Development of a synthetic dataset generation method for deep learning of real urban landscapes using a 3D model of a non-existing realistic city

Abstract

In the urban landscaping field, training datasets for instance segmentation in the detection of building facades are needed for complex analysis and simulation based on data. Manual dataset generation methods are costly, so automatic generation is preferred. However, previous methods are costly and are more reliant on 3D real-city models than LOD2. The objective of this research is to propose a method for automatically generating synthetic datasets for instance segmentation using an image translation technique and procedural modeling. The image translation technique is used to generate street view images for training data, and procedural modeling can automatically specify the composition of generated street view images and generate annotation data, as well as a variety of 3D models. The proposed method generates various kinds of synthetic data at a rate of 4.45 s per set, and the model trained on the generated dataset can detect real buildings for each instance.