic and real data and present a new dataset which comprises both.