Derivation of Geometrically and Semantically Annotated UAV Datasets at Large Scales from 3D City Models

Abstract

While in high demand for the development of deep learning approaches, extensive datasets of annotated unmanned aerial vehicle (UAV) imagery are still scarce today. Manual annotation, however, is time-consuming and, thus, has limited the potential for creating large-scale datasets. We tackle this challenge by presenting a procedure for the automatic creation of simulated UAV image sequences in urban areas and pixel-level annotations from publicly available data sources. We synthesize photo-realistic UAV imagery from GOOGLE EARTH STUDIO and derive annotations from an open CityGML model that not only provides geometric but also semantic information.