Fig. 3 (IMAGE) Osaka University Caption Qualitative results for different types and sizes of buildings when Mask R-CNN is trained using HSRBFIA (Hybrid Collection of Synthetic and Real-world Building Facade Images and Annotations) datasets with different ratios of synthetic to real data: (a) low-rise houses in Osaka; (b) low-rise houses in Los Angeles; (c) high-rise houses in New York City; (d) complex facades in Shanghai. (The red dashed rectangles highlight parts of the street-view images that were prone to failure during facade instance segmentation.) Credit 2022 Jiaxin Zhang et al., Automatic generation of synthetic datasets from a city digital twin for use in the instance segmentation of building facades, Journal of Computational Design and Engineering Usage Restrictions Credit must be given to the creator. License CC BY Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.